mixOmics 6.28.0
multidrug
study
If you are following our online course, the following vignette will be useful as a complementary learning tool. This vignette also covers the essential use cases of various methods in this package for the general mixOmcis
user. The below methods will be covered:
As outlined in 1.3, this is not an exhaustive list of all the methods found within mixOmics
. More information can be found at our website and you can ask questions via our discourse forum.
Figure 1: Different types of analyses with mixOmics (Rohart, Gautier, Singh, and Le Cao 2017).The biological questions, the number of data sets to integrate, and the type of response variable, whether qualitative (classification), quantitative (regression), one (PLS1) or several (PLS) responses, all drive the choice of analytical method
All methods featured in this diagram include variable selection except rCCA. In N-integration, rCCA and PLS enable the integration of two quantitative data sets, whilst the block PLS methods (that derive from the methods from Tenenhaus and Tenenhaus (2011)) can integrate more than two data sets. In P-integration, our method MINT is based on multi-group PLS (Eslami et al. 2014).The following activities cover some of these methods.
mixOmics
is an R toolkit dedicated to the exploration and integration of biological data sets with a specific focus on variable selection. The package currently includes more than twenty multivariate methodologies, mostly developed by the mixOmics
team (see some of our references in 1.2.3). Originally, all methods were designed for omics data, however, their application is not limited to biological data only. Other applications where integration is required can be considered, but mostly for the case where the predictor variables are continuous (see also 1.1).
In mixOmics
, a strong focus is given to graphical representation to better translate and understand the relationships between the different data types and visualize the correlation structure at both sample and variable levels.
Note the data pre-processing requirements before analysing data with mixOmics
:
Types of data. Different types of biological data can be explored and integrated with mixOmics
. Our methods can handle molecular features measured on a continuous scale (e.g. microarray, mass spectrometry-based proteomics and metabolomics) or sequenced-based count data (RNA-seq, 16S, shotgun metagenomics) that become `continuous’ data after pre-processing and normalisation.
Normalisation. The package does not handle normalisation as it is platform-specific and we cover a too wide variety of data! Prior to the analysis, we assume the data sets have been normalised using appropriate normalisation methods and pre-processed when applicable.
Prefiltering. While mixOmics
methods can handle large data sets (several tens of thousands of predictors), we recommend pre-filtering the data to less than 10K predictor variables per data set, for example by using Median Absolute Deviation (Teng et al. 2016) for RNA-seq data, by removing consistently low counts in microbiome data sets (Lê Cao et al. 2016) or by removing near-zero variance predictors. Such step aims to lessen the computational time during the parameter tuning process.
Data format. Our methods use matrix decomposition techniques. Therefore, the numeric data matrix or data frames have \(n\) observations or samples in rows and \(p\) predictors or variables (e.g. genes, proteins, OTUs) in columns.
Covariates. In the current version of mixOmics
, covariates that may confound the analysis are not included in the methods. We recommend correcting for those covariates beforehand using appropriate univariate or multivariate methods for batch effect removal. Contact us for more details as we are currently working on this aspect.
We list here the main methodological or theoretical concepts you need to know to be able to efficiently apply mixOmics
:
Individuals, observations or samples: the experimental units on which information are collected, e.g. patients, cell lines, cells, faecal samples etc.
Variables, predictors: read-out measured on each sample, e.g. gene (expression), protein or OTU (abundance), weight etc.
Variance: measures the spread of one variable. In our methods, we estimate the variance of components rather that variable read-outs. A high variance indicates that the data points are very spread out from the mean, and from one another (scattered).
Covariance: measures the strength of the relationship between two variables, i.e. whether they co-vary. A high covariance value indicates a strong relationship, e.g. weight and height in individuals frequently vary roughly in the same way; roughly, the heaviest are the tallest. A covariance value has no lower or upper bound.
Correlation: a standardized version of the covariance that is bounded by -1 and 1.
Linear combination: variables are combined by multiplying each of them by a coefficient and adding the results. A linear combination of height and weight could be \(2 * weight - 1.5 * height\) with the coefficients \(2\) and \(-1.5\) assigned with weight and height respectively.
Component: an artificial variable built from a linear combination of the observed variables in a given data set. Variable coefficients are optimally defined based on some statistical criterion. For example in Principal Component Analysis, the coefficients of a (principal) component are defined so as to maximise the variance of the component.
Loadings: variable coefficients used to define a component.
Sample plot: representation of the samples projected in a small space spanned (defined) by the components. Samples coordinates are determined by their components values or scores.
Correlation circle plot: representation of the variables in a space spanned by the components. Each variable coordinate is defined as the correlation between the original variable value and each component. A correlation circle plot enables to visualise the correlation between variables - negative or positive correlation, defined by the cosine angle between the centre of the circle and each variable point) and the contribution of each variable to each component - defined by the absolute value of the coordinate on each component. For this interpretation, data need to be centred and scaled (by default in most of our methods except PCA). For more details on this insightful graphic, see Figure 1 in (González et al. 2012).
Unsupervised analysis: the method does not take into account any known sample groups and the analysis is exploratory. Examples of unsupervised methods covered in this vignette are Principal Component Analysis (PCA, Chapter 3), Projection to Latent Structures (PLS, Chapter 4), and also Canonical Correlation Analysis (CCA, not covered here but see the website page).
Supervised analysis: the method includes a vector indicating the class membership of each sample. The aim is to discriminate sample groups and perform sample class prediction. Examples of supervised methods covered in this vignette are PLS Discriminant Analysis (PLS-DA, Chapter 5), DIABLO (Chapter 6) and also MINT (Chapter 7).
If the above descriptions were not comprehensive enough and you have some more questions, feel free to explore the glossary on our website.
Here is an overview of the most widely used methods in mixOmics
that will be further detailed in this vignette, with the exception of rCCA. We depict them along with the type of data set they can handle.
FIGURE 1: An overview of what quantity and type of dataset each method within mixOmics requires. Thin columns represent a single variable, while the larger blocks represent datasets of multiple samples and variables.
Figure 2: List of methods in mixOmics, sparse indicates methods that perform variable selection
Figure 3: Main functions and parameters of each method
The methods implemented in mixOmics
are described in detail in the following publications. A more extensive list can be found at this link.
Overview and recent integrative methods: Rohart F., Gautier, B, Singh, A, Le Cao, K. A. mixOmics: an R package for ’omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752.
Graphical outputs for integrative methods: (González et al. 2012) Gonzalez I., Le Cao K.-A., Davis, M.D. and Dejean S. (2012) Insightful graphical outputs to explore relationships between two omics data sets. BioData Mining 5:19.
DIABLO: Singh A, Gautier B, Shannon C, Vacher M, Rohart F, Tebbutt S, K-A. Le Cao. DIABLO - multi-omics data integration for biomarker discovery.
sparse PLS: Le Cao K.-A., Martin P.G.P, Robert-Granie C. and Besse, P. (2009) Sparse Canonical Methods for Biological Data Integration: application to a cross-platform study. BMC Bioinformatics, 10:34.
sparse PLS-DA: Le Cao K.-A., Boitard S. and Besse P. (2011) Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics, 22:253.
Multilevel approach for repeated measurements: Liquet B, Le Cao K-A, Hocini H, Thiebaut R (2012). A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinformatics, 13:325
sPLS-DA for microbiome data: Le Cao K-A\(^*\), Costello ME \(^*\), Lakis VA , Bartolo F, Chua XY, Brazeilles R and Rondeau P. (2016) MixMC: Multivariate insights into Microbial Communities.PLoS ONE 11(8): e0160169
Each methods chapter has the following outline:
Other methods not covered in this document are described on our website and the following references:
regularised Canonical Correlation Analysis, see the Methods and Case study tabs, and (González et al. 2008) that describes CCA for large data sets.
Microbiome (16S, shotgun metagenomics) data analysis, see also (Lê Cao et al. 2016) and kernel integration for microbiome data. The latter is in collaboration with Drs J Mariette and Nathalie Villa-Vialaneix (INRA Toulouse, France), an example is provided for the Tara ocean metagenomics and environmental data.
First, download the latest version from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("mixOmics")
Alternatively, you can install the latest GitHub version of the package:
BiocManager::install("mixOmicsTeam/mixOmics")
The mixOmics
package should directly import the following packages:
igraph, rgl, ellipse, corpcor, RColorBrewer, plyr, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra
.
For Apple mac users: if you are unable to install the imported package rgl
, you will need to install the XQuartz software first.
library(mixOmics)
Check that there is no error when loading the package, especially for the rgl
library (see above).
The examples we give in this vignette use data that are already part of the package. To upload your own data, check first that your working directory is set, then read your data from a .txt
or .csv
format, either by using File > Import Dataset in RStudio or via one of these command lines:
# from csv file
data <- read.csv("your_data.csv", row.names = 1, header = TRUE)
# from txt file
data <- read.table("your_data.txt", header = TRUE)
For more details about the arguments used to modify those functions, type ?read.csv
or ?read.table
in the R console.
mixOmics
Each analysis should follow this workflow:
Then use your critical thinking and additional functions and visual tools to make sense of your data! (some of which are listed in 1.2.2) and will be described in the next Chapters.
For instance, for Principal Components Analysis, we first load the data:
data(nutrimouse)
X <- nutrimouse$gene
Then use the following steps:
MyResult.pca <- pca(X) # 1 Run the method
plotIndiv(MyResult.pca) # 2 Plot the samples
plotVar(MyResult.pca) # 3 Plot the variables
This is only a first quick-start, there will be many avenues you can take to deepen your exploratory and integrative analyses. The package proposes several methods to perform variable, or feature selection to identify the relevant information from rather large omics data sets. The sparse methods are listed in the Table in 1.2.2.
Following our example here, sparse PCA can be applied to select the top 5 variables contributing to each of the two components in PCA. The user specifies the number of variables to selected on each component, for example, here 5 variables are selected on each of the first two components (keepX=c(5,5)
):
MyResult.spca <- spca(X, keepX=c(5,5)) # 1 Run the method
plotIndiv(MyResult.spca) # 2 Plot the samples
plotVar(MyResult.spca) # 3 Plot the variables
You can see know that we have considerably reduced the number of genes in the plotVar
correlation circle plot.
Do not stop here! We are not done yet. You can enhance your analyses with the following:
Have a look at our manual and each of the functions and their examples, e.g. ?pca
, ?plotIndiv
, ?sPCA
, …
Run the examples from the help file using the example
function: example(pca)
, example(plotIndiv)
, …
Have a look at our website that features many tutorials and case studies,
Keep reading this vignette, this is just the beginning!
multidrug
studyTo illustrate PCA and is variants, we will analyse the multidrug
case study available in the package. This pharmacogenomic study investigates the patterns of drug activity in cancer cell lines (Szakács et al. 2004). These cell lines come from the NCI-60 Human Tumor Cell Lines established by the Developmental Therapeutics Program of the National Cancer Institute (NCI) to screen for the toxicity of chemical compound repositories in diverse cancer cell lines. NCI-60 includes cell lines derived from cancers of colorectal (7 cell lines), renal (8), ovarian (6), breast (8), prostate (2), lung (9) and central nervous system origin (6), as well as leukemia (6) and melanoma (8).
Two separate data sets (representing two types of measurements) on the same NCI-60 cancer cell lines are available in multidrug
(see also ?multidrug
):
$ABC.trans
: Contains the expression of 48 human ABC transporters measured by quantitative real-time PCR (RT-PCR) for each cell line.
$compound
: Contains the activity of 1,429 drugs expressed as GI50, which is the drug concentration that induces 50% inhibition of cellular growth for the tested cell line.
Additional information will also be used in the outputs:
$comp.name
: The names of the 1,429 compounds.
$cell.line
: Information on the cell line names ($Sample
) and the cell line types ($Class
).
In this activity, we illustrate PCA performed on the human ABC transporters ABC.trans
, and sparse PCA on the compound data compound
.
The input data matrix \(\boldsymbol{X}\) is of size \(N\) samples in rows and \(P\) variables (e.g. genes) in columns. We start with the ABC.trans
data.
library(mixOmics)
data(multidrug)
X <- multidrug$ABC.trans
dim(X) # Check dimensions of data
## [1] 60 48
Contrary to the minimal code example, here we choose to also scale the variables for the reasons detailed earlier. The function tune.pca()
calculates the cumulative proportion of explained variance for a large number of principal components (here we set ncomp = 10
). A screeplot of the proportion of explained variance relative to the total amount of variance in the data for each principal component is output (Fig. 4):
tune.pca.multi <- tune.pca(X, ncomp = 10, scale = TRUE)
plot(tune.pca.multi)
Figure 4: Screeplot from the PCA performed on the ABC.trans
data: Amount of explained variance for each principal component on the ABC transporter data.
# tune.pca.multidrug$cum.var # Outputs cumulative proportion of variance
From the numerical output (not shown here), we observe that the first two principal components explain 22.87% of the total variance, and the first three principal components explain 29.88% of the total variance. The rule of thumb for choosing the number of components is not so much to set a hard threshold based on the cumulative proportion of explained variance (as this is data-dependent), but to observe when a drop, or elbow, appears on the screeplot. The elbow indicates that the remaining variance is spread over many principal components and is not relevant in obtaining a low dimensional ‘snapshot’ of the data. This is an empirical way of choosing the number of principal components to retain in the analysis. In this specific example we could choose between 2 to 3 components for the final PCA, however these criteria are highly subjective and the reader must keep in mind that visualisation becomes difficult above three dimensions.
Based on the preliminary analysis above, we run a PCA with three components. Here we show additional input, such as whether to center or scale the variables.
final.pca.multi <- pca(X, ncomp = 3, center = TRUE, scale = TRUE)
# final.pca.multi # Lists possible outputs
The output is similar to the tuning step above. Here the total variance in the data is:
final.pca.multi$var.tot
## [1] 47.98305
By summing the variance explained from all possible components, we would achieve the same amount of explained variance. The proportion of explained variance per component is:
final.pca.multi$prop_expl_var$X
## PC1 PC2 PC3
## 0.12677541 0.10194929 0.07011818
The cumulative proportion of variance explained can also be extracted (as displayed in Figure 4):
final.pca.multi$cum.var
## PC1 PC2 PC3
## 0.1267754 0.2287247 0.2988429
To calculate components, we use the variable coefficient weights indicated in the loading vectors. Therefore, the absolute value of the coefficients in the loading vectors inform us about the importance of each variable in contributing to the definition of each component. We can extract this information through the selectVar()
function which ranks the most important variables in decreasing order according to their absolute loading weight value for each principal component.
# Top variables on the first component only:
head(selectVar(final.pca.multi, comp = 1)$value)
## value.var
## ABCE1 0.3242162
## ABCD3 0.2647565
## ABCF3 0.2613074
## ABCA8 -0.2609394
## ABCB7 0.2493680
## ABCF1 0.2424253
Note:
We project the samples into the space spanned by the principal components to visualise how the samples cluster and assess for biological or technical variation in the data. We colour the samples according to the cell line information available in multidrug$cell.line$Class
by specifying the argument group
(Fig. 5).
plotIndiv(final.pca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
Figure 5: Sample plot from the PCA performed on the ABC.trans
data. Samples are projected into the space spanned by the first two principal components, and coloured according to cell line type. Numbers indicate the rownames of the data.
Because we have run PCA on three components, we can examine the third component, either by plotting the samples onto the principal components 1 and 3 (PC1 and PC3) in the code above (comp = c(1, 3)
) or by using the 3D interactive plot (code shown below). The addition of the third principal component only seems to highlight a potential outlier (sample 8, not shown). Potentially, this sample could be removed from the analysis, or, noted when doing further downstream analysis. The removal of outliers should be exercised with great caution and backed up with several other types of analyses (e.g. clustering) or graphical outputs (e.g. boxplots, heatmaps, etc).
# Interactive 3D plot will load the rgl library.
plotIndiv(final.pca.multi, style = '3d',
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 3')
These plots suggest that the largest source of variation explained by the first two components can be attributed to the melanoma cell line, while the third component highlights a single outlier sample. Hence, the interpretation of the following outputs should primarily focus on the first two components.
Note:
Correlation circle plots indicate the contribution of each variable to each component using the plotVar()
function, as well as the correlation between variables (indicated by a ‘cluster’ of variables). Note that to interpret the latter, the variables need to be centered and scaled in PCA:
plotVar(final.pca.multi, comp = c(1, 2),
var.names = TRUE,
cex = 3, # To change the font size
# cutoff = 0.5, # For further cutoff
title = 'Multidrug transporter, PCA comp 1 - 2')
ABC.trans
data. The plot shows groups of transporters that are highly correlated, and also contribute to PC1 - near the big circle on the right hand side of the plot (transporters grouped with those in orange), or PC1 and PC2 - top left and top bottom corner of the plot, transporters grouped with those in pink and yellow." width="75%" />
Figure 6: Correlation Circle plot from the PCA performed on the ABC.trans
data. The plot shows groups of transporters that are highly correlated, and also contribute to PC1 - near the big circle on the right hand side of the plot (transporters grouped with those in orange), or PC1 and PC2 - top left and top bottom corner of the plot, transporters grouped with those in pink and yellow.
The plot in Figure 6 highlights a group of ABC transporters that contribute to PC1: ABCE1, and to some extent the group clustered with ABCB8 that contributes positively to PC1, while ABCA8 contributes negatively. We also observe a group of transporters that contribute to both PC1 and PC2: the group clustered with ABCC2 contributes positively to PC2 and negatively to PC1, and a cluster of ABCC12 and ABCD2 that contributes negatively to both PC1 and PC2. We observe that several transporters are inside the small circle. However, examining the third component (argument comp = c(1, 3)
) does not appear to reveal further transporters that contribute to this third component. The additional argument cutoff = 0.5
could further simplify this plot.
A biplot allows us to display both samples and variables simultaneously to further understand their relationships. Samples are displayed as dots while variables are displayed at the tips of the arrows. Similar to correlation circle plots, data must be centered and scaled to interpret the correlation between variables (as a cosine angle between variable arrows).
biplot(final.pca.multi, group = multidrug$cell.line$Class,
legend.title = 'Cell line')
ABS.trans
data. The plot highlights which transporter expression levels may be related to specific cell lines, such as melanoma." width="75%" />
Figure 7: Biplot from the PCA performed on the ABS.trans
data. The plot highlights which transporter expression levels may be related to specific cell lines, such as melanoma.
The biplot in Figure 7 shows that the melanoma cell lines seem to be characterised by a subset of transporters such as the cluster around ABCC2 as highlighted previously in Figure 6. Further examination of the data, such as boxplots (as shown in Fig. 8), can further elucidate the transporter expression levels for these specific samples.
ABCC2.scale <- scale(X[, 'ABCC2'], center = TRUE, scale = TRUE)
boxplot(ABCC2.scale ~
multidrug$cell.line$Class, col = color.mixo(1:9),
xlab = 'Cell lines', ylab = 'Expression levels, scaled',
par(cex.axis = 0.5), # Font size
main = 'ABCC2 transporter')
Figure 8: Boxplots of the transporter ABCC2 identified from the PCA correlation circle plot (Fig. 6) and the biplot (Fig. 7) show the level of ABCC2 expression related to cell line types. The expression level of ABCC2 was centered and scaled in the PCA, but similar patterns are also observed in the original data.
In the ABC.trans
data, there is only one missing value. Missing values can be handled by sPCA via the NIPALS algorithm . However, if the number of missing values is large, we recommend imputing them with NIPALS, as we describe in our website in www.mixOmics.org.
First, we must decide on the number of components to evaluate. The previous tuning step indicated that ncomp = 3
was sufficient to explain most of the variation in the data, which is the value we choose in this example. We then set up a grid of keepX
values to test, which can be thin or coarse depending on the total number of variables. We set up the grid to be thin at the start, and coarse as the number of variables increases. The ABC.trans
data includes a sufficient number of samples to perform repeated 5-fold cross-validation to define the number of folds and repeats (leave-one-out CV is also possible if the number of samples \(N\) is small by specifying folds =
\(N\)). The computation may take a while if you are not using parallelisation (see additional parameters in tune.spca()
), here we use a small number of repeats for illustrative purposes. We then plot the output of the tuning function.
grid.keepX <- c(seq(5, 30, 5))
# grid.keepX # To see the grid
set.seed(30) # For reproducibility with this handbook, remove otherwise
tune.spca.result <- tune.spca(X, ncomp = 3,
folds = 5,
test.keepX = grid.keepX, nrepeat = 10)
# Consider adding up to 50 repeats for more stable results
tune.spca.result$choice.keepX
## comp1 comp2 comp3
## 15 20 15
plot(tune.spca.result)
ABC.trans
data. For a grid of number of variables to select indicated on the x-axis, the average correlation between predicted and actual components based on cross-validation is calculated and shown on the y-axis for each component. The optimal number of variables to select per component is assessed via one-sided \(t-\)tests and is indicated with a diamond." width="75%" />
Figure 9: Tuning the number of variables to select with sPCA on the ABC.trans
data. For a grid of number of variables to select indicated on the x-axis, the average correlation between predicted and actual components based on cross-validation is calculated and shown on the y-axis for each component. The optimal number of variables to select per component is assessed via one-sided \(t-\)tests and is indicated with a diamond.
The tuning function outputs the averaged correlation between predicted and actual components per keepX
value for each component. It indicates the optimal number of variables to select for which the averaged correlation is maximised on each component. Figure 9 shows that this is achieved when selecting 15 transporters on the first component, and 20 on the second. Given the drop in values in the averaged correlations for the third component, we decide to only retain two components.
Note:
Based on the tuning above, we perform the final sPCA where the number of variables to select on each component is specified with the argument keepX
. Arbitrary values can also be input if you would like to skip the tuning step for more exploratory analyses:
# By default center = TRUE, scale = TRUE
keepX.select <- tune.spca.result$choice.keepX[1:2]
final.spca.multi <- spca(X, ncomp = 2, keepX = keepX.select)
# Proportion of explained variance:
final.spca.multi$prop_expl_var$X
## PC1 PC2
## 0.1171694 0.1015308
Overall when considering two components, we lose approximately 1 % of explained variance compared to a full PCA, but the aim of this analysis is to identify key transporters driving the variation in the data, as we show below.
We first examine the sPCA sample plot:
plotIndiv(final.spca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, sPCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
Figure 10: Sample plot from the sPCA performed on the ABC.trans
data. Samples are projected onto the space spanned by the first two sparse principal components that are calculated based on a subset of selected variables. Samples are coloured by cell line type and numbers indicate the sample IDs.
In Figure 10, component 2 in sPCA shows clearer separation of the melanoma samples compared to the full PCA. Component 1 is similar to the full PCA. Overall, this sample plot shows that little information is lost compared to a full PCA.
A biplot can also be plotted that only shows the selected transporters (Figure 11):
biplot(final.spca.multi, group = multidrug$cell.line$Class,
legend =FALSE)
Figure 11: Biplot from the sPCA performed on the ABS.trans
data after variable selection. The plot highlights in more detail which transporter expression levels may be related to specific cell lines, such as melanoma, compared to a classical PCA.
The correlation circle plot highlights variables that contribute to component 1 and component 2 (Fig. 12):
plotVar(final.spca.multi, comp = c(1, 2), var.names = TRUE,
cex = 3, # To change the font size
title = 'Multidrug transporter, sPCA comp 1 - 2')
ABC.trans
data. Only the transporters selected by the sPCA are shown on this plot. Transporters coloured in green are discussed in the text." width="75%" />
Figure 12: Correlation Circle plot from the sPCA performed on the ABC.trans
data. Only the transporters selected by the sPCA are shown on this plot. Transporters coloured in green are discussed in the text.
The transporters selected by sPCA are amongst the most important ones in PCA. Those coloured in green in Figure 6 (ABCA9, ABCB5, ABCC2 and ABCD1) show an example of variables that contribute positively to component 2, but with a larger weight than in PCA. Thus, they appear as a clearer cluster in the top part of the correlation circle plot compared to PCA. As shown in the biplot in Figure 11, they contribute in explaining the variation in the melanoma samples.
We can extract the variable names and their positive or negative contribution to a given component (here 2), using the selectVar()
function:
# On the first component, just a head
head(selectVar(final.spca.multi, comp = 2)$value)
## value.var
## ABCA9 0.4264913
## ABCB5 0.4032833
## ABCC2 0.3906354
## ABCD1 0.3767078
## ABCA3 -0.2701947
## ABCA5 0.2414476
The loading weights can also be visualised with plotLoading()
, where variables are ranked from the least important (top) to the most important (bottom) in Figure 13). Here on component 2:
plotLoadings(final.spca.multi, comp = 2)
Figure 13: sPCA loading plot of the ABS.trans
data for component 2. Only the transporters selected by sPCA on component 2 are shown, and are ranked from least important (top) to most important. Bar length indicates the loading weight in PC2.
The data come from a liver toxicity study in which 64 male rats were exposed to non-toxic (50 or 150 mg/kg), moderately toxic (1500 mg/kg) or severely toxic (2000 mg/kg) doses of acetaminophen (paracetamol) (Bushel, Wolfinger, and Gibson 2007). Necropsy was performed at 6, 18, 24 and 48 hours after exposure and the mRNA was extracted from the liver. Ten clinical measurements of markers for liver injury are available for each subject. The microarray data contain expression levels of 3,116 genes. The data were normalised and preprocessed by Bushel, Wolfinger, and Gibson (2007).
liver toxicity
contains the following:
$gene
: A data frame with 64 rows (rats) and 3116 columns (gene expression levels),$clinic
: A data frame with 64 rows (same rats) and 10 columns (10 clinical variables),$treatment
: A data frame with 64 rows and 4 columns, describe the different treatments, such as doses of acetaminophen and times of necropsy.We can analyse these two data sets (genes and clinical measurements) using sPLS1, then sPLS2 with a regression mode to explain or predict the clinical variables with respect to the gene expression levels.
library(mixOmics)
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
As we have discussed previously for integrative analysis, we need to ensure that the samples in the two data sets are in the same order, or matching, as we are performing data integration:
head(data.frame(rownames(X), rownames(Y)))
## rownames.X. rownames.Y.
## 1 ID202 ID202
## 2 ID203 ID203
## 3 ID204 ID204
## 4 ID206 ID206
## 5 ID208 ID208
## 6 ID209 ID209
We first start with a simple case scenario where we wish to explain one \(\boldsymbol Y\) variable with a combination of selected \(\boldsymbol X\) variables (transcripts). We choose the following clinical measurement which we denote as the \(\boldsymbol y\) single response variable:
y <- liver.toxicity$clinic[, "ALB.g.dL."]
Defining the ‘best’ number of dimensions to explain the data requires we first launch a PLS1 model with a large number of components. Some of the outputs from the PLS1 object are then retrieved in the perf()
function to calculate the \(Q^2\) criterion using repeated 10-fold cross-validation.
tune.pls1.liver <- pls(X = X, Y = y, ncomp = 4, mode = 'regression')
set.seed(33) # For reproducibility with this handbook, remove otherwise
Q2.pls1.liver <- perf(tune.pls1.liver, validation = 'Mfold',
folds = 10, nrepeat = 5)
plot(Q2.pls1.liver, criterion = 'Q2')
Figure 14: \(Q^2\) criterion to choose the number of components in PLS1. For each dimension added to the PLS model, the \(Q^2\) value is shown. The horizontal line of 0.0975 indicates the threshold below which adding a dimension may not be beneficial to improve accuracy in PLS.
The plot in Figure 14 shows that the \(Q^2\) value varies with the number of dimensions added to PLS1, with a decrease to negative values from 2 dimensions. Based on this plot we would choose only one dimension, but we will still add a second dimension for the graphical outputs.
Note:
We now set a grid of values - thin at the start, but also restricted to a small number of genes for a parsimonious model, which we will test for each of the two components in the tune.spls()
function, using the MAE criterion.
# Set up a grid of values:
list.keepX <- c(5:10, seq(15, 50, 5))
# list.keepX # Inspect the keepX grid
set.seed(33) # For reproducibility with this handbook, remove otherwise
tune.spls1.MAE <- tune.spls(X, y, ncomp= 2,
test.keepX = list.keepX,
validation = 'Mfold',
folds = 10,
nrepeat = 5,
progressBar = FALSE,
measure = 'MAE')
plot(tune.spls1.MAE)
keepX
is indicated with a diamond." 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VBQYHkcZWVlxh133GHYbLY697fb7ca9995rVFRU1Nr/wQcfNCQZ7du3N1566aVa+44aNeqIXtfp06fX2xaSjC5duhg//vhjwH7nnHNOvfu8/PLLDa7D+++/X297SDI6duxozJ49u0nadPLkyeZ2W7ZsqbV+3rx5Rnp6er3169Wrl7F48eJ6j7G+c8bfzllZWeY+0dHRdW771ltvBb2O9913X73733XXXUbHjh0NScbIkSMb/PrW/B1g9S8lJSWox1JWVmbcddddht1ur7cMScbpp59ueDwec98nnnii3tenoeeM3xtvvGFu98UXXwSsO5L38tGe4yUlJcaIESMO2x7XXXed5f4AAABofggRAADAMXdoiGAYhvH888+by3r06GGUlJTU2u9YhgitW7c2JBlDhgwxnnnmGWPZsmXGN998Y4waNcp8njFjxhgxMTFGYmKi8fTTTxsLFy40fvzxR+PGG280t0lKSjJyc3MDnqNmiCDJsNlsxuTJk43ly5cbOTk5xtdff20MGjTIXH/xxRdbHsf1119vbtOnTx/jk08+MTIzM409e/YYn376qXHCCSeY6//4xz/W2t/f8RgXF2d2/g8ZMsSYMGGCkZiYaLzyyisNfk2XLFliOJ1OQ5LhcDiMBx54wFi4cKGRk5Nj/PTTT8ZNN91k1iUyMtLYsGGDue/q1auNOXPmGA899JC5zcMPP2zMmTPHmDNnjrFnz54G1WHbtm2Gy+UyJBndu3c3PvvsM2Pv3r1Gfn6+8emnnxrXXXddQHs1RZvW1yG8ZcsWs34Oh8O45557jKVLlxqFhYXG4sWLjQceeMDsrE5MTDT2799fq/x58+aZnb92u9247777jMWLFxuFhYXGqlWrjKuuusp8/hEjRpid2nPnzjXmzJlj7jt69Gizfffu3RvUOr788ssB5//06dONbdu2GVu3bjWmT59utGrVKuD8P5IQobCw0Kx3586dDUlGenq6ueyXX34J6rE8/PDDZj1PO+0045tvvjEyMzONgoICY82aNcaTTz5pJCcnm9u8//775r4ZGRnGnDlzAs67t956y5gzZ46xcOHCBp0zNTUkRGjIe/loz/GJEyea6++44w5j3bp1RlFRkbFmzRpj2rRpRs+ePc31M2bMaPBrCwAAgPBFiAAAAI45qxDB5/MZo0ePNpdPmjSp1n7HMkSQZPTu3bvWXQB5eXlGXFycuU1MTIyxdevWWuXUvIvhhx9+CFh3aIjw73//u9b+xcXFximnnGKGDMuWLQtYv2TJErMD9LTTTjPKyspqlVFSUmL83//9nyHJcLlcterp73j0/3vnnXfMdZWVlQ2+iri8vNzo16+fIcmIiIio8yr/t99+23yuM888s9b6GTNmmOs//PDDBj13Ta+//rq5/8yZMy23GTt2rLnN/Pnzg96m9XUIX3jhhebr+eWXX1rW75tvvjHPwRtvvDFgnc/nM9tZkvHVV1/V2t/n8xlDhgwxt/n2228D1vtDhLqCqaOtY35+vhkSJCYmGps2baq1/9atW43U1NRGhQg19e7d25BkDBgwoEmOxe12G+3btzckGf3797cMNg3DML7//nvzWM4555xa6//617+a61etWlVrfTBDhMO9l4NxjvvbpK47lX755RezHmPHjm3UawsAAIDwwpwIAAAgLNhsNr355puKiYmRJL3wwgv66aefQlqnDz74QAkJCQHLkpKSdPrpp5s/33XXXerWrVutfa+88krzcc35Ew51+umn6+qrr661PDY2VlOnTpUkGYahv/3tbwHrJ0+eLJ/Pp8jISP3zn/9UVFRUrTJiYmL0yiuvyOFwqKKiQg899FCd9TjnnHP0m9/8xvzZ6XQ2eMLat99+W+vWrZMkXX/99frVr35lud0111yjsWPHSpJ+/PFHff3110fz8tSyZcsW83HPnj0tt3n66af1wAMP6M0331S7du2atE1rmjNnjj777DNJ0nXXXafzzjvPcruxY8fqmmuukSS98cYb2rhxo7lu0aJFZjuPHz9e48aNq7W/zWbTtGnTJFW9hkcyf0Mw6jhv3jxzzonbbrvN8nXo1q2bJk+e3OB6NUYwjmXlypVq3769EhMTNWnSJPN306HOOussxcbGSqqaEyTU6nsvH+05XlxcrL1790qSevToYfn8J510kp5++mk9/fTTuvXWW0PdHAAAAAgCQgQAABA2unfvHtBxft111zVo0tOm0LFjRw0aNKjOdX6nnnqq5TZt2rQxH2dnZ9f5PHfffXed60499VSdcMIJkqSFCxeay71er3755RdJ0tChQy1DjJpt2r9//1plHMpqQtqGWr16tfn4cJNa33fffebjRYsWNfo5rfgnlZWkCy64QD/88EOtyWT79++vv/3tb7r22mvVtWvXJm3TmubOnWs+vvTSS+vddvz48ZIkn8+nJUuWmMu//PJL83F9nbNnnnmmtmzZotLSUj322GMNbr9g1HHlypXm45tuuqnO/W+++Wa5XK4G1+1IBeNYhg8frqVLlyo/P1833HBDvWW0bdtWkuR2u5vsmBqqrvdyMM7xuLg4DR06VJI0Y8YM3XfffZbByd133627775bF1xwQaibAwAAAEHgDHUFAAAAarrtttv00Ucfac6cOdq6davuv/9+vfDCC8e8Hl26dKlzXc0r9NPT0y23aWgHad++fetd36NHD61evVqZmZkqKSlRbGysdu7cGdBZ+eyzz9Zbhs1mkyTt2LFDHo9HTmftj4B1XbnfEOvXr5ckRUVFBXTMW+nXr5/5uOZV38EwZswYde/eXVu3btWGDRt09tlnKzk5WWeddZbOOeccnXvuuWrfvr3lvk3RpjVt2rTJfDx//nzzjgIr/iu9JWnz5s2WZdR1Fbhf9+7dj7j9glFHf4gQGRmptLS0OvePi4tTWlpavXfpHI1gHEtN/tfb7XZr+/bt2rhxozZt2qQVK1Zo1qxZ2rNnj6SqICLU6novB+scv+GGG7R06VL5fD49+eSTmjZtmgYPHmy+x0466aTDvh8AAADQvPDpDgAAhBX/sEYDBw5USUmJXnrpJU2YMKHOIXKayqHDGNWlriFOGsJut6tz5871btOpUyfz8ZYtWzRo0KCAjs4FCxZowYIFDXo+j8ej7du3W3YyNqbT2c8fInTp0sXseKxLamqqYmJiVFpaGvQQITY2VvPnz9f48ePNNsnLy9N///tf/fe//5UkjRw5Utdee60mTpwou736ptymaNOaapb/17/+tcHHVHO/ffv2Sap6j9TXQd9YwaijP0To2LHjYc+FpgwRgnEsfnv37tWLL76ojz/+WJs3b651d0u4qeu9HKxz3H8XycSJE+XxeGQYhpYvX67ly5fr8ccfV3JysiZMmKB77rlHffr0CXVzAAAAIAgIEQAAQNjp1q2bpk6dqttvv90c1mjVqlVBK98wjMNuc6yupHU4HA1e7w8sioqKzGXdu3ev8+p6K5WVlZbLo6OjG30M5eXlkqSIiIgj2q8hr8ORSklJ0bx58/Ttt9/qvffe03fffRcwnNSiRYu0aNEiffHFF3r33XeVlJTUZG1ak7/8iIgIjRo1qsFlW9XDbrcf9rxpjGDU0T8fQkPeP4mJiUE/hmAeiyR9/vnnuuyyy1RRURGwvHXr1ho4cKBGjhyp888/X7/5zW+0a9euJjseqeHvl7rey8E8x6+99lqdddZZeuedd/TZZ59p8eLF5l0YeXl5mj59ut5//33961//0sUXX9yk7QIAAICmR4gAAADC0q233qqPPvpIs2fP1rZt23TfffeZE6AejsfjqXd9zc60UPL5fMrNzVVKSkqd2+zcuVNSVZjgHyqo5lA2EyZM0BNPPBHS4+jZs6dycnK0Y8eOw26bnZ1tznPhH0c+2BwOh8aNG6dx48bJMAwtW7ZM33zzjb766ivNnz9fkjRz5kw98MADevnll49Jm/bo0UNr166V1+vVN998Y07EeyS6d++uOXPmyOv1KisrK+h3IwSjju3atVN2drZ27dolwzDqvRth9+7dQa1/sI9l2bJluuKKK8wA4frrr9fFF1+sQYMGBcyLIlXPhXC0wVh9v7uO9vdWsM/xTp066Y9//KP++Mc/Kjc3V99//72++eYbff7558rNzVVxcbEuueQS7d27t8ne6wAAADg2mFgZAACEJZvNpjfeeMPs/HvllVc0a9asOreveeXzoVcN11RWVhY2IYKkw3a8+4d76dy5s3mlf82hc5YvX37Y52jqyan99SkuLjaH3KnLli1bzMeHdsQGg8/nU35+vvmzzWbTsGHD9MADD+iXX37Rxx9/bK779ttvax2D1DRt2qtXL7N+NScftuLxeCzP4ZqdwIe76v22227TlVdeqUceeSQkdayoqDDnCaiLPyBrCsE4ln/9618qKyuTJD333HOaPn26xo0bV+u89Xg85h0YjRnqqKG/uw733jqcYJ7jhYWFAYFHq1atdPnll+utt97S7t27zbs/DMPQd999d1T1BgAAQOgRIgAAgLDVrVs3Pfnkk5KqOqP+8pe/1LltZGSkOcRLZmZmndvNnj07LCY/9fvoo4/qXLdgwQKzA3T48OHm8tjYWHXr1k2SNHfuXG3btq3OMsrLy9WjRw/FxsZqxIgRDRp650idcMIJ5uNXXnml3m39V/5L0rhx44JWB8MwdOqppyo2NlYDBgwwh1g61EUXXaSRI0dKqurE9m/X1G1as43++c9/HraNoqOj1aFDB7355pvm8v79+5uP//3vf9e5f1lZmd544w29//77Wrp0acA6/zwQVlfMB6OOF154ofn473//e537f/vtt8rJyTlsuzVWMI5l7ty5kqqCqN/97nd17j979mzzTgSrOwlqzr1h1e4151Wp73dXfSFqQwTjHH///ffVsWNHJSYm6rXXXrPcNzo6WlOmTDF/3rBhw1HVGwAAAKFHiAAAAMLaLbfcojFjxkiq/ypdm81mXn28aNEiff/997W22b17tyZOnBjqQwrw6quv1urolaqGR7n//vslVV2pfOjksP5Apby8XLfeemudw6A8+uijysrKUmlpqYYPH37E8xY0xM0332yOr/7ss8/W2Tk5f/58zZgxQ5KUlJSkc889N2h1sNls6ty5s8rLy5WZmanXX3/dcrvy8nJlZWVJkkaPHq2oqKhj0qZXXHGFOcns9OnTzQ7qQ2VmZmrq1KkyDEPZ2dkaO3asue7888/X4MGDJUlvvfVWwF0dNT3++ONmOHLRRRcFrPN3WBcUFDRJHS+88EIlJydLqrp63+qOCbfbrT//+c9H8vIesWAci/8uKP86K5s3bw4YZs0fJli1eV3t3rt3b/PxY489Zhk0/OUvf9HChQuPul2O9hwfOnSoeYfJY489Zt6pcaiavwNqtikAAACaJ0IEAAAQ1vzDGsXFxR1222uvvdZ8fMMNN+hf//qXsrOzlZGRoTfeeENnn322du/erYSEhFAflqmwsFBjx47Vp59+quLiYknS2rVrNWbMGM2ZM0eSNHHiRPXt2zdgv2uuuUannHKKJOmbb77RqaeeqiVLlsjr9crj8WjWrFm6+eab9be//U2SlJycrHvvvbdJjiE+Pt68Y6SoqEjDhg3TjBkzzGGjcnNz9dJLL2nMmDHmXSCvv/56QOdqMNx4443mGPyPPPKI3n333YC7BDZt2qQJEyYoIyNDUu0O9qZs04iICL3wwguSqjqlzz77bD311FPm1fiZmZl67bXXNGbMGO3du1eS9Pvf/z5g6By73a5nn31WUlWgNmrUKH300UfmsDNFRUWaMmWKHnvsMUnSqFGjdOWVVwbUo3Xr1pKkn376SY8++qg+/PBD80rxYNQxKSlJTz31lKSqc3vEiBGaOXOm3G63DMPQmjVrNHr0aC1evDior31TtLf/XJCqfrf89NNP5nBFBw4c0Mcff6wzzzzT3F+qnli6pjZt2piP//jHP+q9997Tf//7X3PZ+PHjzddl3rx5uummmzR//nyVl5dr9uzZuuOOO/TXv/41KL+3jvYc79Wrl04//XRJUlZWln77298GBAaVlZV68803dd9990mqmvfk5JNPbtLXGgAAAMeAAQAAcIxNmDDBkGRIMjIzMxu0zyuvvGLuI8m48847a22Tk5Nj9OnTJ2A7m80W8POUKVOMG2+80ZBkDBs2rFYZDofDkGScf/75ddbl9ttvN8vbtWuX5TYrVqwwt/nb3/4WsG7KlCmGJMNutxuPPPKIuV1ERISRnJwcUN+LL77YKCgosHyO7du3GyeffHLA9lFRUUZiYmLAspiYGOOXX36ptf+DDz5obrNmzZqjfl0ffPBBs/38x9euXbuAujidTuOpp56y3H/GjBnmdh9++GGj6vD4448HPJ/D4TDS0tKMhISEgOV/+MMfDK/XG/Q2nTx5srnNli1baq1/7rnnjKioqICy2rZtW+s8veSSSyzrZxiG8fTTTxuRkZEBx9ihQ4eAMjp06GBkZGTU2vfmm28OeB5Jxh//+Meg1/G+++6r1YY1z+2zzz7bOP300w1JxsiRIxv1Wvfu3duQZAwYMKDObY7mWAoLC43u3bsHbNeqVSujX79+5v52u9247bbbjNtuu83cZt26dQHlbNu2zYiJiQkox263G6WlpeY2L730kmG32+v83dWtWzdj7ty55s9ffPFFwHMcyXv5aM/xvXv3Gp07dw7YNjEx0UhLSwt4/6enpxurVq1q1GsLAACA8MKdCAAAoFm4+eabdcYZZ9S7TatWrbRkyRJdd9115lXuxsGhQXr06KFXX3213nkVQuHBBx/Uq6++qrZt26qyslJ5eXmSpNTUVD333HP66KOP6rwCuWvXrpo7d66eeuopczih8vJyc8gUp9Op6667TqtXr9ZJJ53U5MfyyCOPaP78+Ro5cqScTqd8Pp95lbbD4dD48eM1d+5c3XPPPU1Wh/vvv1+ffPKJhg0bJqlqotvdu3ersLBQTqdTAwcO1HvvvacnnngiYKz6Y9WmkyZN0ooVK3TmmWfK5XJJkvbv32+ep7169dLbb7+t999/37J+knT33Xdr8eLFOumkk+R0OuX1erVnzx4ZhqGIiAjdeeedWrdundLS0mrt+9xzz+nyyy9XfHy8uWz9+vVBr+PUqVP1/vvvq1+/fmYb5uXlKTo6WldffbW++OILc7igpnQ0xxIfH6/Zs2fr6quvNu9wyc3N1bp16+RwOHTyySdr/vz5evHFF3XJJZeY+7377rsB5aSnp+ujjz5Sz549zXJ8Pp82bdpkbnPrrbfq22+/DZj82DAMxcbGaty4cZo3b545n8HROtpzPDU1VQsWLNBdd90VMDzW7t275fV61bZtW11++eVavHhxwNwUAAAAaL5shmEx6CYAAEAz5/V6tW7dOu3atUvDhw9XampqqKt0WJs2bdLGjRs1cOBAdenS5Yj3z83N1Zo1a5SXl6f09HR169atQcNANYWKigqtX79e27ZtU7t27dSrV6+AYV2Ohb1792rXrl3KzMxUp06ddMIJJ5gdyQ3VlG3q9Xq1efNmbdy4UUlJSerWrZvS0tLMjuaGtvO6deu0c+dOdevWTb169QqY56EuPp9PW7ZsUVRUlDp06CCn09kkdTQMQxs2bNCWLVuUmpqqIUOGNMm8HE3d3jk5OdqyZYv27Nmjbt26qW/fvoqMjDziOuTn5yszM1NpaWlKTEy03ObAgQNaunSpWrVqpSFDhtT52gTL0Zzj5eXl2rlzp3bt2iW3263BgwcHDAkFAACAluH/A+G8ThP5WfdXAAAAPXRFWHRpY2M6Y29weXJpZ2h0AENvcHlyaWdodCAyMDA3IEFwcGxlIEluYy4sIGFsbCByaWdodHMgcmVzZXJ2ZWQunmbcKQAAACN0RVh0aWNjOmRlc2NyaXB0aW9uAEdlbmVyaWMgUkdCIFByb2ZpbGUapziOAAAAAElFTkSuQmCC" alt="Mean Absolute Error criterion to choose the number of variables to select in PLS1, using repeated CV times for a grid of variables to select. The MAE increases with the addition of a second dimension comp 1 to 2, suggesting that only one dimension is sufficient. The optimal keepX
is indicated with a diamond." width="75%" />
Figure 15: Mean Absolute Error criterion to choose the number of variables to select in PLS1, using repeated CV times for a grid of variables to select. The MAE increases with the addition of a second dimension comp 1 to 2, suggesting that only one dimension is sufficient. The optimal keepX
is indicated with a diamond.
Figure 15 confirms that one dimension is sufficient to reach minimal MAE. Based on the tune.spls()
function we extract the final parameters:
choice.ncomp <- tune.spls1.MAE$choice.ncomp$ncomp
# Optimal number of variables to select in X based on the MAE criterion
# We stop at choice.ncomp
choice.keepX <- tune.spls1.MAE$choice.keepX[1:choice.ncomp]
choice.ncomp
## [1] 1
choice.keepX
## comp1
## 20
Note:
measure = 'MSE
, the optimal keepX
was rather unstable, and is often smaller than when using the MAE criterion. As we have highlighted before, there is some back and forth in the analyses to choose the criterion and parameters that best fit our biological question and interpretation.Here is our final model with the tuned parameters:
spls1.liver <- spls(X, y, ncomp = choice.ncomp, keepX = choice.keepX,
mode = "regression")
The list of genes selected on component 1 can be extracted with the command line (not output here):
selectVar(spls1.liver, comp = 1)$X$name
We can compare the amount of explained variance for the \(\boldsymbol X\) data set based on the sPLS1 (on 1 component) versus PLS1 (that was run on 4 components during the tuning step):
spls1.liver$prop_expl_var$X
## comp1
## 0.08150917
tune.pls1.liver$prop_expl_var$X
## comp1 comp2 comp3 comp4
## 0.11079101 0.14010577 0.21714518 0.06433377
The amount of explained variance in \(\boldsymbol X\) is lower in sPLS1 than PLS1 for the first component. However, we will see in this case study that the Mean Squared Error Prediction is also lower (better) in sPLS1 compared to PLS1.
For further graphical outputs, we need to add a second dimension in the model, which can include the same number of keepX
variables as in the first dimension. However, the interpretation should primarily focus on the first dimension. In Figure 16 we colour the samples according to the time of treatment and add symbols to represent the treatment dose. Recall however that such information was not included in the sPLS1 analysis.
spls1.liver.c2 <- spls(X, y, ncomp = 2, keepX = c(rep(choice.keepX, 2)),
mode = "regression")
plotIndiv(spls1.liver.c2,
group = liver.toxicity$treatment$Time.Group,
pch = as.factor(liver.toxicity$treatment$Dose.Group),
legend = TRUE, legend.title = 'Time', legend.title.pch = 'Dose')
liver.toxicity
data with two dimensions. Components associated to each data set (or block) are shown. Focusing only on the projection of the sample on the first component shows that the genes selected in \(\boldsymbol X\) tend to explain the 48h length of treatment vs the earlier time points. This is somewhat in agreement with the levels of the \(\boldsymbol y\) variable. However, more insight can be obtained by plotting the first components only, as shown in Figure 17." width="75%" />
Figure 16: Sample plot from the PLS1 performed on the liver.toxicity
data with two dimensions. Components associated to each data set (or block) are shown. Focusing only on the projection of the sample on the first component shows that the genes selected in \(\boldsymbol X\) tend to explain the 48h length of treatment vs the earlier time points. This is somewhat in agreement with the levels of the \(\boldsymbol y\) variable. However, more insight can be obtained by plotting the first components only, as shown in Figure 17.
The alternative is to plot the component associated to the \(\boldsymbol X\) data set (here corresponding to a linear combination of the selected genes) vs. the component associated to the \(\boldsymbol y\) variable (corresponding to the scaled \(\boldsymbol y\) variable in PLS1 with one dimension), or calculate the correlation between both components:
# Define factors for colours matching plotIndiv above
time.liver <- factor(liver.toxicity$treatment$Time.Group,
levels = c('18', '24', '48', '6'))
dose.liver <- factor(liver.toxicity$treatment$Dose.Group,
levels = c('50', '150', '1500', '2000'))
# Set up colours and symbols
col.liver <- color.mixo(time.liver)
pch.liver <- as.numeric(dose.liver)
plot(spls1.liver$variates$X, spls1.liver$variates$Y,
xlab = 'X component', ylab = 'y component / scaled y',
col = col.liver, pch = pch.liver)
legend('topleft', col = color.mixo(1:4), legend = levels(time.liver),
lty = 1, title = 'Time')
legend('bottomright', legend = levels(dose.liver), pch = 1:4,
title = 'Dose')
liver.toxicity
data on one dimension. A reduced representation of the 20 genes selected and combined in the \(\boldsymbol X\) component on the \(x-\)axis with respect to the \(\boldsymbol y\) component value (equivalent to the scaled values of \(\boldsymbol y\)) on the \(y-\)axis. We observe a separation between the high doses 1500 and 2000 mg/kg (symbols \(+\) and \(\times\)) at 48h and 18h while low and medium doses cluster in the middle of the plot. High doses for 6h and 18h have high scores for both components." width="75%" />
Figure 17: Sample plot from the sPLS1 performed on the liver.toxicity
data on one dimension. A reduced representation of the 20 genes selected and combined in the \(\boldsymbol X\) component on the \(x-\)axis with respect to the \(\boldsymbol y\) component value (equivalent to the scaled values of \(\boldsymbol y\)) on the \(y-\)axis. We observe a separation between the high doses 1500 and 2000 mg/kg (symbols \(+\) and \(\times\)) at 48h and 18h while low and medium doses cluster in the middle of the plot. High doses for 6h and 18h have high scores for both components.
cor(spls1.liver$variates$X, spls1.liver$variates$Y)
## comp1
## comp1 0.7515489
Figure 17 is a reduced representation of a multivariate regression with PLS1. It shows that PLS1 effectively models a linear relationship between \(\boldsymbol y\) and the combination of the 20 genes selected in \(\boldsymbol X\).
The performance of the final model can be assessed with the perf()
function, using repeated cross-validation (CV). Because a single performance value has little meaning, we propose to compare the performances of a full PLS1 model (with no variable selection) with our sPLS1 model based on the MSEP (other criteria can be used):
set.seed(33) # For reproducibility with this handbook, remove otherwise
# PLS1 model and performance
pls1.liver <- pls(X, y, ncomp = choice.ncomp, mode = "regression")
perf.pls1.liver <- perf(pls1.liver, validation = "Mfold", folds =10,
nrepeat = 5, progressBar = FALSE)
perf.pls1.liver$measures$MSEP$summary
## feature comp mean sd
## 1 Y 1 0.7281681 0.04134627
# To extract values across all repeats:
# perf.pls1.liver$measures$MSEP$values
# sPLS1 performance
perf.spls1.liver <- perf(spls1.liver, validation = "Mfold", folds = 10,
nrepeat = 5, progressBar = FALSE)
perf.spls1.liver$measures$MSEP$summary
## feature comp mean sd
## 1 Y 1 0.5958565 0.02697727
The MSEP is lower with sPLS1 compared to PLS1, indicating that the \(\boldsymbol{X}\) variables selected (listed above with selectVar()
) can be considered as a good linear combination of predictors to explain \(\boldsymbol y\).
PLS2 is a more complex problem than PLS1, as we are attempting to fit a linear combination of a subset of \(\boldsymbol{Y}\) variables that are maximally covariant with a combination of \(\boldsymbol{X}\) variables. The sparse variant allows for the selection of variables from both data sets.
As a reminder, here are the dimensions of the \(\boldsymbol{Y}\) matrix that includes clinical parameters associated with liver failure.
dim(Y)
## [1] 64 10
Similar to PLS1, we first start by tuning the number of components to select by using the perf()
function and the \(Q^2\) criterion using repeated cross-validation.
tune.pls2.liver <- pls(X = X, Y = Y, ncomp = 5, mode = 'regression')
set.seed(33) # For reproducibility with this handbook, remove otherwise
Q2.pls2.liver <- perf(tune.pls2.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
plot(Q2.pls2.liver, criterion = 'Q2.total')
Figure 18: \(Q^2\) criterion to choose the number of components in PLS2. For each component added to the PLS2 model, the averaged \(Q^2\) across repeated cross-validation is shown, with the horizontal line of 0.0975 indicating the threshold below which the addition of a dimension may not be beneficial to improve accuracy.
Figure 18 shows that one dimension should be sufficient in PLS2. We will include a second dimension in the graphical outputs, whilst focusing our interpretation on the first dimension.
Note:
nrepeat = 1
.Using the tune.spls()
function, we can perform repeated cross-validation to obtain some indication of the number of variables to select. We show an example of code below which may take some time to run (see ?tune.spls()
to use parallel computing). We had refined the grid of tested values as the tuning function tended to favour a very small signature. Hence we decided to constrain the start of the grid to 3 for a more insightful signature. Both measure = 'cor
and RSS
gave similar signature sizes, but this observation might differ for other case studies.
The optimal parameters can be output, along with a plot showing the tuning results, as shown in Figure 19:
# This code may take several min to run, parallelisation option is possible
list.keepX <- c(seq(5, 50, 5))
list.keepY <- c(3:10)
set.seed(33) # For reproducibility with this handbook, remove otherwise
tune.spls.liver <- tune.spls(X, Y, test.keepX = list.keepX,
test.keepY = list.keepY, ncomp = 2,
nrepeat = 1, folds = 10, mode = 'regression',
measure = 'cor',
# the following uses two CPUs for faster computation
# it can be commented out
BPPARAM = BiocParallel::SnowParam(workers = 14)
)
plot(tune.spls.liver)
keepX
and keepY
, the averaged correlation coefficients between the \(\boldsymbol t\) and \(\boldsymbol u\) components are shown across repeated CV, with optimal values (here corresponding to the highest mean correlation) indicated in a green square for each dimension and data set." width="60%" />
Figure 19: Tuning plot for sPLS2. For every grid value of keepX
and keepY
, the averaged correlation coefficients between the \(\boldsymbol t\) and \(\boldsymbol u\) components are shown across repeated CV, with optimal values (here corresponding to the highest mean correlation) indicated in a green square for each dimension and data set.
Here is our final model with the tuned parameters for our sPLS2 regression analysis. Note that if you choose to not run the tuning step, you can still decide to set the parameters of your choice here.
#Optimal parameters
choice.keepX <- tune.spls.liver$choice.keepX
choice.keepY <- tune.spls.liver$choice.keepY
choice.ncomp <- length(choice.keepX)
spls2.liver <- spls(X, Y, ncomp = choice.ncomp,
keepX = choice.keepX,
keepY = choice.keepY,
mode = "regression")
The amount of explained variance can be extracted for each dimension and each data set:
spls2.liver$prop_expl_var
## $X
## comp1 comp2
## 0.19254877 0.08806924
##
## $Y
## comp1 comp2
## 0.3649928 0.2180177
The selected variables can be extracted from the selectVar()
function, for example for the \(\boldsymbol X\) data set, with either their $name
or the loading $value
(not output here):
selectVar(spls2.liver, comp = 1)$X$value
The VIP measure is exported for all variables in \(\boldsymbol X\), here we only subset those that were selected (non null loading value) for component 1:
vip.spls2.liver <- vip(spls2.liver)
# just a head
head(vip.spls2.liver[selectVar(spls2.liver, comp = 1)$X$name,1])
## A_42_P620915 A_43_P14131 A_42_P578246 A_43_P11724 A_42_P840776 A_42_P675890
## 25.50450 23.12343 15.92084 15.08770 13.87380 12.96174
The (full) output shows that most \(\boldsymbol X\) variables that were selected are important for explaining \(\boldsymbol Y\), since their VIP is greater than 1.
We can examine how frequently each variable is selected when we subsample the data using the perf()
function to measure how stable the signature is (Table 1). The same could be output for other components and the \(\boldsymbol Y\) data set.
perf.spls2.liver <- perf(spls2.liver, validation = 'Mfold', folds = 10, nrepeat = 5)
# Extract stability
stab.spls2.liver.comp1 <- perf.spls2.liver$features$stability.X$comp1
# Averaged stability of the X selected features across CV runs, as shown in Table
stab.spls2.liver.comp1[1:choice.keepX[1]]
# We extract the stability measures of only the variables selected in spls2.liver
extr.stab.spls2.liver.comp1 <- stab.spls2.liver.comp1[selectVar(spls2.liver,
comp =1)$X$name]
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We recommend to mainly focus on the interpretation of the most stable selected variables (with a frequency of occurrence greater than 0.8).
Using the plotIndiv()
function, we display the sample and metadata information using the arguments group
(colour) and pch
(symbol) to better understand the similarities between samples modelled with sPLS2.
The plot on the left hand side corresponds to the projection of the samples from the \(\boldsymbol X\) data set (gene expression) and the plot on the right hand side the \(\boldsymbol Y\) data set (clinical variables).
plotIndiv(spls2.liver, ind.names = FALSE,
group = liver.toxicity$treatment$Time.Group,
pch = as.factor(liver.toxicity$treatment$Dose.Group),
col.per.group = color.mixo(1:4),
legend = TRUE, legend.title = 'Time',
legend.title.pch = 'Dose')
liver.toxicity
data. Samples are projected into the space spanned by the components associated to each data set (or block). We observe some agreement between the data sets, and a separation of the 1500 and 2000 mg doses (\(+\) and \(\times\)) in the 18h, 24h time points, and the 48h time point." width="75%" />
Figure 20: Sample plot for sPLS2 performed on the liver.toxicity
data. Samples are projected into the space spanned by the components associated to each data set (or block). We observe some agreement between the data sets, and a separation of the 1500 and 2000 mg doses (\(+\) and \(\times\)) in the 18h, 24h time points, and the 48h time point.
From Figure 20 we observe an effect of low vs. high doses of acetaminophen (component 1) as well as time of necropsy (component 2). There is some level of agreement between the two data sets, but it is not perfect!
If you run an sPLS with three dimensions, you can consider the 3D plotIndiv()
by specifying style = '3d
in the function.
The plotArrow()
option is useful in this context to visualise the level of agreement between data sets. Recall that in this plot:
plotArrow(spls2.liver, ind.names = FALSE,
group = liver.toxicity$treatment$Time.Group,
col.per.group = color.mixo(1:4),
legend.title = 'Time.Group')
liver.toxicity
data. The start of the arrow indicates the location of a given sample in the space spanned by the components associated to the gene data set, and the tip of the arrow the location of that same sample in the space spanned by the components associated to the clinical data set. We observe large shifts for 18h, 24 and 48h samples for the high doses, however the clusters of samples remain the same, as we observed in Figure 20." width="75%" />
Figure 21: Arrow plot from the sPLS2 performed on the liver.toxicity
data. The start of the arrow indicates the location of a given sample in the space spanned by the components associated to the gene data set, and the tip of the arrow the location of that same sample in the space spanned by the components associated to the clinical data set. We observe large shifts for 18h, 24 and 48h samples for the high doses, however the clusters of samples remain the same, as we observed in Figure 20.
In Figure 21 we observe that specific groups of samples seem to be located far apart from one data set to the other, indicating a potential discrepancy between the information extracted. However the groups of samples according to either dose or treatment remains similar.
Correlation circle plots illustrate the correlation structure between the two types of variables. To display only the name of the variables from the \(\boldsymbol{Y}\) data set, we use the argument var.names = c(FALSE, TRUE)
where each boolean indicates whether the variable names should be output for each data set. We also modify the size of the font, as shown in Figure 22:
plotVar(spls2.liver, cex = c(3,4), var.names = c(FALSE, TRUE))
liver.toxicity
data. The plot highlights correlations within selected genes (their names are not indicated here), within selected clinical parameters, and correlations between genes and clinical parameters on each dimension of sPLS2. This plot should be interpreted in relation to Figure 20 to better understand how the expression levels of these molecules may characterise specific sample groups." width="75%" />
Figure 22: Correlation circle plot from the sPLS2 performed on the liver.toxicity
data. The plot highlights correlations within selected genes (their names are not indicated here), within selected clinical parameters, and correlations between genes and clinical parameters on each dimension of sPLS2. This plot should be interpreted in relation to Figure 20 to better understand how the expression levels of these molecules may characterise specific sample groups.
To display variable names that are different from the original data matrix (e.g. gene ID), we set the argument var.names
as a list for each type of label, with geneBank ID for the \(\boldsymbol X\) data set, and TRUE
for the \(\boldsymbol Y\) data set:
plotVar(spls2.liver,
var.names = list(X.label = liver.toxicity$gene.ID[,'geneBank'],
Y.label = TRUE), cex = c(3,4))
$gene.ID
(Note: some gene names are missing)." 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alt="Correlation circle plot from the sPLS2 performed on the liver.toxicity
data. A variant of Figure 22 with gene names that are available in $gene.ID
(Note: some gene names are missing)." width="75%" />
Figure 23: Correlation circle plot from the sPLS2 performed on the liver.toxicity
data. A variant of Figure 22 with gene names that are available in $gene.ID
(Note: some gene names are missing).
The correlation circle plots highlight the contributing variables that, together, explain the covariance between the two data sets. In addition, specific subsets of molecules can be further investigated, and in relation with the sample group they may characterise. The latter can be examined with additional plots (for example boxplots with respect to known sample groups and expression levels of specific variables, as we showed in the PCA case study previously. The next step would be to examine the validity of the biological relationship between the clusters of genes with some of the clinical variables that we observe in this plot.
A 3D plot is also available in plotVar()
with the argument style = '3d
. It requires an sPLS2 model with at least three dimensions.
Other plots are available to complement the information from the correlation circle plots, such as Relevance networks and Clustered Image Maps (CIMs), as described in Module 2.
The network in sPLS2 displays only the variables selected by sPLS, with an additional cutoff
similarity value argument (absolute value between 0 and 1) to improve interpretation. Because Rstudio sometimes struggles with the margin size of this plot, we can either launch X11()
prior to plotting the network, or use the arguments save
and name.save
as shown below:
# Define red and green colours for the edges
color.edge <- color.GreenRed(50)
# X11() # To open a new window for Rstudio
network(spls2.liver, comp = 1:2,
cutoff = 0.7,
shape.node = c("rectangle", "circle"),
color.node = c("cyan", "pink"),
color.edge = color.edge,
# To save the plot, unotherwise:
# save = 'pdf', name.save = 'network_liver'
)
cutoff
argument (optional)." 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" alt="Network representation from the sPLS2 performed on the liver.toxicity
data. The networks are bipartite, where each edge links a gene (rectangle) to a clinical variable (circle) node, according to a similarity matrix described in Module 2. Only variables selected by sPLS2 on the two dimensions are represented and are further filtered here according to a cutoff
argument (optional)." width="75%" />
Figure 24: Network representation from the sPLS2 performed on the liver.toxicity
data. The networks are bipartite, where each edge links a gene (rectangle) to a clinical variable (circle) node, according to a similarity matrix described in Module 2. Only variables selected by sPLS2 on the two dimensions are represented and are further filtered here according to a cutoff
argument (optional).
Figure 24 shows two distinct groups of variables. The first cluster groups four clinical parameters that are mostly positively associated with selected genes. The second group includes one clinical parameter negatively associated with other selected genes. These observations are similar to what was observed in the correlation circle plot in Figure 22.
Note:
igraph
R package Csardi, Nepusz, et al. (2006)).The Clustered Image Map also allows us to visualise correlations between variables. Here we choose to represent the variables selected on the two dimensions and we save the plot as a pdf figure.
# X11() # To open a new window if the graphic is too large
cim(spls2.liver, comp = 1:2, xlab = "clinic", ylab = "genes",
# To save the plot, uncomment:
# save = 'pdf', name.save = 'cim_liver'
)
liver.toxicity
data. The plot displays the similarity values (as described in Module 2) between the \(\boldsymbol X\) and \(\boldsymbol Y\) variables selected across two dimensions, and clustered with a complete Euclidean distance method." width="75%" />
Figure 25: Clustered Image Map from the sPLS2 performed on the liver.toxicity
data. The plot displays the similarity values (as described in Module 2) between the \(\boldsymbol X\) and \(\boldsymbol Y\) variables selected across two dimensions, and clustered with a complete Euclidean distance method.
The CIM in Figure 25 shows that the clinical variables can be separated into three clusters, each of them either positively or negatively associated with two groups of genes. This is similar to what we have observed in Figure 22. We would give a similar interpretation to the relevance network, had we also used a cutoff
threshold in cim()
.
Note:
To finish, we assess the performance of sPLS2. As an element of comparison, we consider sPLS2 and PLS2 that includes all variables, to give insights into the different methods.
# Comparisons of final models (PLS, sPLS)
## PLS
pls.liver <- pls(X, Y, mode = 'regression', ncomp = 2)
perf.pls.liver <- perf(pls.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
## Performance for the sPLS model ran earlier
perf.spls.liver <- perf(spls2.liver, validation = 'Mfold', folds = 10,
nrepeat = 5)
plot(c(1,2), perf.pls.liver$measures$cor.upred$summary$mean,
col = 'blue', pch = 16,
ylim = c(0.6,1), xaxt = 'n',
xlab = 'Component', ylab = 't or u Cor',
main = 's/PLS performance based on Correlation')
axis(1, 1:2) # X-axis label
points(perf.pls.liver$measures$cor.tpred$summary$mean, col = 'red', pch = 16)
points(perf.spls.liver$measures$cor.upred$summary$mean, col = 'blue', pch = 17)
points(perf.spls.liver$measures$cor.tpred$summary$mean, col = 'red', pch = 17)
legend('bottomleft', col = c('blue', 'red', 'blue', 'red'),
pch = c(16, 16, 17, 17), c('u PLS', 't PLS', 'u sPLS', 't sPLS'))
Figure 26: Comparison of the performance of PLS2 and sPLS2, based on the correlation between the actual and predicted components \(\boldsymbol{t,u}\) associated to each data set for each component.
We extract the correlation between the actual and predicted components \(\boldsymbol{t,u}\) associated to each data set in Figure 26. The correlation remains high on the first dimension, even when variables are selected. On the second dimension the correlation coefficients are equivalent or slightly lower in sPLS compared to PLS. Overall this performance comparison indicates that the variable selection in sPLS still retains relevant information compared to a model that includes all variables.
Note:
The Small Round Blue Cell Tumours (SRBCT) data set from (Khan et al. 2001) includes the expression levels of 2,308 genes measured on 63 samples. The samples are divided into four classes: 8 Burkitt Lymphoma (BL), 23 Ewing Sarcoma (EWS), 12 neuroblastoma (NB), and 20 rhabdomyosarcoma (RMS). The data are directly available in a processed and normalised format from the mixOmics
package and contains the following:
$gene
: A data frame with 63 rows and 2,308 columns. These are the expression levels of 2,308 genes in 63 subjects,
$class
: A vector containing the class of tumour for each individual (4 classes in total),
$gene.name
: A data frame with 2,308 rows and 2 columns containing further information on the genes.
More details can be found in ?srbct
. We will illustrate PLS-DA and sPLS-DA which are suited for large biological data sets where the aim is to identify molecular signatures, as well as classify samples. We will analyse the gene expression levels of srbct$gene
to discover which genes may best discriminate the 4 groups of tumours.
We first load the data from the package. We then set up the data so that \(\boldsymbol X\) is the gene expression matrix and \(\boldsymbol y\) is the factor indicating sample class membership. \(\boldsymbol y\) will be transformed into a dummy matrix \(\boldsymbol Y\) inside the function. We also check that the dimensions are correct and match both \(\boldsymbol X\) and \(\boldsymbol y\):
library(mixOmics)
data(srbct)
X <- srbct$gene
# Outcome y that will be internally coded as dummy:
Y <- srbct$class
dim(X); length(Y)
## [1] 63 2308
## [1] 63
summary(Y)
## EWS BL NB RMS
## 23 8 12 20
As covered in Module 3, PCA is a useful tool to explore the gene expression data and to assess for sample similarities between tumour types. Remember that PCA is an unsupervised approach, but we can colour the samples by their class to assist in interpreting the PCA (Figure 27). Here we center (default argument) and scale the data:
pca.srbct <- pca(X, ncomp = 3, scale = TRUE)
plotIndiv(pca.srbct, group = srbct$class, ind.names = FALSE,
legend = TRUE,
title = 'SRBCT, PCA comp 1 - 2')
Figure 27: Preliminary (unsupervised) analysis with PCA on the SRBCT
gene expression data. Samples are projected into the space spanned by the principal components 1 and 2. The tumour types are not clustered, meaning that the major source of variation cannot be explained by tumour types. Instead, samples seem to cluster according to an unknown source of variation.
We observe almost no separation between the different tumour types in the PCA sample plot, with perhaps the exception of the NB samples that tend to cluster with other samples. This preliminary exploration teaches us two important findings:
The perf()
function evaluates the performance of PLS-DA - i.e., its ability to rightly classify ‘new’ samples into their tumour category using repeated cross-validation. We initially choose a large number of components (here ncomp = 10
) and assess the model as we gradually increase the number of components. Here we use 3-fold CV repeated 10 times. In Module 2, we provided further guidelines on how to choose the folds
and nrepeat
parameters:
plsda.srbct <- plsda(X,Y, ncomp = 10)
set.seed(30) # For reproducibility with this handbook, remove otherwise
perf.plsda.srbct <- perf(plsda.srbct, validation = 'Mfold', folds = 3,
progressBar = FALSE, # Set to TRUE to track progress
nrepeat = 10) # We suggest nrepeat = 50
plot(perf.plsda.srbct, sd = TRUE, legend.position = 'horizontal')
max.dist
, centroids.dist
and mahalanobis.dist
. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components." 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alt="Tuning the number of components in PLS-DA on the SRBCT
gene expression data. For each component, repeated cross-validation (10 \(\times 3-\)fold CV) is used to evaluate the PLS-DA classification performance (overall and balanced error rate BER), for each type of prediction distance; max.dist
, centroids.dist
and mahalanobis.dist
. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components." width="75%" />
Figure 28: Tuning the number of components in PLS-DA on the SRBCT
gene expression data. For each component, repeated cross-validation (10 \(\times 3-\)fold CV) is used to evaluate the PLS-DA classification performance (overall and balanced error rate BER), for each type of prediction distance; max.dist
, centroids.dist
and mahalanobis.dist
. Bars show the standard deviation across the repeated folds. The plot shows that the error rate reaches a minimum from 3 components.
From the classification performance output presented in Figure 28, we observe that:
There are some slight differences between the overall and balanced error rates (BER) with BER > overall, suggesting that minority classes might be ignored from the classification task when considering the overall performance (summary(Y)
shows that BL only includes 8 samples). In general the trend is the same, however, and for further tuning with sPLS-DA we will consider the BER.
The error rate decreases and reaches a minimum for ncomp = 3
for the max.dist
distance. These parameters will be included in further analyses.
Notes:
ncomp
) as a ‘compounding’ model (i.e. PLS-DA with component 3 includes the trained model on the previous two components).Additional numerical outputs from the performance results are listed and can be reported as performance measures (not output here):
perf.plsda.srbct
We now run our final PLS-DA model that includes three components:
final.plsda.srbct <- plsda(X,Y, ncomp = 3)
We output the sample plots for the dimensions of interest (up to three). By default, the samples are coloured according to their class membership. We also add confidence ellipses (ellipse = TRUE
, confidence level set to 95% by default, see the argument ellipse.level
) in Figure 29. A 3D plot could also be insightful (use the argument type = '3D'
).
plotIndiv(final.plsda.srbct, ind.names = FALSE, legend=TRUE,
comp=c(1,2), ellipse = TRUE,
title = 'PLS-DA on SRBCT comp 1-2',
X.label = 'PLS-DA comp 1', Y.label = 'PLS-DA comp 2')
plotIndiv(final.plsda.srbct, ind.names = FALSE, legend=TRUE,
comp=c(2,3), ellipse = TRUE,
title = 'PLS-DA on SRBCT comp 2-3',
X.label = 'PLS-DA comp 2', Y.label = 'PLS-DA comp 3')
SRBCT
gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes." width="50%" />SRBCT
gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes." width="50%" />
Figure 29: Sample plots from PLS-DA performed on the SRBCT
gene expression data. Samples are projected into the space spanned by the first three components. (a) Components 1 and 2 and (b) Components 1 and 3. Samples are coloured by their tumour subtypes. Component 1 discriminates RMS + EWS vs. NB + BL, component 2 discriminates RMS + NB vs. EWS + BL, while component 3 discriminates further the NB and BL groups. It is the combination of all three components that enables us to discriminate all classes.
We can observe improved clustering according to tumour subtypes, compared with PCA. This is to be expected since the PLS-DA model includes the class information of each sample. We observe some discrimination between the NB and BL samples vs. the others on the first component (x-axis), and EWS and RMS vs. the others on the second component (y-axis). From the plotIndiv()
function, the axis labels indicate the amount of variation explained per component. However, the interpretation of this amount is not as important as in PCA, as PLS-DA aims to maximise the covariance between components associated to \(\boldsymbol X\) and \(\boldsymbol Y\), rather than the variance \(\boldsymbol X\).
We can rerun a more extensive performance evaluation with more repeats for our final model:
set.seed(30) # For reproducibility with this handbook, remove otherwise
perf.final.plsda.srbct <- perf(final.plsda.srbct, validation = 'Mfold',
folds = 3,
progressBar = FALSE, # TRUE to track progress
nrepeat = 10) # we recommend 50
Retaining only the BER and the max.dist
, numerical outputs of interest include the final overall performance for 3 components:
perf.final.plsda.srbct$error.rate$BER[, 'max.dist']
## comp1 comp2 comp3
## 0.53850543 0.25986413 0.04481884
As well as the error rate per class across each component:
perf.final.plsda.srbct$error.rate.class$max.dist
## comp1 comp2 comp3
## EWS 0.2565217 0.08695652 0.08260870
## BL 0.8125000 0.51250000 0.00000000
## NB 0.3000000 0.37500000 0.04166667
## RMS 0.7850000 0.06500000 0.05500000
From this output, we can see that the first component tends to classify EWS and NB better than the other classes. As components 2 and then 3 are added, the classification improves for all classes. However we see a slight increase in classification error in component 3 for EWS and RMS while BL is perfectly classified. A permutation test could also be conducted to conclude about the significance of the differences between sample groups, but is not currently implemented in the package.
A prediction background can be added to the sample plot by calculating a background surface first, before overlaying the sample plot (Figure 30, see Extra Reading material, or ?background.predict
). We give an example of the code below based on the maximum prediction distance:
background.max <- background.predict(final.plsda.srbct,
comp.predicted = 2,
dist = 'max.dist')
plotIndiv(final.plsda.srbct, comp = 1:2, group = srbct$class,
ind.names = FALSE, title = 'Maximum distance',
legend = TRUE, background = background.max)
Figure 30: Sample plots from PLS-DA on the SRBCT
gene expression data and prediction areas based on prediction distances. From our usual sample plot, we overlay a background prediction area based on permutations from the first two PLS-DA components using the three different types of prediction distances. The outputs show how the prediction distance can influence the quality of the prediction, with samples projected into a wrong class area, and hence resulting in predicted misclassification. (Currently, the prediction area background can only be calculated for the first two components).
Figure 30 shows the differences in prediction according to the prediction distance, and can be used as a further diagnostic tool for distance choice. It also highlights the characteristics of the distances. For example the max.dist
is a linear distance, whereas both centroids.dist
and mahalanobis.dist
are non linear. Our experience has shown that as discrimination of the classes becomes more challenging, the complexity of the distances (from maximum to Mahalanobis distance) should increase, see details in the Extra reading material.
In high-throughput experiments, we expect that many of the 2308 genes in \(\boldsymbol X\) are noisy or uninformative to characterise the different classes. An sPLS-DA analysis will help refine the sample clusters and select a small subset of variables relevant to discriminate each class.
We estimate the classification error rate with respect to the number of selected variables in the model with the function tune.splsda()
. The tuning is being performed one component at a time inside the function and the optimal number of variables to select is automatically retrieved after each component run, as described in Module 2.
Previously, we determined the number of components to be ncomp = 3
with PLS-DA. Here we set ncomp = 4
to further assess if this would be the case for a sparse model, and use 5-fold cross validation repeated 10 times. We also choose the maximum prediction distance.
Note:
We first define a grid of keepX
values. For example here, we define a fine grid at the start, and then specify a coarser, larger sequence of values:
# Grid of possible keepX values that will be tested for each comp
list.keepX <- c(1:10, seq(20, 100, 10))
list.keepX
## [1] 1 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 100
# This chunk takes ~ 2 min to run
# Some convergence issues may arise but it is ok as this is run on CV folds
tune.splsda.srbct <- tune.splsda(X, Y, ncomp = 4, validation = 'Mfold',
folds = 5, dist = 'max.dist',
test.keepX = list.keepX, nrepeat = 10)
The following command line will output the mean error rate for each component and each tested keepX
value given the past (tuned) components.
# Just a head of the classification error rate per keepX (in rows) and comp
head(tune.splsda.srbct$error.rate)
## comp1 comp2 comp3 comp4
## 1 0.5942844 0.3252355 0.07589674 0.010923913
## 2 0.5638859 0.3071830 0.05585145 0.007798913
## 3 0.5491938 0.2985507 0.04576087 0.006711957
## 4 0.5360054 0.2920743 0.03071558 0.006711957
## 5 0.5211685 0.2855707 0.02821558 0.006711957
## 6 0.5150362 0.2819837 0.02254529 0.005461957
When we examine each individual row, this output globally shows that the classification error rate continues to decrease after the third component in sparse PLS-DA.
We display the mean classification error rate on each component, bearing in mind that each component is conditional on the previous components calculated with the optimal number of selected variables. The diamond in Figure 31 indicates the best keepX
value to achieve the lowest error rate per component.
# To show the error bars across the repeats:
plot(tune.splsda.srbct, sd = TRUE)
keepX
value chosen for the previous component (comp 1)." 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cpYvXp19Pb2Rl9fX/T390c2m53RJp/PR19fX/T19cWBAwciIiKVSs0IFZqamqKmpqbcwwcAAACAWQkRACLisssui66urhgeHj5tu+rq6rjiiiuiqqoq1qxZExFRKNg8FSr09vbG4ODgKTMbstlsoaDzlMrKyhmhQlNTU1RWVpb76QAAAACAiBAiAETE5GyEl770pfHwww9HX19f0TaNjY1x3XXXRVVV1Yz7pxdsnpLNZguBwtS/IyMjpxxzfHw8Ojo6oqOjo3BfbW1tIVRobm6OxsZG9RWITCazaMdOpVKW2gIAAACKEiIAPK+6ujpuuummOHbsWOzatSsGBwcjYrL48tatW2P16tUln2hNpVLR2toara2thfvGx8dnhAp9fX0xPj5+yr7Dw8MxPDw8o75CQ0PDjKWQ6uvrnfS9iIyNjcV//ud/LtrxX/WqV50SjgEAAABECBEAZkgkErF69eoYHh6OZ599NiIiVq9eXVi66GxUVlbGihUrYsWKFYX7hoeHC6FCb2/vrPUV+vv7o7+/v1C3IZVKRWNjYyFUaG5uVl8BAAAAgAUnRAAoo9ra2qitrY3Vq1dHxGRgMDg4WAgV+vr6YmBgoGh9hZ6enujp6SncV1lZeUrhZvUVLgyJRKLk/8vps1v8/wMAAABnS4gAsIRMLV3U0NAQ69evj4jJwKC/v3/GUkjFCkCPj4/HiRMn4sSJE4X7ampqTincrL7C+aeysjJuu+22ktrecccdhdCp1H0AAAAAZiNEAFjiUqlUtLS0REtLS+G+iYmJU+orjI2NnbLvyMhIjIyMxNGjRwv3NTQ0zCjcXF9fH8lkstzDBAAAAGAJEiIAnIfS6XQsX748li9fXrhvZGRkRrDQ398fmUzmlH0HBgZiYGCgUF8hmUwWZilMzVqora0t9xC5yPT09EQul1uUYzc2NkY6nS73EAEAAOC8JEQAuEDU1NRETU3NKfUVps9W6O/vP6W+Qi6XO6W+QjqdPqW+QlVVVbmHyAXs4YcfnlHPYSFdf/31MwI3AAAAoHRCBIAL1PT6CuvWrYuIycBgqr7CVLAwNDR0yr4TExPR2dkZnZ2dhfuqq6sLoUJzc3M0NjZGRYU/IwAAAAAXMmd/AC4iyWSyEAJMmZiYmDFbobe3t2h9hdHR0Th27FgcO3ascF99ff2M2QoNDQ3qK3BG2traii6/9UI9PT2Fdi0tLSUFWZWVleUeHgAAAJy3hAgAF7l0Oh3Lli2LZcuWFe4bHR09pXBzsRO8g4ODMTg4GIcOHYqIyZCisbFxRuHm2traSCQSZ93PbDYbAwMDhe+Hh4djYmLCWvcXiGuuuaakdt/+9rejv78/IiKuvPLKqK+vL3fXl6R8Ph/ZbHbRjm8WEgAAwMXDJ0AATlFdXR2rVq2KVatWRcTkCcmhoaFCqNDb2xsDAwOnFMLN5XKF7VMqKipmhArNzc3zrq9w8ODBePbZZ2esmX/ixIn4+te/Hpdcckls3bp1QYIKuFD09fXFPffcsyjHTiaTcfvtt5d7iAAAAJwjQgQA5pRIJKK+vj7q6+tj7dq1EXGyvsL02QqDg4On7JvJZIrWV5geKjQ2Ns46o2Dnzp2xd+/eotuy2Wzs2rUr+vr64rrrrhMkAAAAACwwIQIAZ2R6fYWNGzdGxGRg8ML6CqOjo6fsOzo6GqOjo3H8+PHCfXV1dTPqKzQ2NkZHR8esAcJ0HR0dsXv37ti2bVu5nxZYEhKJREm1IPL5fExMTBS+L2UfdU8AAAAuLkIEABZMRUVFtLW1RVtbW+G+sbGxwhJHfX190dfXN+Ok5ZShoaEYGhqKw4cPR8T8T1Tu3bs3Nm/ebK12iIimpqa47bbb5mw3OjoaX//61yMiora2Nr7ru76r3F0HAABgiXGmBYBFVVVVFStXroyVK1cW7hsaGpoxW6G/v79ofYX5yGaz0dnZWajjAAAAAMDZEyIAcM7V1dVFXV3djPoKAwMDM5ZCGhgYmPdxh4eHyz00LiL5fD527ty5aMe/5JJL5l2EHAAAABaaEAGAsksmk9HU1BRNTU2xYcOGiIg4fvx4PPzww+XuGswqn8/Hvn37Fu3469atEyKcoTvvvDMymcyiHPv666+P5ubmcg8RAADgnBEiALAkNTU1zXufPXv2RETE+vXrI51Ol3sIQJmMj49HNptdlGPPd6k1AACA850QAYAlqbq6OlpbW6O7u7vkfSYmJmLnzp2xa9euWL9+fWzatClqa2vLPRQuUIlEIi6//PKS2j777LOFk9rbt2+PVCo15z7V1dXlHiIAAAAIEQBYui677LK49957I5/Pz9m2oqKisHxJNpuN9vb2aG9vj5UrV8bq1asjkUiUezhcYBKJRGzatKmktrt37y6ECBs2bDBTZpHddtttJb1u3H333TE0NBQRES9/+cujpqZmzn1KCYAAAAAuJEIEAJas5ubmuOqqq+KJJ5447QnBrVu3xtatW+Po0aPR3t4efX19hW3Hjx+P48ePR01NTWQymVi7dm0kk8lyDw1YRGdyor+ioiIqKrw1BgAAeCGflABY0tauXRt1dXWxc+fOU5Y2amhoiEsvvTRWrlxZaLt27dro7u6Offv2xfHjxwttR0ZG4sknn4znnnsuNm3aFOvXr4/KyspyDw8AAABgSRMiALDkNTc3x0tf+tLYu3dv7Ny5MyImA4Orr766aPvW1tZobW2N4eHhaG9vj4MHDxaWkhkbG4tnn302du3aFevWrYtNmzZFfX19uYcIAAAAsCQJEQA4b0xfaqSUZUdqa2vj8ssvj3Xr1sW+ffuiq6srRkdHIyIil8vFgQMH4sCBA7F8+fLYvHlzLFu2rNxDBAAAAFhShAgAXPAqKipixYoVsWPHjjhx4kS0t7dHT09PYfuJEyfixIkT0dDQEJs2bYo1a9YongoAAAAQQgQALiKJRCJWr14dq1evjt7e3ti3b18cO3asULR5YGAgnnzyyXj22Wdj48aNsWHDhqiqqip3t+G8NTY2FhMTE4ty7HQ67fcTAADgHBAiAHBRam5ujmuuuSZGRkZi//79ceDAgchkMhERMT4+Hrt27Yo9e/bEmjVrYtOmTdHY2FjuLsN557nnnouDBw8uyrE3bNgQV1xxRbmHCAAAcMETIgBwUaupqYnLLrsstm3bFocOHYr29vYYGhqKiMm6CYcOHYpDhw5FW1tbbN68OZYvXx6JRKLc3QYAAAA4J4QIABARqVSqsIRRR0dHtLe3R1dXV2F7V1dXdHV1RV1dXWzatCnWrVunbgLMoaampqRZPGNjYzE2NhYREVVVVSUtU1RTU1Pu4QEAAFwUhAickVwuFydOnCip7dRa4xERHR0dJV3Bu2zZMifngLJIJBKxcuXKWLlyZfT398e+ffvi6NGjkcvlIiJiaGgonnrqqXjuuediw4YNsXHjxqiuri53t2FJ2rp1a2zdunXOdrt27Ypdu3ZFRMTGjRtL2gcAAIBzQ4jAGclms/Hwww/Pe79HHnmkpHa33nqrKwyBsmtsbIyrr746LrvsskLdhPHx8YiImJiYiD179sTevXtj9erVsXnz5mhqaip3lwEAAAAWlBABAOZQVVUVl156aWzZsiWOHDkS+/bti8HBwYiYnG115MiROHLkSLS0tMTmzZtj5cqV6iYAAAAAFwQhAmckmUzGqlWrSmo7OjpauF3qkh+WMgKWolQqFevXr4/169fHiRMnor29fcbSbj09PdHT0xM1NTWxadOmWL9+fVRU+FMLF6r9+/fHzp0752w3fWnHiCgpZGxpaYkbbrih3EMEAAAQInBmUqlUXHvttSW1PXbsWOHD86pVq1ydC1wQli9fHsuXL4/BwcHYt29fHD58uFA3YWRkJJ555pnYtWtXrF+/PjZu3Bi1tbXl7jKwwPL5fGSz2UU59mIdFwAAYL6ECABwFurr6+PKK6+M7du3x4EDB2L//v0xNjYWERGZTCb27dsX+/bti1WrVsXmzZujpaWl3F0GAAAAKJkQAQAWQGVlZWzdujUuueSSOHr0aOzbty/6+/sL248dOxbHjh2Lpqam2Lx5c6xatSqSyWS5uw2chU2bNsXGjRvnbHfw4MH4zne+ExERbW1tlikCAADOK0IEAFhAyWQy1q5dG2vXro3u7u7Yt29fHD9+vLC9r68vHnvssaiuro6NGzfGhg0bIp1Ol7vbwBkqZZnG6W0SiYSlHQEAgPOKEAEAFklra2u0trbG0NBQtLe3x6FDhwrrnI+Ojsazzz4bu3fvjnXr1sWmTZuirq6u3F0GAAAAmEGIAACLrK6uLnbs2BGXXnppHDx4MNrb22N0dDQiJoun7t+/P/bv3x8rVqyIzZs3R1tbW7m7DAAAABARQgQAOGfS6XRccsklsXnz5jh27Fjs27cvent7C9s7Ojqio6MjGhoaYvPmzbFmzRp1EwAAAICyEiIAwDmWSCRi9erVsXr16ujt7Y19+/bFsWPHIp/PR0TEwMBAPPHEE7Fz585C3YSqqqpydxsAAAC4CAkRAKCMmpub45prromRkZFob2+PgwcPRiaTiYiI8fHx2LVrV+zZsyfWrl0bmzZtioaGhlmP1dfXVwgipr5vamoq9xABAACA85gQAQCWgJqamnjRi14U27Zti0OHDkV7e3sMDw9HREQul4uDBw/GwYMHY9myZbF58+ZYtmxZJBKJiIgYHByMJ598Mnp6emYc8+67746Wlpa44oorThs+AAAAAMxGiAAAS0hFRUVs2rQpNm7cGB0dHbFv377o7u4ubO/s7IzOzs6oq6uLzZs3R319fTz44IORzWaLHq+npyfuueeeuPHGG6O5ubncwwMAAADOM0IEAFiCEolErFy5MlauXBn9/f2xb9++OHLkSGG5oqGhofjOd75T0rGy2Ww88sgj8cpXvjJSqVS5hwYAAACcR4QIALDENTY2xtVXXx3bt2+P/fv3x4EDB2JiYmJexxgdHY0DBw7E5s2byz0cAMpsz549kcvlFuXY69ati5qamnIPEQCABSREAIDzRHV1dWzfvj22bt0ahw8fjmeeeWbWZYyKOXbs2HkfIkw/6TW9iDQApduzZ09kMplFOXZbW5sQ4Qx1dXXN6+/6fDQ1NZV7eADAeUyIAADnmVQqFRs2bIg9e/bEyMhIyfv19PTEV77ylaiqqprzq7KyMpLJZLmHWnD48OHYu3dvDA4OFu679957Y/369bF169ZIp9Pl7iIAnJUnnnhiXn/X5+Paa6/1txIAOGNCBAA4T53JSf5sNhvDw8MxPDw8Z9vKysqSAofFPCmRz+fj8ccfjyNHjpyyLZPJxL59++LYsWNxww03RF1d3aL1A+BCsm3btpKWM9q3b1+Mj49HRMTmzZujsrJyzn3MQgAAuPAIEQDgPNXQ0BBDQ0Mlt08kEvNaAmh8fDzGx8djYGDgtO2SyWRh9kJ1dfVpZzfMt7Dzzp07iwYI042MjMRDDz0Ut9xyi8LRACUodWm7w4cPF0KEjRs3Rm1tbbm7fkFbsWJFjI2Nzdmuq6urUBupra2tpDC/urp60ZZKAgAufEIEADhPrVmzJo4dO1Zy+xe/+MWxcuXKGBsbK+mr1KKbuVwuRkZGYmRkJPr6+k7btqKi4rRBw/TZDcPDw9He3l5SH4aGhmLfvn2xdevWsv1/AMDZ2LFjR0nt7r333ujp6YmIiO3bt0dzc3NJ+3V1dZV7iADAeUqIwBkZGB+J2z/9ByW1nV60raKitB+5z/7gr8equuZyDxNgSVu1alW0trZGd3f3nG1bW1tj1apVkUgkoqampqTlJiYmJkoKG6auUi1FJpOJwcHBGbUNikkkEpFKpeY1c+Lw4cNCBAAAAFhgQgTOSC6Xj2e7j5z9gWYxkc2c/UEALgLXXnttPPDAA9Hf3z9rm4aGhrjmmmsikUjM69jpdDrS6XTU19eftl0+ny95dkOpSynk8/kZIXQphoaGIpfLLamC0AAAAHC+EyIAwHmssrIyXvayl8WePXti//79hTWSIyZDgI0bN8aWLVsWtVZAIpGI6urqqK6unrNtJpMpeXbDfGYhTHn66adjxYoV0draWvLsNwAAAGB2Pl1zRhqqquOhn/njktre8snfjtHs5Emt+3/qDyOVnPtE1ur6lnIPEeC8kUql4tJLL41t27bFl7/85cLJ99tuu23esw8WW0VFRVRUVERdXd1p2+Xz+XjyySfj0KFD8zr+gQMH4sCBA5FIJKK5uTna2tpi2bJl0dzcbIYCAAAAnAEhAmckmUjGpqYVJbWdfgJrU9OKkkIEAObvhYHBUgsQ5juW1atXzztEmJLP56Onpyd6enpi9+7dkUwmo7W1NZYtWxZtbW3R2Nh4Xj8/AAAAcK4IEQCAJWn58uXR3Nwcvb29c7ZNJBJx1VVXxdDQUHR2dkZfX9+M5ZByuVx0dnZGZ2dnREwu9dTW1laYqTDXzAgAAAC4WAkRACi7qXXw5zIyMlK4PT4+ftpiwlOsi39+u+aaa+Kee+6Z8+fjiiuuiLVr10ZExKWXXhqZTCa6u7ujs7Mzurq6YmBgYEb7iYmJOHbsWBw7diwiIqqrqwuzFNra2kqq7wAAAAAXA2dWACi7/fv3x+7du+e1z9GjR+Po0aNztlu5cmW86EUvKvcQOUM1NTVx0003xZNPPlmYRTBddXV17NixI1auXDnj/oqKilixYkWsWDG59N7Y2Fh0dXVFV1dXdHZ2zgikIiJGR0fj0KFDheWT6uvrC7MUWltbI51Ol/upAAAAgLIQIgAAS1pNTU3ccMMN0d/fHw8++GBhVsKOHTti3bp1kUrNXWunqqoq1qxZE2vWrImIiOHh4cIsha6urhgfH5/RfnBwMAYHB2P//v0REdHU1FSYqdDS0lLSY+bz+Thy5Ejs3bt3xvG//e1vx8aNG2PTpk2KPQMAALDkCREAKLuamppobm5elGPX19eXe3gskMbGxqiqqiqECG1tbSWdzC+mtrY2NmzYEBs2bIh8Ph8DAwOFWQrd3d2RzWZntO/r64u+vr7Ys2dPJJPJaGlpKYQKTU1NpxRpzmaz8dhjj8Xx48dPeeyRkZHYuXNnHDt2LF7ykpdEZWVluZ9aAAAAmJUQAYCyW79+faxfv37Rjj88PFzuIbKEJRKJaGxsjMbGxti8eXPkcrno7e0thAq9vb2nFGmemsEQMbl00vQizfX19fH0008XDRCm6+3tjUceeSRuvPHGU0KIcyGfz8+oK5LJZGJoaEiRaQAAAGYQIgAATJNMJqO1tTVaW1tj27Ztkc1mZxRpfmFB70wmE8ePHy+EBul0OiYmJkp6rO7u7jh69GhhmaVzpbe3N5588skZBafHx8fjG9/4RqxcuTJ27NihuDQAAAARIUQAADitVCoVy5cvj+XLl0fE5Mn26UWaXzjTpdQAYcqhQ4fOaYjQ0dERDz/88IzZFdMdP348ent746abboqamppz1i8AAACWJiECAMA8VFZWxurVq2P16tURMVnjYGqWQmdn5ylFmufS2dkZ99xzT6TT6dN+VVZWRjqdjoqKijOuBTE2NhaPPvrorAHC9HaPPPJI3HTTTWVZagkAAIClQ4gAAHAWampqZtT1+MY3vhFDQ0PzOkZvb++82ieTyUKoUOpXZWVl7Nmz55Si0bPp6+uLjo6OWLlyZbmfYgAAAMpIiABcVA72d0bHUN+c7Q71HI4DI5NFU8d7aqP/aHLOfZbVNsbGpuXlHiJQZnV1dfMOEeYrl8vF6OhojI6OLurjHD9+/IIMEfL5fHR1dc2YNXLs2LFYv359VFR4ewwAADCdT0nAReUvH/5yfPzx/5zfTgdLa/bjO14eH/qeny33EIEyW7FiRXR0dJTcfv369bFhw4aYmJiY82t8fDwymUxkMplzMpYX1nu4EAwODsZjjz12SoHsZ555Jvbs2RM7duwoLFUFAJy/Dh8+HCdOnFiUY7e1tRVmoc7liSeeiFwutyj92LZtW9TV1Z1yf19fX+zbt29RHrOqqipe9KIXLcqxgaVLiAAAsIDWrl0bu3fvLmmWQCqVim3btkV1dfW8HiOfz88IFcbHx0sKIcbGxub1OMPDw9Hd3R0tLS0XRG2EgYGBuPfee2cNYcbHx+PRRx+NiYmJ2LBhQ7m7CwCchd7e3jhy5MiiHDuVSpUcIhw5cmTRQoSNGzcWDRFGRkYWbew1NTVCBLgICRGAi8rWllVxy7rL5my3r/tYHB7ujYiI1TVNsaVt7qtSt7W6chWY/FB57bXXxn333TfnB8arrrpq3gFCREQikYjKysqorKyc137f/va3T7kC/3RGR0fjvvvui6qqqli9enWsWbMmmpubz8nzuNDy+Xw88sgjJc3ieOqpp6K1tTXq6+vL3W0AWNJGRkbm9d5iPtLpdLS2tpZ7iMyhu7s7JiYm5mw3ODhYaJfNZiOdTs+5T2NjY9TU1JR7iEAIEYCLzC+8+Lb4hRffNme73/3K38eHn/l6RES8dt1V8cevs0wRULrm5ua46aab4vHHH4+BgYFTtldXV8eVV14Zy5ef2zoqa9asOaMP+mNjY9He3h7t7e1RU1MTa9asidWrV0djY+M57f/ZOHbsWMm1KvL5fOzduzeuuuqqcncbAJa0zs7OePLJJxfl2I2NjXHLLbec8f7r168vKYQ4fvx44ar9VatWlbSsYW1tbcn9ePGLXxz5fH7Odk8//XRh1uiOHTtKulik2CyEiMn3otdcc82c+w8PD8ezzz5bONall1465z6pVGrG988880z09c1dd/BMXHHFFWaHwhIhRAAuKoODgyUtMTIxrdjmxMREdHZ2zrlPdXW1q1aBgqkPvp2dnfHII49ENpuNiIirr746Vq1adcoHsHNh48aNcfDgwZJOpi9btiwaGhri6NGjM143R0ZGYs+ePbFnz56oq6srzFBY6q9/86lTcSbtAYClpbGxsaQLHqbXgGpoaFjw2kirVq0qqd3UyfyIyRpbZ3MFfnV1dUnj6OvrKzxuZWWlulDArIQIwEVl//79sX///jnbTb+SYmCgPx544IE591m3bp2rVoEZEolELF++PFKpVCFEWLFiRVkChIjJK8euu+66eOCBB04bqLa2tsZ1110XqVQqLrvssujp6YmjR4/G0aNHY3xayDo0NBS7d++O3bt3Fz50r1mzZl5X5y22fD4fQ0ND0dvbO6/9xsfHI5vNlu3/CgDOB7W1tSWdJB8bG4uenp6ImCzM29LSUtKxWfqWLVtWUuDR1dVVWM6oqamppH38DMDSIUQAALiI1NfXxy233BLPPfdcHDp0aEbdhsrKyrjkkkti06ZNkUwmI2IyCGltbY3W1ta4/PLLo6urK44ePRrHjh2bsf7twMBADAwMxHPPPRdNTU2xevXqWL169Wk/IE5MTJwS7ra3t0dExKZNm6KiYv5vVTOZTPT29kZPT0/h31LqIBTzta99LZYtW1b4mm3JgLn6Mz2YHhkZiZGREev7AnBBaGtri7a2tjnbnThxIh588MGImDyBfO2115a76yyQ7du3l9Tu3nvvLQRJmzZtirVr15a768A8CBGAi0pDQ0OsWLFiznZ144cinl/BqK6urqR9zqe1wYGLW2VlZVxxxRWxefPm+MY3vhERk9Peb7311kgkErPul0gkCifUd+zYEZ2dnXH06NE4fvz4jBP1fX190dfXFzt37oyWlpZCoFBVVVVo09vbGw8//HBh7d8p4+Pj8dxzz8WBAwfiuuuui6amptOOZWhoKHp6egqhQbEaFGcqm83G8ePH4/jx44XnaGr8bW1tM8ZTzMGDB2Pnzp0zwpahoaG48847Y/369fGiF73ojIISAACAc8mnFuCismHDhpIKM90THRHtk7fXrFkTL3nJS8rd9QvaNw8+Hd86+MyiHPuy1rXxvRvO72WmhibG4oMPfrGktntO7CncfvSekZL2eedLXhf1ldVlHeMHH/hiDGXG5mx38PDBGB+bXE7ngYf6Il1Cwbkfu/zlsbl57iDwYjR9qZ5kMnnaAOGFkslkrFixIlasWBHZbDZOnDhRCBSmz26YOsH/9NNPR1tbW6Eg8wMPPHDaGQKjo6PxwAMPxM0331yYyj51Vf/0WQbTT9DPprq6OhoaGuLEiRMljy+dTp9y7NHR0Th06FAcOnQoIibD47a2tli2bFm0trbOeD6fffbZ2LNnz6zHP3jwYPT19cVLX/pSQQIAALCk+cQCQNndc+jZ+MADpZ0kn6/Xbb3uvA8RhifGzuz5eaC0YObnr76t7CHCXz36/6JrZJ5XkHeXNr6b124XIiyyVCoVq1atilWrVkUmk4mOjo44evRonDhxYkag0NXVFV1dXSUfd2JiIh555JFoaWmJnp6eGBgYiHw+f9p9kslkNDY2RktLS7S0tERzc3NUV0/+fO/cuTP27t075+M2NTXFy172shgbG4sTJ04U+j29HkRERH9/f/T398e+ffsimUxGS0tLtLW1RSqVOm2AMH3/p59+Wj0dAABgSRMiAACwYCoqKmLNmjWxZs2amJiYiOPHj8fRo0ejs7NzzgCgmKkT9bOprq6O5ubmQmDQ1NRUqOfwQtu3b4/x8fHCTIJiGhoa4rrrrotkMhk1NTWFGWz5fD76+/ujq6srOjs7o7u7e0ZAksvl5h2SREQcOnQotm3bpkYCwBxOnDgRR44cWZRj19fXx5YtW8o6voGBgZKC7jNRUVERO3bsKOv4ADi/CREAKLtXbbwyGivnPoH20NE98X93PxQRES9dsy1eu2XugmyXtKws9/DOWn1ldbz35W8uqe3vfuufIx8RiYj4vRL3aSjzLISIiN982RtjZGLu5Yw+dN8XomtiOCIi3nbVbbG2ae5CfltbVi9Kn7O5XHz3P/7PktoODJ68gv6PPn1vScsG/d3rfzk2NZ3ZDIpnOg/F27/813O2y+dzMTA4GBERyUQy6g99fc59KlMV8dW3/I+S+vHnj9wR/3fXQ88/Vj4mMhMxMT4e2Wkn3+cjERHJZCpSqVS8bvM18es3vaHoyffb//kPYmRifNbjZDITMTY2HtlctnBfMpGIysrKSHdXRmL/V2fd9y9f/fNxwyU3RDabjX949M74q8e/GplMZsax5qvyU3dFOl0REYlIJCa/rlqxMf7yNT9/xseE03ndZ/4wBsdH52w3NDRY+H2t/9d7Zg3opvvzV/9sXL1iU7mHeF760c9/II4O9szZbmh4KLLZydecuhN3z1hKbTZ/8Mq3xOW15/esvH9++u74+NN3ldy+IpKRidL+3lzRvDb+fsuvl3V89x58Nv7bPf+wKMduqqiOu3b80aIc+z/2PBJ/fO/n52yXyWRieGTyPVzF4Yqo3fl/59zn0tbV8bHXvuOM+/ahB78Un3v2/jnbjY+PxejzNZqqjn4jqu6vmnOf79t6Xfz6S3+gpH68+tO/H2OZuZdfHBwcjFz++dfcfyntNfevbv+FuHzZ+lPu/9bBZ+K3v/FPc+6fzWZjaHgoIiJSh1JRt+eOOfdZXd8Sn37Duwrfv/2Oj8YzXYfn3G/6a1fN8W9FOp2ec5/feOkPxOu2XlfS8wwsLiECAGV3/Zqtcf2arXO2++ST3yiECFeu2Bi/dN3tJR1/eHi43EM8KzUVlSWP9fe+9ZnIPh8jlLrPUvAzV91aUru/ffhrhRDhhy+9MV68bu6fm8WSj3w81Xlw/juO9ZXWrIQPm7MZyYyfWd9G5z55VZkq/e3j4YHuM+tHCQYTmVmv3n+m81AMlRBKnTr+uZsMTUw2SqVSMV4RsXug4+wHU6SrmeHRuOOOO6KiouK0X6lUas42FRUVJZ2I4OLxdOehGBgvrW5OQYmvXUPjZ/C7R0REPNd9JA70d85vpxL/X/rHRiJqyz3Cs9M9NhQHSxzvm5ddGZfWLIv3H/pWjOYzc7ZvG60v9/BiaGK05PHNV3928X4ve0eH5v+3fiwihuaeuZdKnN3friODPWfWtxJW2Lxm5eaSD/n0iYMxmp3n+7oSfxaGZ7loon9s5Mzegw13z9mkf3zmZ6s9vcfP4HkubXzdo4PzHwOwKIQIAACwBOXz+ZiYmCipePRcksnkKeFDOp0uKYSYajv1/XwKcAMslPr60k70v3nZlfG61ssiIuLX1728pCChtrb8Ccti9iFd4dQPAGfHXxIAgDNQkUzFfT/1v+Zst7PrcPz0F/+y8P3v3fLD8ZoSluLa0LjsjPt2+bL1JfXtSO+JeNO//1lERCxL18YXf+x35twnOY8TyO+56U2nzIjJZDJxzz33zKiP8LXe3fHV3t0REfGa5m3x3c0n16VOJBJx8003ReoFJ0CaqmY/2XLnj/9e5Eqov/AD//SHcXx88nLDf/6+d8bGtlVz7rOu4eQSWj9y2U1x68YrZmzPZbNx9z33zKiX8J2h4/H3Jx6NiIgX162Otyy/esY+jQ0NkaqoiGwmE9lsNjLZbKRyk2M/kzoSxeRyuRgfHz+lOPSZSKVS8wohThdOCCTK4+s/9j8jm597mZc3fPoP49jY5O/IP772V+KS5Wvm3GdtQ2u5h3fe+rcf/I2YmGVptPHx8Xjuueeiq6sr/uro/dE+Njlz7FdWvywuaVgeW7dujeXLl8967NX1LTHaf35f0fuz194W3/+iG0/b5sT+w9Fz5Hjh+201y+KT1/xErLt8ayRPs+xTXXru5WsW28s37Sjpb/dTR9rj57760YiI2FDTEp/5kd+Yc5+KRZyN9vqtL4kb1mybs93Xnn0kfvu+f4mIiKub1sZH3/DLc+5TnZp7uZvT+fUbvz9+4cW3zdnuL7717/GpvfdFRMQPbbwu3v1dPzTnPqUsxTrlmz/5+yW9L3n9P/5BnJiYXFroX17/rljfOvcSZNPfl0z3XRtL+3l65MBz8Ut3/p+IiNhatyz+4Yf+65z7pJMzf5f+z+v+S0kzLd7+b38Zj/VPLnv0u9e9Ib73ihvn3GdlbdOcbYBzQ4gAAHCGtrac/qTz4x3t8c6vfmLGfX9wz+diS+vquP2SaxatX9UV6Tn7FhFRMe0cYiqRKmmf+Vhe2xjLaxtPuT+z5fI4cOBA4fuG1MmTN42pqlhd2VD4fuPGjbF9+bp5Pe4lzaXVQqmYtkzChsZl8x5/U3VtNFWfGmYMb9g2o/jn4bGThaFrkukZ40un03HrK2+NilmuEs1ms5HJZM76a2oN4oWQzWYjm80uWCBxpiHECwMJSre5ubS18dOJk8/r2rqWBX+NYKaNTcVDgLGxsbjnsXuicmQ8Vlc2ROW0E3ht6dpoi6ro2X0wVlc2xKZNm2Y9/mic3yFCc3VdNFfXzbp9586dMwKEwrgHh6Lzuf1xww03zPpauxTUpqtK+h3rGzj5N6VyEf52z1dDVU00VM19Qv07004G16RKe59ytpbVNsayIu9DXqh52oUJTZW1C963Ut+XTF++aWPjsrjkLPpRV+LP07Huk0uoVSYrzmjs6xrnrlEWMTMUWlHbWPafXWB+lu5fUC4I+/tOzCg0uLvnWGxvW1vubgHAonu8oz3e9Nn3R9/YzHVjJ3LZ+Jkvfjj+z/f9l0UNEpay7du3R1dXVwwNDZ22XX19fWzfvr3c3Z23Sy+9NDo6OiKTmXsd7u3bt5/2pNbUVf9VVWd3lWw+nz+rQCKbzcbExERkMpkZsyzO1lQgMTZ29ut1L0QQIZCY/FnZv39/7N27d8b/9cMPPxz5nqG49NJLo7KystzdvKg89thjMTIydw2Lp59+OlpbW6Oxce6TpheaZ555Jvbt2zfr9t7e3njggQeWfJAAAEuVv54sikP9XfHf7vyH+Mq+x2bcf/Pf/3a8csOOeP93/2TJaTwAnG9mCxCmXOxBQjqdjpe+9KXx2GOPRVdX8cKKbW1tcc0115yXJ3tqa2vjuuuui4cffvi0QcKWLVtiw4YN56RPiUSicLL8bOXz+bMKIaaHGQsZSEwdcyGfq/nUi5gKIaYv83S+FbTOZrPxyCOPxIkTJ07Zlsvl4sCBA9HR0RE33HBDyevTc3Z6enpmfZ0sZvfu3XHttXMvmXchmStAmCJIAIAz5y8nC+657qPx/f/yv6JzZKDo9m8ceCq+55/eG5/7wV+Pq1dsKnd3AWBBzRUgTLnYg4Sqqqq48cYb48SJE3HHnXsK91dXV8f1119/2rW9zwdtbW1xyy23xHPPPReJ5w7P2NbU1BTbtm2LFStKW1JmqUkkEpFOpyOdPru1qiMmT0zPFTpMDx4mJiZmnVGxUPUjFrOgdSkhxGxf56J+xNNPP100QJhudHQ0HnroobjllluciJ0mn89HLpc75d8X3p5t22ztOzo65tWPjo6OyOfzF029kVIDhCmCBAA4M/5qsqAmspn4qS/8+awBwpS+seF46xf+Iu596x9G7RIoYgUAC6HUAGHKxR4kREQsX758xhXN9fX1532AMKW2tjZe/OIXx6PDRyOeL5FQWVkZN998c7m7tmQkk8morKxckOVxTrdcUykhxPSvhbLQBa1nW4JpPkWuZytoPTAwEAcPHiypL8PDw9He3h5bt25dsOeq2HM324n1s7ldyon7+bZfyBk1C/G87d27N9ra2qKhoeGCXp5rvgHCFEECAMyfv5gsqM88c0/s6jlWUtvDA93xySe/EW+/9tXl7jYAnLX5BghTBAkXvulL2lwsVweXw0LVj4iIWUOIpVDQeiHrR0wPIUpZc3+69vb2RTvRv1CzSi5Wzz77bOF2fX19NDY2Fuok1NXVnelhl5QzDRCmCBIAYH78tWRBfWnPI/Nq//ff+UbsWL4+aioqJ7/SlSdvV1RGVcXZT5MHgMV2pgHCFEECLC0LXT/ibIpaL9X6EePj47F79+4F69P5LJFIRCKRiGQyGclkcsbt6d/P1ma29slkMnp6euZcYup0BgcHY3BwMI4cOVK4r7q6uhAsNDU1RWNjY9TU1JT7aSzZ2QYIUwQJAFA6fylZUPt6j8+r/bPdR+KNn/2T07ZJJ1PRWFUb1RXpqK2ojJqKqqiuSEdNumry+3RlVD8fOtRWPH87Pe32C8OJdGXUpKbdl66Kmop0JBPnV+E7AJaGsw0QpggS4MKzkPUjzqSg9WyzI5bS8jvFlHrCfSncXuzi2SMjI3HXXXeVPDtj2bJlUV1dHX19fTE4OFh0v9HR0RgdHZ1Rb6GiomJGqNDY2Bh1dXVLrjj4QgUIUwQJdHR0RH9/f+H7wcHBOH78eKxcufKc9WFiYiIOHTo04/d1/4EDsbK2aVFnD/X398eRIyfrNmWz2di/f3+sW7fugl4KDTgz/kqyoFLJhf9DM5HLRtccNRYWQjKRiIbKmhlhQ/XzwURNsXAiXVk01KiuSEdNRVXUpk/enh5cVKb82i11d+x5NP5t5/2F77+w66G4vG1dvG7rdZahAGZYqABhiiABmM1iF7Tes2fPvK54T6VSsXXr1jmvpC92e672nFRTUxNbtmwpadZHdXV1XHPNNYWfkVwuF4ODg9Hf3x99fX3R3d0dw8PDRZfZymQy0d3dHd3d3YX7kslkNDQ0RGNjYzQ0NERTU1M0NDSU7WT7QgcIUwQJF6exsbF49NFHo7u7e8YycePjY/Hwww9HS0tLXHPNNVFdXb2o/Th+/Hg88cQTMTExMeP+w4cOxTc7emLr1q2xdevWBX1tzOVysXPnzmhvb48Toz2F+/P5fDz11FOxe/fuuOaaa6K1tXVRxw6cX/yFZEFtb10TO7sOl9x+eW1jXL1iU4xmxmM4Mx4jE+MxkhmL0cxEDGfGYmRiPCZyC7eW7Onk8vnoGxtesBNBp1OXrpo2Q2JyJkQhbJi6XVEZVamKqKusLh5iVFRGbXpawPH89ydvK1g9X8eH+uJnv/ThuP/Irhn3P9N1OH76Sx+Oa1ddEn/7uv8Saxq8mQIWPkCYIkgAFluxgtbZbHZeIcLatWtjy5Yt5R7KRWHbtm0xOjoahw4dmrVNdXV1XH/99TNCpmQyWZhVsG7duujq6op8Ph+1tbXR19cX/f39ha9itTZyuVz09fVFX1/fjPvr6uoKx536WohaJKezWAHCFEHCuVXuqicTExNx3333xdDQ0Kxtenp64r777oubb755QcLbYjo6OuLhhx+e/XnK52PXrl2RzWbjsssuW7DHffLJJ+Pw4dnP24yNjcX9998fL3vZy6K5uXlRxl72HwJg3vx1ZEG9afuN8e+7Hiy5/f+4+YfjLTtumbPdWGYi+sdHYvT5oGE4Mz55OzMewxMnb09+PxlCjDwfQoxkJ2JkYuz57SdvTw8upm6fK0MTYzE0MRYxv/p185ZOpqIuXT3Hck4z61BMDzcKS0iln59t8Xw4UZht8XzbikWYgXKu9Y8Nxw/86x/F7tMUBn/k2N54/b/8UXz1Lf8jWmvqy93li87wxFg82bG/8P3unmPRPTJ4Ufxf5PP5GBiYe0bWru6ZP78Ptz8b21pXz7lffX39kluuoJj2vo7ITAuWn+k8HFeuuSRSZej7YgUIUwQJwLm2fPnyaG5ujt7e3jnbJpNJAcI5lEgk4qqrrorly5efMiMhlUzFxo0bY+vWrSWdyE8kElFbWxu1tbWxevXJ9whjY2MzQoX+/v5ZT7AODQ3F0NBQHD16tHBfVVXVKcFCbW3tglw9vdgBwhRBwuIYHx+PPXv2xJ5pP7vjY+PxwAMPxNatW8tytftzzz132gBhyvDwcOzcuTOuvPLKBe9DJpOJJ554oqS2e/fujZUrV0ZLS8tZP+6xY8dOGyBMyefz8dhjj8UrXvGKs/6c0N3dHbt3754x22L//v3xTN0zsWXLlhmBNrB0+cvIgnrtlmvjZWsvjXsPPzdn2yuXb4gfedFNJR23qiIdy89BkeVMLhsD4yOTMyEKYcPJWRFT4cT04KIQaEwU/35k2gyLkcxEjGTGZ5wEW0wTuWz0jg1F79jcb5DORjKRiNrCck6VLwgbnq87kUrPnHkxNRvjBXUrCktIpU+9XZ1KL9oU9z+4+7OnDRCm7O8/Eb/7rX+Ov3j1zy3qc8pM/+eJO+N9d392xs/ynfu/E1f+zbviHdfeHr/5sjdcEGHWbMbHx+Pb3/72vPc7/vSeOB575mz3qle9atGvIDwbncP98Rt3/kP8310Pxup0Q+H+9377M/Hnj385/uS73xovX/+ic9afxQ4QpggSgHPtmmuuiXvvvTdGR0dnbZNIJOLqq68+rwrxXihWr14dq1evjopnv1S4b8eOHbFj246zPnZVVVUsX748li9fXrgvm82eEiwMDAwUrakxNjYWJ06cmDGbJZVKnRIs1NfXz2ut9XMVIEwRJCys/v7+eOihh2J0dDSy2Zk/N52dndHZ2RmXXnppbN269Zz1KZPJxIEDB0puf+jQobjssssWfDbC4cOHY3y89AsZ9+3btyAhwnx+n4aHh6OjoyNWrVp1xo+3e/fueO65U88P5XK52LdvXxw9ejRe8pKXRGNj41mPDVhc/iqyoBKJRHz8tb8Ub/rc+0+7rNHmphXx96///8py9ejpVCRT0VK9+Fc15/P5GJwYLQQNM5dzKj5LYqQww2I8hp9f8mlmODE2c5bGxHiMZTORPwfzBHP5fAyOj8bg+OjZH2wONS+oOzHrck7TloCqqZhl1sXzoUYun4tPfucbJffhn5+5O37v5W++KK6AXwp+6xv/GB999KtFt41lM/HBB78YT3S0xz/+wK9e0EHCxerYUG+89p/fFwf6O4tu39VzLH7oc38a//v2X4w3br9x0ftzrgKEKYIE4FyqqamJm2++OZ588skZRXen1NXVxRVXXBFtbW3l7urFbdpFNYtZQiKVSkVLS8uME5f5fL5QZ2H61wvXc4+YDCF6enqip+fkmuuJRCLq6+tPCReKnaA91wHClKUcJPT19c04AZ7N5WLv3r2xfv36RVty50yNj48XAoTTee6556KqqirWr1+/oI+fyWRiYmLilK+enp6SC5VHTP7M33nnnQteaLjY78zpHDt2LP7zP//zrB4zn8/PK7iImAxRamtro6qqKiorK+d1Ud+hQ4eKBgjTjY6OxoMPPhi33HLLkr6oCRAisAhW1DXFHW/+rfjT+/5v/J8n74zhiZPra1al0vETV7wi3vOyN0ZzdV25u1o2ieeLODdULv4VXCOFQGEihibGigYPhdkWpwQX025PCyemLyc1FXxk87mz72yp48mMR08s7uyK08nl8/H6f/lfsbKuOSpTFZFOpiKdqojK5/8tfP/8fRXJ1GS7VEVUJiuiIpWKymRFVKZSkU5WRPr5fyfbpCKdnNw+dYyKwvepFzxexZIL4hba5597YNYAYbqv7/9OvP++f4/33PSmcnd5USSTyWhqajrl/lw+H093HozxaQUS89Nu7Rk5WRyxMpWKy5etj2SRN/5LuYjl2+/46KwBwpRsPhe//P8+Htes2hybmlYsWl/OdYAwZSpI+Nvv++V4zSUvXrDjPnXiYHzikZO/X9lcNt5/37/HT135XbGiruksjly6XD4Xn3v2/ni68+Q630+eOBife/b+eMOl10cysTivcZ3D/fF3T941Y2bgRx7+cvzsda+OK1dsWNDHunP/d+Jr+08uV9A5MhAff/w/48d2vDxqKkzfZ+mpqqqKzTu2x1dH9kdm2vu7B+JEXHPZDQKEMuofG46/e/IbMz5f/eNT34rq2tq4fs25uZI7kUhEQ0NDNDQ0xNq1awv3j4yMnBIsjIycunbr1BKNAwMDM5ZUqampmREqdHR0xMGDB8v2XC+1ICGXy8UzzzwTdz77aHy792SwMp7LxPvv+Xy8uu3SuPm6G2LFisV7HzRfu3fvLgQITw93xP0DJ/8/h7Lj8ZWeXfFdTZujKlkRO3funJxl84LnOpfLFQ0Cpn+Nj49HJpOJ8fHxGfeXEhTk8vl4YPBQtE8rLnxwrC/u6d8fL23YUHjfPFWAfrH0Z0bjP/v2zvhM/aWeZ+OVTZtjQ1Vz4b5itUvOxq6Rrri7v/3k8XOZ+FL3s3Fr0yVRm5oMpTo6OgqhciKRiKqqqqiqqorq6upZ/02n05HNZuOZZ54pHPcbfftiMHuy/w8OHIqKRDIur10RY2NjsXv37tix4+xnVQGLJ5GfTwTLae3cuTN+4Ad+ID760Y/Gd33Xd5W7O0vCWGYiLvmrX4qx7OQf3PZf+kjUn4MT55x7mVw2hifGTrOc08zvXxhODE+1mRibEWrMXFpqvPCzxEmVzwcK6efDiamQYjKEeD6omBZkFAs9JsOJyfYVz/87dYxZ2xdCkucfZ6ofhRClSEiSTM3rhPVL/+49JS0zFRFRXVEZz/zCB6Oh6tTXmOHh4ejr64sVK1Ys+FVE5fSxx74W77nrUyW3f98r3xJvu+bV5e52yb518Jl442f/ZMZ9q9MNcXRisj7E8oraOJE5eUL/J3a8Ij74PT+zKH0pV4AwXTqZWpAgIZfPxXu//S/x4Ye/Eg3JyujPTX6ga6uoja7McNSmq+IDr/rp+MHLXrqo4znY3xlv/cJfxJMnDsRlNcti58hkWLS9Znk8O3Iirl6xMf7u+34l1jUu7AnLf3/uwfj/vvrxGJoYi+UVdXEiMxlKN6eqozc7Gm+/5tXxey9/81mHtP1jw/GOL38svrLvsbikujX2jk6Gelur22L3aFesbWiNv/2+X45rVm5e1OcZ5usLux6KX/l/H4/BidFYka6LjonJ35HWiprozozEL774e+K9r3iz2X/n2Nfbn4y3ffmj0TM6FOsrm+Lg+GSx4zXphjgyMRBvufyW+NPvfmtUnWYJ2K6uroiIcxYETUxMnBIsDA4Ozusq8HJrbm5eEkHCw48+En/82Jfi6317Y0VFXXQ8/7drTWVDHBkfiJpkOt6++sZ4260/EMuWLSv30xb5fD6+9rWvxeDYSHzs2IPxwOCh2FjVHPvHeiMi4pKq1tg71h1Nqer4lTUvi0trlkVjY2NUVFTMCAKy2cVbBrhrYjj+/Mg9sW+sZ8b7kMtqlsfOkROxoao53rnmplieXtyLH+/rPxAfP/5QjOWzsayiNjqff2/blKqO/uxo3N5yabx52VVFLwQ6UxO5bPxdxyPxzf72Ge+tp15b6pKV8Y7VN8ZVdWe2jFEikSj8X+4a6Yy/OHJv9GZHC//vERGbqlqifawnXlK/Nn5x1Q1RX1kdt91223lRp20xXHnllfFzP/dz8au/+qvl7grMqvyROhe0qor0jKsIXXF34apIpqKxqjYaq2oX9XHy+fyMoGLmrIipEGPi+WBi5oyL6UHG9ODi+FBvtPedOPvOlcl4NhPj2UzE/GbElk0qkSzMuqhMVhRmakwFGZMhRCqyuVzJAUJExGhmPH7qi38RGxqXn7Itk528OqmmumZJX3U/X19rL60Y25Q/f/A/4pnOuQuplcOKXFUkY+b/zd6eY/HShplT2w+O9RVub69ZEVvi5IfLw0cOx91PP1aou1KdSkdVRTra2trOanr0UggQIiY/8P30F//yrIOE99z1j/Hxx2efDj88MRZv+/JHoyKZih+49PpFGUvncH+8/l/+KA4NdM3a5vGO/fH9//pH8bW3/O6CLR/3pd0Px8/9x0dO2+Z/P/r/YiQzHv+/V/3UGT/OeDYTb/n3D8b9R3bN2ubwQHe84V//OL7yo78Tl7WtncfRYfHcsefR+Jkvffi0bf76sa/G8MTYooW2nOrbB3fGj/3fD0YmN/vM3396+tsxOD4an3jdLy2Z9zrp9OTf4OmhRTabLboc0mKeLD4bS2FGwvHjx+P3Hvn3uGdg9nX8R3IT8cHD346au6vind/3ljO+aCafz0c2my1ceT/99gu/f+G26fdNTEzE2Ph4fPDIPfHU8PFZH68vOxp/fOgb8dvrvzsWOlJPp9NFv5LJZDy559n4w4N3FS4kKObAWG+87+Cd8d4Nt8Xrbv2eqK6uXtD+tbe3x6ceuTM+cuz+2f8/IuKOnudiPJeNX7v8e+Laa68968e955574o92fS0eHDw0a5uh3Hj82eFvx39b94q4YeWWSCaTMTo6+v9v777jo6jzP46/N70XSiiBhJoAQYr0KhGwIKCCHli4U1Dkd3iKiIcKdypW7KJiw3oq3Cn2UxA4aQKCCAJipEoJoEAghdTdnd8fuEOWbJJNsptd4PV8PHgwu/P9zny+M7Ob2fnMfL9uPwlhGIZKSkr0a+Exzdy/XMVG+Z/v7/MylZ/5re5q0k8FBQWKjDx3e6wA/B1JBABnFIvFosjgUEUGh0oeeqjlaEGu2r56u+xVuCvq/eG3KzYsUsU2q6x2m4ptVpXYrCq221Rit52ctllVYreWem1Tsd0q6x//O8pb//jfsYwSu00l9pPlHa/N8jabrI55dsc6/PNHV3lshv2PZI7nl71838+SfvZ1E/3Wb/nZeven5b4Ow6UXWgxXTJDzhf4OMbHSaWOs/WPPqe53htZNVeMQ5wLZvx5Q9mnLnv37Wh205ysmNFwxoSe7kosJCVd0aMTJ90IiFB0SdjIRGhL+R7mT7+3NPqLrP5/l8wSCQ00TCav2/1JhAqG0yUve0gVJ7bzS/eB9K/5TYQLBYW/OEd2/8j+aNXhsjdeZW1SgOxa/5VbZtzcv1fDW3XRBUrtqrevVjYsqTCA4nCgp0m2L3tDXo/9R4/YBNZVbXKBJi99wq+y7Py3X5SndlJ7c3tdhn/WKbVb9bdGcChMIDp/v+F6fbFtbK2MEVVdgYKBiY2Odumk0DEP5+fn6+eefXY7F4Wu+TiTM+2FphQkEB0PS7L3fatAvndSgbv1qJQM8mcz5JntXhQkEhxLDrtcOrdNDyYPL3G0fGBhYbjIgODhYISEhLt8PCgqqMJn2yKb/VphAcMiyFujj3G0aHe35LlNjG9TTm7+td6vskuydGhMx0CPjXmwLzqswgeBgl6E5v32v/xs2WhGhJxMojjEVHAmF8v4vKiqSYZysX1ECwWFrwe/63/FdusDW3+PbGYDnkEQAcM6rGx6ty1t308fb1rpV/tIWnXWRB/sl9wSb3a4iW4lK/kgulPyRlDCTHHarSmw2M+lwMoFhPVW+VGLDUdfxv3OSxFHf5maSxLEO5yRJbY2hAUhSbnGh9uSfuU8bna4miYRXNlY+xohDdlG+/vPzKo3vPNij8WcV5OmDjFVul//31m/1YL/Rig2r2ZNuH2SsVlZhntvlX92wqNpJBHfGcnH44dAufX9wp7o2almj9gE1NT9jjY4WuP8ZeWXDIpIIteCrnRu0L6fypKvDKxsX+XUSwRWLxaK9e/f6ZQLB4fjx4/r6668VGFi1rjlryjAMfbjXvQvNknTcVqh5Py5X39hmPthKzhYeqzyZ7rC/OFuHI+y6olPfMk8NeFpOUb4W/5bhdvlvjmzT0YJc1Q2P9mgcn+5arxN29wc5/s+v6zT0vF41Xu/8vRvcLnu45IQW7dlsPplaekyEihiGobmrF2nv9uNur2vh8e2a6eGnPQB4FkkEAJA0o/9ordyfocP5ORWWqxMWpUcGXOvrcMsIDAhQRECoVPObU2qFYRinEhqlnroonYQ4mHdM13z6bJWWO6nbZS67BSkuLlZ+fr5iYmK88mPEVz37vrphkTb8ttvt8h0TmmnC+f45JkLIkXyVnPY00L7sI9p32t3qebZTP7bW5e5XXNCpR5KCLAGqGx59Kmn2x//HrAU625TYbbrus+cU+sfg6+46UVK1Afn+sXyeHl4136OxW+22Kj35ZTPsavfa7TXuf73QWrU+3xbu3qjkFydUeT12w1CB1f2LApI07INHFRLIaTl8q6qfkcW/bqrWZwRVU2Sr2n75/uBOJb1wi8sL3Y6xCPyluyOH8yMb6+aErr4Owy3Ls3bprd9/qLX1RViClWMvrFKdb3P26O3f3b9QXIZFspTzpnvvW2So6n8LH926QPdv/a8Ht55rVrvNadD4ytgNQx3mTPb4ODBV/c79cueGGn/nOroGropbFrys2xa9XuV1hRpV+831e0mejhafUKMQusAG/BW/VgBAUqOoeH0y8u+67rPnyh0foUl0Xb07/DY1jfH9YGVnOovFotCgYIVWkPVoXz9J/Zq01Yr97nVP1CgqXlN7XqFgFxfiztaBlSOCQvSXL15wu/zt3YZoeGvv9HHvDduyDqrPO9NklJOmmX/0J6fXd/W4XJN7XVGm3D9LipRTXKCcogLlFuWfmi4uUE6p1znF+cotLlR24Ql9d2D7GdFVWJHN6tUB522GvcqJhzOxneWprbaX/NEVHnCm8YfvB5RV1YuEvrby+G61Ca2nfn5w93xF9hdl6/0jP6rIqL2/R9VZV66tqGYx+ujumKwS/+g60hVfnYeczhffuVa7XVZ71ddbeWdRZR3Jz1WjqPhabyMA95BEAIA/pNZN1MoxD+vtzd/opR++NvvrbhQVrwmdL9KNHdIVEVz9wVlRdff3/5Munfewiu2Vn7TP6DfKZQLhbDak5fnq0yRV3+7/pdKyvRJTNKzVmXGXn0NKnUYa2yFdr2/6X6Vlm0TX1V/Pv9jlvIjgUEUEh6phZJzb616TuU2jPnmaC2QAAHiZIWnOb+skyW8TCfuLsvXo/mXKtdX+eYFFVbuuX+RGH/SAP6oTzqDKgD87t662AEAlwoKCdUvni1Rks2rGyg8kSWPa99fELpf4OrRzUseEZpp9yc3664LXKkwk3NtrxBnX/68nWCwWvT7kr7r646e0+XD5A+61r99Ub1w20e+6L3DHQxdco8y8LC3YtbHcMg0i4zT38kmKDvXQaOuSeiam6N9XTPbbREKAxaIXL7pZI6p43I/5fJa+3v2j2+Xv6/uncpMz1fV7frY6zLmz3CdMXLV1y03P1Lgv4tc2Ltb05XPdLp+e3F7zLr+jyusxDEMdXr9Tv+dnu13n05FT1TMxpUbtA2rqjR//p3uWved2+QuS0vSfKyb7Ouyz3vxf1uivC19zu3zHhGblDtZ+9OjJG2Tq1q3r62a5ZBiGtmzerAMHDvg6FCeRUVG6Pj1d40JH+WT9l7w/QxuO7HG7/F19rtSYDgN8Emtp3d+aqj05R9wuP3f4JF3Y7Dyvx3WsME/tXptUpa4VN497WgmRsW6Xd8fLG77WfSv+7Xb5wc066N3ht9d4vRfNe1A//v6r2+VnX3yzRqb2rPJ6lu79SaM+edrt8k1j6iox2j+/mwCc5PdJhCNHjmjNmjU6cOCAGjdurPbt2ys5OblGF0LWrVunbdu2qbi4WGlpaUpLS1NkJBlPAPBHV6R0V6v4hpqx8gN9s2eL02XHzg2a697eI87pgR3rRcTov3+6V8+t+6/e2PQ/HSs89fBwfFikbuxwoSZ1u+yMfYomODBI7wz7m97atFSzvv/SfEJIkkIDg/Wntr10T68RHv9hJ/lvIiHAYtFLF4/XyDZV/0F3c6dBbicRIoNDdU27Pgr08DgijaLiNbx1V326fZ1b5a9M6eGR/TuqXW/NXPOJcovdGyPj5k6Dqt32cR0v1KOrP3arbPv6TdW7SeoZmeTD2eXqtr302JqPlV3kXpciN3Uc6PHvB5Q1rHVXzVj5gQ6dOO5W+Zs7lb9fHO/7837r2LGjLBaLMjMzfR2KJCkqKko9evSodCBZb/q/bpdq/Fcvu1U2PjRCI9v18ot9fFOnQfrH8nlulW0V31DpzdrXStz1ImI0MrWnPshY7Vb54a27qVG057vYGd2uj55Y86nyStwb8+KmGpyXlHZzp4G69Wv3xjhoGBmnYa27Vmu9A5LS1LpOI23POuhW+XEdB9a4bQC8y/d/WSrw4YcfavTo0Zo5c6b+9a9/aebMmRozZozuvvtuFRVV/cf8kSNHdMstt2jy5Ml6+eWX9cYbb+jOO+/UFVdcobVr1/q6uQCAcrSvn6T/XHmnZvQfbb53XVo/Lbrmn+d0AsEhIjhU9/QeoYzxsxRoOfmnPcBiUcb4Wbq394gzNoHgEGAJ0NiOF2rjuCeVWqex+f6C0dP1zKAbvZJAcHAkEiL9ZBvWJIEgnby7fnS7Pm6VfXTAdaoXEeOVdjx0wTVKiKh8vzWMjNMD/T1z52d8WJRmpl/vVtmrUnvqouYdq72uv3a5RB0TkistFxoYrGcHjSWBAL8QFxapx9PHuFV2RGoPXdqys69DPieEB4XomUE3yKLKvycGNjtPf2rb29ch14jFYlGHDh2UmJjo61D8IoEgSVemdNeQFu593p4dPFbRIZ57MrMmxnUcqO6NWlVaLjggUM8NHuvxgYsrcn+/UW51c1k/IkYPlfr94Ul1w6P1WPp1bpUd1baPBnroKY0/te3t1rIssuiZQTcoPKh6Ax0HBgRo1qCxCnZjv3Zt1FLjOw32SPsAeI/fJhEWLlyo5557TsXFxbr22mv15JNP6o477lDjxo21atUqTZkyRTab+339FRUVacqUKdq6datat26te++9VzNnztTQoUNVWFioqVOnas2aNb5uNgCgApHBYeb0mX5h3BsCAwKcLkb6w11onhYaGOxy2pv8JZFQ0wSCwzMDb9CY9v3LnR8cEKgnL/yzrk3r57W2NIqK1ydXTVXLuAbllmkd31CfXDW1SmNZVOZPbXvr2UE3KiSg/Idxr2nXV7MuGlej9YQHhejfV0xW78TUcsvUDY/Sv6+YrE4NmnmsfUBNjWzTU7MGj1VoBWMMjWrbR88PrtlnBFUzuHlHvX7Z/1V47nNZqy5647KJCrCc+X/7/SGR4C8JBMf2eGXIhAq7MAwLDNbLl4zXZa26+DpcU0hgkN6/fJIGJKWVWyY2NELvDb9dPRq3rtXYGkTG6tOrpqp1nUbllmkR10CfjpyqxtF1vBbH6HZ99dTAv1R4XnJ9Wn89M+gGj60zwBKgNy6bWOGxEhEcqtcv+z8NrsENFZLUrXErzb18kuJCI8ot079pO829fJJCzrGx7YAzkV9+SouLizV79mxJ0pQpUzR8+HBzXv/+/TVhwgRt3LhRq1evVt++fd1a5meffaadO3eqadOmeuGFFxQRcfJLrHfv3mrUqJFee+01vfrqq+rZs2Y/zAEAwNnH110beSqBIJ3sIuqZQTdqdLu+emP9Is3feapboQmdL9L4ToOVFFvP621KqdNIK8Y8pHe3LNdHG5ZLBSf7TY4PjdQTvS7VdWn9vPKD8vr2/TUgKU2vblykDzd/a75/YXJ73XD+IPVuklqDpZ9SLyJGn141VZ9uX6f3v1+iXYVZkqSQgEBN6z1C4zoOVEwFP6oBX7k2rZ8uSGqnVzcs1oebV5rvX9CknW7oOkh9mrTxdYjnpOGtu6ln4xS9unGxPtl06rurc8PmeqbLII/dpewvHIkESbXetZE/JRAcwoNC9OqlE3RdWn+9vX6xPtuzQZIUIItu6zpEN3capEZRnu9yp6biwiL14Ygp+u+OH/Te+iXac/C4JCkoMFBTe16hmzoNVHxYlE9iaxnfUMuvm6H3flqh+aXOQ2JDIzSz5/W6Pq2/QoO8f8PKX84boPSk9nrtx8X6oNR37qDk83Rjl0FeGTMpMjhUbw+9VUt+3ax/rV+iL/ad7Ooy0BKgSd2GanynQR570ndAcnt9f+PjmvPjEn288VT7WsU10APdrvOrxBeAivnlbQpLly5VVlaWoqOjdemllzrNq1evnoYNGyZJ+uijj9xe5vz58yVJw4YNMxMIDqNGjVJ4eLi2b9+uLVu2+Lr5AADAD/nqiQRPJhBK69G4te7pPcJ8HRQQqIcuuKZWEggOIYFBGtvxQp1Xquufzg2a6cYO6V69I61JTF3N6D/a6RH7u3pe7rEEgoPFYtEVKd11WYvzzfcSImJ1R/dhJBDg1xKj6+qB/qOcPoeTew4ngeBjCZGxmt5npCJCTv0duqnjwLMugeDgiycS/DGBUNoFSe00scsl5uuwoBD9s+/VfplAKO2yVuc7dbVVJyxSd/W83GcJBIfgwCDd0CFd5zdsYb7Xvn6SxnUcWCsJBIek2Hp6sP9oBZV6kmhqryu8kkAobWCz83RTp0Hm64iQUE3vM9LjXYXGhUVqSo/hig87NRbplSndSSAAZxi/TCJs2rRJkpSenq7g4LJf3IMGnfySW7dunXJycipdXlZWlnn3wuDBZftZCw0NVb9+Jx/ZX7Jkia+bDwAA/FRtJxK8lUAAAOBMUJuJBH9PIAAA4Et+mUTIyMiQJCUlJbmcn5iYqMDAk3eO7d692+3lRUREqF4913fXOda1a9cuXzcfAAD4sdpKJJBAAACgdhIJJBAAAKiYX46JcOzYMUlSbGz5j1BFR0fr+PHjOnz4sNvLi4uLq3B5kipd3sqVK5Wdne1y3vHjxyWdHMS5oKDAZ9vPnxUUFCiwVNcBgL8qKSkpNW3lM+0nSkqKzWmr1f39Ulx8sl5hYaECzsLBhp0Z5tTZeNzaDbs5XVRY6LM2dqzTVO9cOlFjvnxR+VbPj5EQYLHouQtv0JDkjl5tY1FR6dgNn21Pu/3UfrXZ7T6Jw5vnb1abzZw2fLidgZrgN44fMU79rS8uLnF7v9j++C46U/dj69atZbPZdOjQIY8uNyIiQh07dpTdR39/qspxXiudWX9TrFbrqbgN/4rbVurvtN1u84vYCmvpO7f07yvV4n6x2/xjO/sTo9R3O+CP/DKJkJ+fL8m9JEJhYaHby4uJiSm3jGNeZct75plnyh03oVmzZpKkEydOmAkFOMvOzlaA5Wy/gIezQWGpE5rCwkI+037C8X0uScVFRVXeL+50gXfGK3XueTYet6V/5OXk5ui4JcxnsbQOq6vJjXrrif0rVGRYa77AP1hk0YSG3dUzqqnX92Fubq7Ta18dM6UTt1ZrSS3GceoDk5eXp+NB3lmvtVT7ZJydn02cnUpf0MjLy9PxkOO+Dgly3i8FBflV/k45k7+DGjZsqOLiYmVlZXlkeWFhYWrRooXy8/OdzjP9WV5enjltnEF/U0pfMLbbDb+Ku3SCw2q1+kVseXm5Oh7o/fPcEydOHfeG4eX9cloC1B+2sz8pfVMN4I/88mqu44fk6QMglxYeHu5UtiKOTL2nlgcAACBJKeH1NKVJP4VaPHNfhplAiE7ibiQAAE5jsViUlJSkOnXq1HhZYWFhatWqlctxGAEAgDO/fBKhTp06OnjwoFOG/XSOeRUlBhzq1q3rVKcmy7vwwguVkpLicl5wcLB+/fVXhYSEmEkJOAsLC6M7I5wRSv+YCA4O4jPtJ4KDQ8zpwCD394vValVJSYnCwsJksVh83YxaczYet6WfZgsLDfNpGx0X+VP/SCQ8WcMnEhwJhF4xJ8dpCgvzfvvCipzvcPPV9nSMdSVJAQGBtRjHqe8Db56/BQQ6n/ucjZ9NnP1CQ0M5dv1FqXOZ4GD3v7scXdidDf3+p6amaseOHW51b+xKeHi40tLSFBISUq36vlR6/1l05vxNKf37ymKx+FXcpbs7rd3zkPKF1tJ5bkhIqSSat/dLqe+uwMAAv9jO/uRc+p2KM5NfJhHq1aungwcPVtjthOPx+6ioKLeWJ1XcjYVjXmXLmzhxYrnzMjIy9O9//1tRUVEVjr9wLouLiyOJgDNCWKkTmrCwMD7TfqJ0ojc0NNTt/ZKfn6/s7GzFxMQ4Xaw8K1ksZg8tZ+NxW3r/RcfE+LyNQ4YMOfm/pF6ZPTXqk6d1oqTqYyT4ahDl47bS3ThafLY9g4JOnZIGBwX5JI7o6GivrTfktAsnvj5uAXeVvqDBbxz/UXq/RESEu71fjh49KunsOT/o2rWrNm3apMzMzCrVO9MHUY7MiTSnz6S/KWFhp25cCAjwr7hLJziCfHQecrroWvrOjTjqm+MpOCTEL7azPzn7x+7Dmc4vj9DKLvrb7XYziZCQkFDp8hxPIriTlHBneQAAAKfrmZiif18xWZHBVbso4asEAgAAZzKLxaIOHTooMTHR7TpnegIBAABf8cskQqNGjSRJP/30k8v5jvdDQ0PVvHnzSpfXoEEDBQYGKj8/X7t373ZZZvPmzZKkdu3a+br5AADgDFXVRAIJBAAAqq8qiQQSCAAAVJ9fJhEcXQN8++23ys/PLzN/0aJFkqQuXbo4Pf5enoiICKWnp0uSFi9eXGZ+VlaWfvjhB0lSt27dfN18AABwBnM3kUACAQCAmnMnkUACAQCAmvHLMRGSk5PVu3dvrVq1Sk8//bTuuecesw/k9evX66uvvpIkjR492qnewYMHtWzZMknSpZdeqtjYWHPe6NGjtXjxYn300Ufq3bu30tLSJEklJSV64oknZLfb1alTJ7Vp08bXzQeAc86HGav10S/fVVpuf85Rc3rhro3ak135YHrdG7XS+PYX+rqJNZJTlK8JC151q6zNbpMk2Q1D1376rFt1Xrp4vGLDItwq6y0TvnpFOcUFlZbbnf27OX3X/95WZHBYpXXu6TVC5yUk1Wp7HImE8sZIIIEAAIDnOBIJksqMkUACAQCAmvPLJIIkjR07Vj/++KMWLlyoHTt2qFu3bjp48KBWr16t4uJijRgxQp07d3aqs3fvXr344ouSpB49ejglEVJTU3XRRRfp66+/1qRJk9S7d2/Vr19fa9as0Z49exQTE6O77rrL180GgHPSjmOH9PXuH6tUZ2/OEe3NOVJpueBAv/1T57Yim7XK20eS23UKbSWKdauk93yz9ycdLcitUp1v9//iVrnxnQb5pE3lJRJIIAAAUHWFhYUueyoorUmTJvrl0F5F2U7ehHjcVqguqV104sQJnThxotx6wcHBio6O9mn7SkpKzLEaK1K6jGEYysrKqrROQEDAOTmIbVFRkYqLiystZ7VazWmbzerWfggODnYaLLoieXl5MgyjSrHn5+e7FUdERIR50+3pbSooqPwGncLCwlMvDMOtdQYEBCgyMrLScgDOLn57ZSU1NVWvvfaaZsyYoYyMDO3cuVPSyS/IP//5zxozZkyVlzl9+nQlJyfr3Xff1f/+9z/z/Y4dO2rSpElKSqrduxQBAMCZ7fcT2RXObxHXQC9fcovGfD7LfO+BvqPUr2nbSuvWDY9WYED1ep602m3KKsirtFzpxI3NsFcak3Tybs/6ETFuxZFbXKCCksp/vOdbi52m3YkjPDhE0SHhLucdyc+R3Y0f6zbDbk5nFeS5td74sEgzOVlgLVZuUeU/0HOLT/1AL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" alt="Tuning keepX
for the sPLS-DA performed on the SRBCT
gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX
values (x-axis) with the standard deviation based on the repeated cross-validation folds. The diamond indicates the optimal keepX
value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test. As sPLS-DA is an iterative algorithm, values represented for a given component (e.g. comp 1 to 2) include the optimal keepX
value chosen for the previous component (comp 1)." width="75%" />
Figure 31: Tuning keepX
for the sPLS-DA performed on the SRBCT
gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX
values (x-axis) with the standard deviation based on the repeated cross-validation folds. The diamond indicates the optimal keepX
value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test. As sPLS-DA is an iterative algorithm, values represented for a given component (e.g. comp 1 to 2) include the optimal keepX
value chosen for the previous component (comp 1).
The tuning results depend on the tuning grid list.keepX
, as well as the values chosen for folds
and nrepeat
. Therefore, we recommend assessing the performance of the final model, as well as examining the stability of the selected variables across the different folds, as detailed in the next section.
Figure 31 shows that the error rate decreases when more components are included in sPLS-DA. To obtain a more reliable estimation of the error rate, the number of repeats should be increased (between 50 to 100). This type of graph helps not only to choose the ‘optimal’ number of variables to select, but also to confirm the number of components ncomp
. From the code below, we can assess that in fact, the addition of a fourth component does not improve the classification (no statistically significant improvement according to a one-sided \(t-\)test), hence we can choose ncomp = 3
.
# The optimal number of components according to our one-sided t-tests
tune.splsda.srbct$choice.ncomp$ncomp
## [1] 3
# The optimal keepX parameter according to minimal error rate
tune.splsda.srbct$choice.keepX
## comp1 comp2 comp3 comp4
## 6 30 40 6
Here is our final sPLS-DA model with three components and the optimal keepX
obtained from our tuning step.
You can choose to skip the tuning step, and input your arbitrarily chosen parameters in the following code (simply specify your own ncomp
and keepX
values):
# Optimal number of components based on t-tests on the error rate
ncomp <- tune.splsda.srbct$choice.ncomp$ncomp
ncomp
## [1] 3
# Optimal number of variables to select
select.keepX <- tune.splsda.srbct$choice.keepX[1:ncomp]
select.keepX
## comp1 comp2 comp3
## 6 30 40
splsda.srbct <- splsda(X, Y, ncomp = ncomp, keepX = select.keepX)
The performance of the model with the ncomp
and keepX
parameters is assessed with the perf()
function. We use 5-fold validation (folds = 5
), repeated 10 times (nrepeat = 10
) for illustrative purposes, but we recommend increasing to nrepeat = 50
. Here we choose the max.dist
prediction distance, based on our results obtained with PLS-DA.
The classification error rates that are output include both the overall error rate, as well as the balanced error rate (BER) when the number of samples per group is not balanced - as is the case in this study.
set.seed(34) # For reproducibility with this handbook, remove otherwise
perf.splsda.srbct <- perf(splsda.srbct, folds = 5, validation = "Mfold",
dist = "max.dist", progressBar = FALSE, nrepeat = 10)
# perf.splsda.srbct # Lists the different outputs
perf.splsda.srbct$error.rate
## $overall
## max.dist
## comp1 0.44444444
## comp2 0.20317460
## comp3 0.01269841
##
## $BER
## max.dist
## comp1 0.53068841
## comp2 0.27567029
## comp3 0.01138587
We can also examine the error rate per class:
perf.splsda.srbct$error.rate.class
## $max.dist
## comp1 comp2 comp3
## EWS 0.02608696 0.004347826 0.01304348
## BL 0.60000000 0.450000000 0.01250000
## NB 0.91666667 0.483333333 0.00000000
## RMS 0.58000000 0.165000000 0.02000000
These results can be compared with the performance of PLS-DA and show the benefits of variable selection to not only obtain a parsimonious model, but also to improve the classification error rate (overall and per class).
During the repeated cross-validation process in perf()
we can record how often the same variables are selected across the folds. This information is important to answer the question: How reproducible is my gene signature when the training set is perturbed via cross-validation?.
par(mfrow=c(1,2))
# For component 1
stable.comp1 <- perf.splsda.srbct$features$stable$comp1
barplot(stable.comp1, xlab = 'variables selected across CV folds',
ylab = 'Stability frequency',
main = 'Feature stability for comp = 1')
# For component 2
stable.comp2 <- perf.splsda.srbct$features$stable$comp2
barplot(stable.comp2, xlab = 'variables selected across CV folds',
ylab = 'Stability frequency',
main = 'Feature stability for comp = 2')
par(mfrow=c(1,1))
Figure 32: Stability of variable selection from the sPLS-DA on the SRBCT gene expression data. We use a by-product from perf()
to assess how often the same variables are selected for a given keepX
value in the final sPLS-DA model. The barplot represents the frequency of selection across repeated CV folds for each selected gene for component 1 and 2. The genes are ranked according to decreasing frequency.
Figure 32 shows that the genes selected on component 1 are moderately stable (frequency < 0.5) whereas those selected on component 2 are more stable (frequency < 0.7). This can be explained as there are various combinations of genes that are discriminative on component 1, whereas the number of combinations decreases as we move to component 2 which attempts to refine the classification.
The function selectVar()
outputs the variables selected for a given component and their loading values (ranked in decreasing absolute value). We concatenate those results with the feature stability, as shown here for variables selected on component 1:
# First extract the name of selected var:
select.name <- selectVar(splsda.srbct, comp = 1)$name
# Then extract the stability values from perf:
stability <- perf.splsda.srbct$features$stable$comp1[select.name]
# Just the head of the stability of the selected var:
head(cbind(selectVar(splsda.srbct, comp = 1)$value, stability))
## value.var Var1 Freq
## g123 0.82019919 g123 0.46
## g846 0.48384321 g846 0.46
## g335 0.18883438 g335 0.22
## g1606 0.17786558 g1606 0.24
## g836 0.14246204 g836 0.36
## g783 0.07469278 g783 0.20
As we hinted previously, the genes selected on the first component are not necessarily the most stable, suggesting that different combinations can lead to the same discriminative ability of the model. The stability increases in the following components, as the classification task becomes more refined.
Note:
vip()
function on splsda.srbct
.Previously, we showed the ellipse plots displayed for each class. Here we also use the star argument (star = TRUE
), which displays arrows starting from each group centroid towards each individual sample (Figure 33).
plotIndiv(splsda.srbct, comp = c(1,2),
ind.names = FALSE,
ellipse = TRUE, legend = TRUE,
star = TRUE,
title = 'SRBCT, sPLS-DA comp 1 - 2')
plotIndiv(splsda.srbct, comp = c(2,3),
ind.names = FALSE,
ellipse = TRUE, legend = TRUE,
star = TRUE,
title = 'SRBCT, sPLS-DA comp 2 - 3')
SRBCT
gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes." width="50%" />SRBCT
gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes." width="50%" />
Figure 33: Sample plots from the sPLS-DA performed on the SRBCT
gene expression data. Samples are projected into the space spanned by the first three components. The plots represent 95% ellipse confidence intervals around each sample class. The start of each arrow represents the centroid of each class in the space spanned by the components. (a) Components 1 and 2 and (b) Components 2 and 3. Samples are coloured by their tumour subtype. Component 1 discriminates BL vs. the rest, component 2 discriminates EWS vs. the rest, while component 3 further discriminates NB vs. RMS vs. the rest. The combination of all three components enables us to discriminate all classes.
The sample plots are different from PLS-DA (Figure 29) with an overlap of specific classes (i.e. NB + RMS on component 1 and 2), that are then further separated on component 3, thus showing how the genes selected on each component discriminate particular sets of sample groups.
We represent the genes selected with sPLS-DA on the correlation circle plot. Here to increase interpretation, we specify the argument var.names
as the first 10 characters of the gene names (Figure 34). We also reduce the size of the font with the argument cex
.
Note:
plotvar()
as an object to output the coordinates and variable names if the plot is too cluttered.var.name.short <- substr(srbct$gene.name[, 2], 1, 10)
plotVar(splsda.srbct, comp = c(1,2),
var.names = list(var.name.short), cex = 3)
Figure 34: Correlation circle plot representing the genes selected by sPLS-DA performed on the SRBCT
gene expression data. Gene names are truncated to the first 10 characters. Only the genes selected by sPLS-DA are shown in components 1 and 2. We observe three groups of genes (positively associated with component 1, and positively or negatively associated with component 2). This graphic should be interpreted in conjunction with the sample plot.
By considering both the correlation circle plot (Figure 34) and the sample plot in Figure 33, we observe that a group of genes with a positive correlation with component 1 (‘EH domain’, ‘proteasome’ etc.) are associated with the BL samples. We also observe two groups of genes either positively or negatively correlated with component 2. These genes are likely to characterise either the NB + RMS classes, or the EWS class. This interpretation can be further examined with the plotLoadings()
function.
In this plot, the loading weights of each selected variable on each component are represented (see Module 2). The colours indicate the group in which the expression of the selected gene is maximal based on the mean (method = 'median'
is also available for skewed data). For example on component 1:
plotLoadings(splsda.srbct, comp = 1, method = 'mean', contrib = 'max',
name.var = var.name.short)
SRBCT
gene expression data. Genes are ranked according to their loading weight (most important at the bottom to least important at the top), represented as a barplot. Colours indicate the class for which a particular gene is maximally expressed, on average, in this particular class. The plot helps to further characterise the gene signature and should be interpreted jointly with the sample plot (Figure 33)." width="75%" />
Figure 35: Loading plot of the genes selected by sPLS-DA on component 1 on the SRBCT
gene expression data. Genes are ranked according to their loading weight (most important at the bottom to least important at the top), represented as a barplot. Colours indicate the class for which a particular gene is maximally expressed, on average, in this particular class. The plot helps to further characterise the gene signature and should be interpreted jointly with the sample plot (Figure 33).
Here all genes are associated with BL (on average, their expression levels are higher in this class than in the other classes).
Notes:
ndisplay
to only display the top selected genes if the signature is too large.contrib = 'min'
to interpret the inverse trend of the signature (i.e. which genes have the smallest expression in which class, here a mix of NB and RMS samples).To complete the visualisation, the CIM in this special case is a simple hierarchical heatmap (see ?cim
) representing the expression levels of the genes selected across all three components with respect to each sample. Here we use an Euclidean distance with Complete agglomeration method, and we specify the argument row.sideColors
to colour the samples according to their tumour type (Figure 36).
cim(splsda.srbct, row.sideColors = color.mixo(Y))
Figure 36: Clustered Image Map of the genes selected by sPLS-DA on the SRBCT
gene expression data across all 3 components. A hierarchical clustering based on the gene expression levels of the selected genes, with samples in rows coloured according to their tumour subtype (using Euclidean distance with Complete agglomeration method). As expected, we observe a separation of all different tumour types, which are characterised by different levels of expression.
The heatmap shows the level of expression of the genes selected by sPLS-DA across all three components, and the overall ability of the gene signature to discriminate the tumour subtypes.
Note:
comp
if you wish to visualise a specific set of components in cim()
.In this section, we artificially create an ‘external’ test set on which we want to predict the class membership to illustrate the prediction process in sPLS-DA (see Extra Reading material). We randomly select 50 samples from the srbct
study as part of the training set, and the remainder as part of the test set:
set.seed(33) # For reproducibility with this handbook, remove otherwise
train <- sample(1:nrow(X), 50) # Randomly select 50 samples in training
test <- setdiff(1:nrow(X), train) # Rest is part of the test set
# Store matrices into training and test set:
X.train <- X[train, ]
X.test <- X[test,]
Y.train <- Y[train]
Y.test <- Y[test]
# Check dimensions are OK:
dim(X.train); dim(X.test)
## [1] 50 2308
## [1] 13 2308
Here we assume that the tuning step was performed on the training set only (it is really important to tune only on the training step to avoid overfitting), and that the optimal keepX
values are, for example, keepX = c(20,30,40)
on three components. The final model on the training data is:
train.splsda.srbct <- splsda(X.train, Y.train, ncomp = 3, keepX = c(20,30,40))
We now apply the trained model on the test set X.test
and we specify the prediction distance, for example mahalanobis.dist
(see also ?predict.splsda
):
predict.splsda.srbct <- predict(train.splsda.srbct, X.test,
dist = "mahalanobis.dist")
The $class
output of our object predict.splsda.srbct
gives the predicted classes of the test samples.
First we concatenate the prediction for each of the three components (conditionally on the previous component) and the real class - in a real application case you may not know the true class.
# Just the head:
head(data.frame(predict.splsda.srbct$class, Truth = Y.test))
## mahalanobis.dist.comp1 mahalanobis.dist.comp2 mahalanobis.dist.comp3
## EWS.T7 EWS EWS EWS
## EWS.T15 EWS EWS EWS
## EWS.C8 EWS EWS EWS
## EWS.C10 EWS EWS EWS
## BL.C8 BL BL BL
## NB.C6 NB NB NB
## Truth
## EWS.T7 EWS
## EWS.T15 EWS
## EWS.C8 EWS
## EWS.C10 EWS
## BL.C8 BL
## NB.C6 NB
If we only look at the final prediction on component 2, compared to the real class:
# Compare prediction on the second component and change as factor
predict.comp2 <- predict.splsda.srbct$class$mahalanobis.dist[,2]
table(factor(predict.comp2, levels = levels(Y)), Y.test)
## Y.test
## EWS BL NB RMS
## EWS 4 0 0 0
## BL 0 1 0 0
## NB 0 0 1 1
## RMS 0 0 0 6
And on the third compnent:
# Compare prediction on the third component and change as factor
predict.comp3 <- predict.splsda.srbct$class$mahalanobis.dist[,3]
table(factor(predict.comp3, levels = levels(Y)), Y.test)
## Y.test
## EWS BL NB RMS
## EWS 4 0 0 0
## BL 0 1 0 0
## NB 0 0 1 0
## RMS 0 0 0 7
The prediction is better on the third component, compared to a 2-component model.
Next, we look at the output $predict
, which gives the predicted dummy scores assigned for each test sample and each class level for a given component (as explained in Extra Reading material). Each column represents a class category:
# On component 3, just the head:
head(predict.splsda.srbct$predict[, , 3])
## EWS BL NB RMS
## EWS.T7 1.26848551 -0.05273773 -0.24070902 0.024961232
## EWS.T15 1.15058424 -0.02222145 -0.11877994 -0.009582845
## EWS.C8 1.25628411 0.05481026 -0.16500118 -0.146093198
## EWS.C10 0.83995956 0.10871106 0.16452934 -0.113199949
## BL.C8 0.02431262 0.90877176 0.01775304 0.049162580
## NB.C6 0.06738230 0.05086884 0.86247360 0.019275265
In PLS-DA and sPLS-DA, the final prediction call is given based on this matrix on which a pre-specified distance (such as mahalanobis.dist
here) is applied. From this output, we can understand the link between the dummy matrix \(\boldsymbol Y\), the prediction, and the importance of choosing the prediction distance. More details are provided in Extra Reading material.
As PLS-DA acts as a classifier, we can plot the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) to complement the sPLS-DA classification performance results. The AUC is calculated from training cross-validation sets and averaged. The ROC curve is displayed in Figure 37. In a multiclass setting, each curve represents one class vs. the others and the AUC is indicated in the legend, and also in the numerical output:
auc.srbct <- auroc(splsda.srbct)
SRBCT
gene expression data on component 1 averaged across one-vs.-all comparisons. Numerical outputs include the AUC and a Wilcoxon test p-value for each ‘one vs. other’ class comparisons that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the sPLS-DA model can distinguish BL subjects from the other groups with a high true positive and low false positive rate, while the model is less well able to distinguish samples from other classes on component 1." width="75%" />
Figure 37: ROC curve and AUC from sPLS-DA on the SRBCT
gene expression data on component 1 averaged across one-vs.-all comparisons. Numerical outputs include the AUC and a Wilcoxon test p-value for each ‘one vs. other’ class comparisons that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the sPLS-DA model can distinguish BL subjects from the other groups with a high true positive and low false positive rate, while the model is less well able to distinguish samples from other classes on component 1.
## $Comp1
## AUC p-value
## EWS vs Other(s) 0.4163 2.717e-01
## BL vs Other(s) 1.0000 5.586e-06
## NB vs Other(s) 0.8448 2.214e-04
## RMS vs Other(s) 0.6000 2.041e-01
##
## $Comp2
## AUC p-value
## EWS vs Other(s) 1.0000 5.135e-11
## BL vs Other(s) 1.0000 5.586e-06
## NB vs Other(s) 0.9020 1.663e-05
## RMS vs Other(s) 0.7895 2.363e-04
##
## $Comp3
## AUC p-value
## EWS vs Other(s) 1 5.135e-11
## BL vs Other(s) 1 5.586e-06
## NB vs Other(s) 1 8.505e-08
## RMS vs Other(s) 1 2.164e-10
The ideal ROC curve should be along the top left corner, indicating a high true positive rate (sensitivity on the y-axis) and a high true negative rate (or low 100 - specificity on the x-axis), with an AUC close to 1. This is the case for BL vs. the others on component 1. The numerical output shows a perfect classification on component 3.
Note:
N-Integration is the framework of having multiple datasets which measure different aspects of the same samples. For example, you may have transcriptomic, genetic and proteomic data for the same set of cells. N-integrative methods are built to use the information in all three of these dataframes simultaenously.
DIABLO is a novel mixOmics
framework for the integration of multiple data sets while explaining their relationship with a categorical outcome variable. DIABLO stands for Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies. It can also be referred to as Multiblock (s)PLS-DA.
Human breast cancer is a heterogeneous disease in terms of molecular alterations, cellular composition, and clinical outcome. Breast tumours can be classified into several subtypes, according to their levels of mRNA expression (Sørlie et al. 2001). Here we consider a subset of data generated by The Cancer Genome Atlas Network (Cancer Genome Atlas Network et al. 2012). For the package, data were normalised, and then drastically prefiltered for illustrative purposes.
The data were divided into a training set with a subset of 150 samples from the mRNA, miRNA and proteomics data, and a test set including 70 samples, but only with mRNA and miRNA data (the proteomics data are missing). The aim of this integrative analysis is to identify a highly correlated multi-omics signature discriminating the breast cancer subtypes Basal, Her2 and LumA.
The breast.TCGA
(more details can be found in ?breast.TCGA
) is a list containing training and test sets of omics data data.train
and data.test
which include:
$miRNA
: A data frame with 150 (70) rows and 184 columns in the training (test) data set for the miRNA expression levels,$mRNA
: A data frame with 150 (70) rows and 520 columns in the training (test) data set for the mRNA expression levels,$protein
: A data frame with 150 rows and 142 columns in the training data set for the protein abundance (there are no proteomics in the test set),$subtype
: A factor indicating the breast cancer subtypes in the training (for 150 samples) and test sets (for 70 samples).This case study covers an interesting scenario where one omic data set is missing in the test set, but because the method generates a set of components per training data set, we can still assess the prediction or performance evaluation using majority or weighted prediction vote.
To illustrate the multiblock sPLS-DA approach, we will integrate the expression levels of miRNA, mRNA and the abundance of proteins while discriminating the subtypes of breast cancer, then predict the subtypes of the samples in the test set.
The input data is first set up as a list of \(Q\) matrices \(\boldsymbol X_1, \dots, \boldsymbol X_Q\) and a factor indicating the class membership of each sample \(\boldsymbol Y\). Each data frame in \(\boldsymbol X\) should be named as we will match these names with the keepX
parameter for the sparse method.
library(mixOmics)
data(breast.TCGA)
# Extract training data and name each data frame
# Store as list
X <- list(mRNA = breast.TCGA$data.train$mrna,
miRNA = breast.TCGA$data.train$mirna,
protein = breast.TCGA$data.train$protein)
# Outcome
Y <- breast.TCGA$data.train$subtype
summary(Y)
## Basal Her2 LumA
## 45 30 75
The choice of the design can be motivated by different aspects, including:
Biological apriori knowledge: Should we expect mRNA
and miRNA
to be highly correlated?
Analytical aims: As further developed in Singh et al. (2019), a compromise needs to be achieved between a classification and prediction task, and extracting the correlation structure of the data sets. A full design with weights = 1 will favour the latter, but at the expense of classification accuracy, whereas a design with small weights will lead to a highly predictive signature. This pertains to the complexity of the analytical task involved as several constraints are included in the optimisation procedure. For example, here we choose a 0.1 weighted model as we are interested in predicting test samples later in this case study.
design <- matrix(0.1, ncol = length(X), nrow = length(X),
dimnames = list(names(X), names(X)))
diag(design) <- 0
design
## mRNA miRNA protein
## mRNA 0.0 0.1 0.1
## miRNA 0.1 0.0 0.1
## protein 0.1 0.1 0.0
Note however that even with this design, we will still unravel a correlated signature as we require all data sets to explain the same outcome \(\boldsymbol y\), as well as maximising pairs of covariances between data sets.
res1.pls.tcga <- pls(X$mRNA, X$protein, ncomp = 1)
cor(res1.pls.tcga$variates$X, res1.pls.tcga$variates$Y)
res2.pls.tcga <- pls(X$mRNA, X$miRNA, ncomp = 1)
cor(res2.pls.tcga$variates$X, res2.pls.tcga$variates$Y)
res3.pls.tcga <- pls(X$protein, X$miRNA, ncomp = 1)
cor(res3.pls.tcga$variates$X, res3.pls.tcga$variates$Y)
## comp1
## comp1 0.9031761
## comp1
## comp1 0.8456299
## comp1
## comp1 0.7982008
The data sets taken in a pairwise manner are highly correlated, indicating that a design with weights \(\sim 0.8 - 0.9\) could be chosen.
As in the PLS-DA framework presented in Module 3, we first fit a block.plsda
model without variable selection to assess the global performance of the model and choose the number of components. We run perf()
with 10-fold cross validation repeated 10 times for up to 5 components and with our specified design matrix. Similar to PLS-DA, we obtain the performance of the model with respect to the different prediction distances (Figure 38):
diablo.tcga <- block.plsda(X, Y, ncomp = 5, design = design)
set.seed(123) # For reproducibility, remove for your analyses
perf.diablo.tcga = perf(diablo.tcga, validation = 'Mfold', folds = 10, nrepeat = 10)
#perf.diablo.tcga$error.rate # Lists the different types of error rates
# Plot of the error rates based on weighted vote
plot(perf.diablo.tcga)
Figure 38: Choosing the number of components in block.plsda
using perf()
with 10 x 10-fold CV function in the breast.TCGA
study. Classification error rates (overall and balanced, see Module 2) are represented on the y-axis with respect to the number of components on the x-axis for each prediction distance presented in PLS-DA in Seciton 3.4 and detailed in Extra reading material 3 from Module 3. Bars show the standard deviation across the 10 repeated folds. The plot shows that the error rate reaches a minimum from 2 to 3 dimensions.
The performance plot indicates that two components should be sufficient in the final model, and that the centroids distance might lead to better prediction. A balanced error rate (BER) should be considered for further analysis.
The following outputs the optimal number of components according to the prediction distance and type of error rate (overall or balanced), as well as a prediction weighting scheme illustrated further below.
perf.diablo.tcga$choice.ncomp$WeightedVote
## max.dist centroids.dist mahalanobis.dist
## Overall.ER 3 2 3
## Overall.BER 3 2 3
Thus, here we choose our final ncomp
value:
ncomp <- perf.diablo.tcga$choice.ncomp$WeightedVote["Overall.BER", "centroids.dist"]
We then choose the optimal number of variables to select in each data set using the tune.block.splsda
function. The function tune()
is run with 10-fold cross validation, but repeated only once (nrepeat = 1
) for illustrative and computational reasons here. For a thorough tuning process, we advise increasing the nrepeat
argument to 10-50, or more.
We choose a keepX
grid that is relatively fine at the start, then coarse. If the data sets are easy to classify, the tuning step may indicate the smallest number of variables to separate the sample groups. Hence, we start our grid at the value 5
to avoid a too small signature that may preclude biological interpretation.
# chunk takes about 2 min to run
set.seed(123) # for reproducibility
test.keepX <- list(mRNA = c(5:9, seq(10, 25, 5)),
miRNA = c(5:9, seq(10, 20, 2)),
proteomics = c(seq(5, 25, 5)))
tune.diablo.tcga <- tune.block.splsda(X, Y, ncomp = 2,
test.keepX = test.keepX, design = design,
validation = 'Mfold', folds = 10, nrepeat = 1,
BPPARAM = BiocParallel::SnowParam(workers = 2),
dist = "centroids.dist")
list.keepX <- tune.diablo.tcga$choice.keepX
Note:
?tune.block.splsda
.The number of features to select on each component is returned and stored for the final model:
list.keepX
## $mRNA
## [1] 8 25
##
## $miRNA
## [1] 14 5
##
## $protein
## [1] 10 5
Note:
ncomp
and keepX
parameters (as a list for the latter, as shown below).list.keepX <- list( mRNA = c(8, 25), miRNA = c(14,5), protein = c(10, 5))
The final multiblock sPLS-DA model includes the tuned parameters and is run as:
diablo.tcga <- block.splsda(X, Y, ncomp = ncomp,
keepX = list.keepX, design = design)
## Design matrix has changed to include Y; each block will be
## linked to Y.
#06.tcga # Lists the different functions of interest related to that object
A warning message informs us that the outcome \(\boldsymbol Y\) has been included automatically in the design, so that the covariance between each block’s component and the outcome is maximised, as shown in the final design output:
diablo.tcga$design
## mRNA miRNA protein Y
## mRNA 0.0 0.1 0.1 1
## miRNA 0.1 0.0 0.1 1
## protein 0.1 0.1 0.0 1
## Y 1.0 1.0 1.0 0
The selected variables can be extracted with the function selectVar()
, for example in the mRNA block, along with their loading weights (not output here):
# mRNA variables selected on component 1
selectVar(diablo.tcga, block = 'mRNA', comp = 1)
Note:
perf()
function, similar to the example given in the PLS-DA analysis (Module 3).plotDiablo
plotDiablo()
is a diagnostic plot to check whether the correlations between components from each data set were maximised as specified in the design matrix. We specify the dimension to be assessed with the ncomp
argument (Figure 39).
plotDiablo(diablo.tcga, ncomp = 1)
breast.TCGA
study. Samples are represented based on the specified component (here ncomp = 1
) for each data set (mRNA, miRNA and protein). Samples are coloured by breast cancer subtype (Basal, Her2 and LumA) and 95% confidence ellipse plots are represented. The bottom left numbers indicate the correlation coefficients between the first components from each data set. In this example, mRNA expression and protein concentration are highly correlated on the first dimension." width="75%" />
Figure 39: Diagnostic plot from multiblock sPLS-DA applied on the breast.TCGA
study. Samples are represented based on the specified component (here ncomp = 1
) for each data set (mRNA, miRNA and protein). Samples are coloured by breast cancer subtype (Basal, Her2 and LumA) and 95% confidence ellipse plots are represented. The bottom left numbers indicate the correlation coefficients between the first components from each data set. In this example, mRNA expression and protein concentration are highly correlated on the first dimension.
The plot indicates that the first components from all data sets are highly correlated. The colours and ellipses represent the sample subtypes and indicate the discriminative power of each component to separate the different tumour subtypes. Thus, multiblock sPLS-DA is able to extract a strong correlation structure between data sets, as well as discriminate the breast cancer subtypes on the first component.
plotIndiv
The sample plot with the plotIndiv()
function projects each sample into the space spanned by the components from each block, resulting in a series of graphs corresponding to each data set (Figure 40). The optional argument blocks
can output a specific data set. Ellipse plots are also available (argument ellipse = TRUE
).
plotIndiv(diablo.tcga, ind.names = FALSE, legend = TRUE,
title = 'TCGA, DIABLO comp 1 - 2')
breast.TCGA
study. The samples are plotted according to their scores on the first 2 components for each data set. Samples are coloured by cancer subtype and are classified into three classes: Basal, Her2 and LumA. The plot shows the degree of agreement between the different data sets and the discriminative ability of each data set." width="75%" />
Figure 40: Sample plot from multiblock sPLS-DA performed on the breast.TCGA
study. The samples are plotted according to their scores on the first 2 components for each data set. Samples are coloured by cancer subtype and are classified into three classes: Basal, Her2 and LumA. The plot shows the degree of agreement between the different data sets and the discriminative ability of each data set.
This type of graphic allows us to better understand the information extracted from each data set and its discriminative ability. Here we can see that the LumA group can be difficult to classify in the miRNA data.
Note:
block = 'average'
that averages the components from all blocks to produce a single plot. The argument block='weighted.average'
is a weighted average of the components according to their correlation with the components associated with the outcome.plotArrow
In the arrow plot in Figure 41, the start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of that same sample but in each block. Such graphics highlight the agreement between all data sets at the sample level when modelled with multiblock sPLS-DA.
plotArrow(diablo.tcga, ind.names = FALSE, legend = TRUE,
title = 'TCGA, DIABLO comp 1 - 2')
breast.TCGA
study. The samples are projected into the space spanned by the first two components for each data set then overlaid across data sets. The start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of the same sample in each block. Arrows further from their centroid indicate some disagreement between the data sets. Samples are coloured by cancer subtype (Basal, Her2 and LumA)." width="75%" />
Figure 41: Arrow plot from multiblock sPLS-DA performed on the breast.TCGA
study. The samples are projected into the space spanned by the first two components for each data set then overlaid across data sets. The start of the arrow indicates the centroid between all data sets for a given sample and the tip of the arrow the location of the same sample in each block. Arrows further from their centroid indicate some disagreement between the data sets. Samples are coloured by cancer subtype (Basal, Her2 and LumA).
This plot shows that globally, the discrimination of all breast cancer subtypes can be extracted from all data sets, however, there are some dissimilarities at the samples level across data sets (the common information cannot be extracted in the same way across data sets).
The visualisation of the selected variables is crucial to mine their associations in multiblock sPLS-DA. Here we revisit existing outputs presented in Module 2 with further developments for multiple data set integration. All the plots presented provide complementary information for interpreting the results.
plotVar
The correlation circle plot highlights the contribution of each selected variable to each component. Important variables should be close to the large circle (see Module 2). Here, only the variables selected on components 1 and 2 are depicted (across all blocks), see Figure 42. Clusters of points indicate a strong correlation between variables. For better visibility we chose to hide the variable names.
plotVar(diablo.tcga, var.names = FALSE, style = 'graphics', legend = TRUE,
pch = c(16, 17, 15), cex = c(2,2,2),
col = c('darkorchid', 'brown1', 'lightgreen'),
title = 'TCGA, DIABLO comp 1 - 2')
Figure 42: Correlation circle plot from multiblock sPLS-DA performed on the breast.TCGA
study. The variable coordinates are defined according to their correlation with the first and second components for each data set. Variable types are indicated with different symbols and colours, and are overlaid on the same plot. The plot highlights the potential associations within and between different variable types when they are important in defining their own component.
The correlation circle plot shows some positive correlations (between selected miRNA and proteins, between selected proteins and mRNA) and negative correlations between mRNAand miRNA on component 1. The correlation structure is less obvious on component 2, but we observe some key selected features (proteins and miRNA) that seem to highly contribute to component 2.
Note:
These results can be further investigated by showing the variable names on this plot (or extracting their coordinates available from the plot saved into an object, see ?plotVar
), and looking at various outputs from selectVar()
and plotLoadings()
.
You can choose to only show specific variable type names, e.g. var.names = c(FALSE, FALSE, TRUE)
(where each argument is assigned to a data set in \(\boldsymbol X\)). Here for example, the protein names only would be output.
circosPlot
The circos plot represents the correlations between variables of different types, represented on the side quadrants. Several display options are possible, to show within and between connections between blocks, and expression levels of each variable according to each class (argument line = TRUE
). The circos plot is built based on a similarity matrix, which was extended to the case of multiple data sets from González et al. (2012) (see also Module 2 and Extra Reading material from that module). A cutoff
argument can be further included to visualise correlation coefficients above this threshold in the multi-omics signature (Figure 43). The colours for the blocks and correlation lines can be chosen with color.blocks
and color.cor
respectively:
circosPlot(diablo.tcga, cutoff = 0.7, line = TRUE,
color.blocks = c('darkorchid', 'brown1', 'lightgreen'),
color.cor = c("chocolate3","grey20"), size.labels = 1.5)
breast.TCGA
study. The plot represents the correlations greater than 0.7 between variables of different types, represented on the side quadrants. The internal connecting lines show the positive (negative) correlations. The outer lines show the expression levels of each variable in each sample group (Basal, Her2 and LumA)." width="75%" />
Figure 43: Circos plot from multiblock sPLS-DA performed on the breast.TCGA
study. The plot represents the correlations greater than 0.7 between variables of different types, represented on the side quadrants. The internal connecting lines show the positive (negative) correlations. The outer lines show the expression levels of each variable in each sample group (Basal, Her2 and LumA).
The circos plot enables us to visualise cross-correlations between data types, and the nature of these correlations (positive or negative). Here we observe that correlations > 0.7 are between a few mRNAand some Proteins, whereas the majority of strong (negative) correlations are observed between miRNA and mRNAor Proteins. The lines indicating the average expression levels per breast cancer subtype indicate that the selected features are able to discriminate the sample groups.
network
Relevance networks, which are also built on the similarity matrix, can also visualise the correlations between the different types of variables. Each colour represents a type of variable. A threshold can also be set using the argument cutoff
(Figure 44). By default the network includes only variables selected on component 1, unless specified in comp
.
Note that sometimes the output may not show with Rstudio due to margin issues. We can either use X11()
to open a new window, or save the plot as an image using the arguments save
and name.save
, as we show below. An interactive
argument is also available for the cutoff
argument, see details in ?network
.
# X11() # Opens a new window
network(diablo.tcga, blocks = c(1,2,3),
cutoff = 0.4,
color.node = c('darkorchid', 'brown1', 'lightgreen'),
# To save the plot, uncomment below line
#save = 'png', name.save = 'diablo-network'
)
Figure 44: Relevance network for the variables selected by multiblock sPLS-DA performed on the breast.TCGA
study on component 1. Each node represents a selected variable with colours indicating their type. The colour of the edges represent positive or negative correlations. Further tweaking of this plot can be obtained, see the help file ?network
.
The relevance network in Figure 44 shows two groups of features of different types. Within each group we observe positive and negative correlations. The visualisation of this plot could be further improved by changing the names of the original features.
Note that the network can be saved in a .gml format to be input into the software Cytoscape, using the R package igraph
(Csardi, Nepusz, et al. 2006):
# Not run
library(igraph)
myNetwork <- network(diablo.tcga, blocks = c(1,2,3), cutoff = 0.4)
write.graph(myNetwork$gR, file = "myNetwork.gml", format = "gml")
plotLoadings
plotLoadings()
visualises the loading weights of each selected variable on each component and each data set. The colour indicates the class in which the variable has the maximum level of expression (contrib = 'max'
) or minimum (contrib = 'min'
), on average (method = 'mean'
) or using the median (method = 'median'
).
plotLoadings(diablo.tcga, comp = 1, contrib = 'max', method = 'median')
breast.TCGA
study on component 1. The most important variables (according to the absolute value of their coefficients) are ordered from bottom to top. As this is a supervised analysis, colours indicate the class for which the median expression value is the highest for each feature (variables selected characterise Basal and LumA)." width="75%" />
Figure 45: Loading plot for the variables selected by multiblock sPLS-DA performed on the breast.TCGA
study on component 1. The most important variables (according to the absolute value of their coefficients) are ordered from bottom to top. As this is a supervised analysis, colours indicate the class for which the median expression value is the highest for each feature (variables selected characterise Basal and LumA).
The loading plot shows the multi-omics signature selected on component 1, where each panel represents one data type. The importance of each variable is visualised by the length of the bar (i.e. its loading coefficient value). The combination of the sign of the coefficient (positive / negative) and the colours indicate that component 1 discriminates primarily the Basal samples vs. the LumA samples (see the sample plots also). The features selected are highly expressed in one of these two subtypes. One could also plot the second component that discriminates the Her2 samples.
cimDiablo
The cimDiablo()
function is a clustered image map specifically implemented to represent the multi-omics molecular signature expression for each sample. It is very similar to a classical hierarchical clustering (Figure 46).
cimDiablo(diablo.tcga, color.blocks = c('darkorchid', 'brown1', 'lightgreen'),
comp = 1, margin=c(8,20), legend.position = "right")
##
## trimming values to [-3, 3] range for cim visualisation. See 'trim' arg in ?cimDiablo
Figure 46: Clustered Image Map for the variables selected by multiblock sPLS-DA performed on the breast.TCGA
study on component 1. By default, Euclidean distance and Complete linkage methods are used. The CIM represents samples in rows (indicated by their breast cancer subtype on the left hand side of the plot) and selected features in columns (indicated by their data type at the top of the plot).
According to the CIM, component 1 seems to primarily classify the Basal samples, with a group of overexpressed miRNA and underexpressed mRNAand proteins. A group of LumA samples can also be identified due to the overexpression of the same mRNAand proteins. Her2 samples remain quite mixed with the other LumA samples.
We assess the performance of the model using 10-fold cross-validation repeated 10 times with the function perf()
. The method runs a block.splsda()
model on the pre-specified arguments input from our final object diablo.tcga
but on cross-validated samples. We then assess the accuracy of the prediction on the left out samples. Since the tune()
function was used with the centroid.dist
argument, we examine the outputs of the perf()
function for that same distance:
set.seed(123) # For reproducibility with this handbook, remove otherwise
perf.diablo.tcga <- perf(diablo.tcga, validation = 'Mfold', folds = 10,
nrepeat = 10, dist = 'centroids.dist')
#perf.diablo.tcga # Lists the different outputs
We can extract the (balanced) classification error rates globally or overall with
perf.diablo.tcga$error.rate.per.class
, the predicted components associated to \(\boldsymbol Y\), or the stability of the selected features with perf.diablo.tcga$features
.
Here we look at the different performance assessment schemes specific to multiple data set integration.
First, we output the performance with the majority vote, that is, since the prediction is based on the components associated to their own data set, we can then weight those predictions across data sets according to a majority vote scheme. Based on the predicted classes, we then extract the classification error rate per class and per component:
# Performance with Majority vote
perf.diablo.tcga$MajorityVote.error.rate
## $centroids.dist
## comp1 comp2
## Basal 0.02666667 0.04444444
## Her2 0.20666667 0.12333333
## LumA 0.04533333 0.00800000
## Overall.ER 0.07200000 0.04200000
## Overall.BER 0.09288889 0.05859259
The output shows that with the exception of the Basal samples, the classification improves with the addition of the second component.
Another prediction scheme is to weight the classification error rate from each data set according to the correlation between the predicted components and the \(\boldsymbol Y\) outcome.
# Performance with Weighted vote
perf.diablo.tcga$WeightedVote.error.rate
## $centroids.dist
## comp1 comp2
## Basal 0.006666667 0.04444444
## Her2 0.140000000 0.10666667
## LumA 0.045333333 0.00800000
## Overall.ER 0.052666667 0.03866667
## Overall.BER 0.064000000 0.05303704
Compared to the previous majority vote output, we can see that the classification accuracy is slightly better on component 2 for the subtype Her2.
An AUC plot per block is plotted using the function auroc()
. We have already mentioned in Module 3 for PLS-DA, the interpretation of this output may not be particularly insightful in relation to the performance evaluation of our methods, but can complement the statistical analysis. For example, here for the miRNA data set once we have reached component 2 (Figure 47):
auc.diablo.tcga <- auroc(diablo.tcga, roc.block = "miRNA", roc.comp = 2,
print = FALSE)
Figure 47: ROC and AUC based on multiblock sPLS-DA performed on the breast.TCGA
study for the miRNA data set after 2 components. The function calculates the ROC curve and AUC for one class vs. the others. If we set print = TRUE
, the Wilcoxon test p-value that assesses the differences between the predicted components from one class vs. the others is output.
Figure 47 shows that the Her2 subtype is the most difficult to classify with multiblock sPLS-DA compared to the other subtypes.
The predict()
function associated with a block.splsda()
object predicts the class of samples from an external test set. In our specific case, one data set is missing in the test set but the method can still be applied. We need to ensure the names of the blocks correspond exactly to those from the training set:
# Prepare test set data: here one block (proteins) is missing
data.test.tcga <- list(mRNA = breast.TCGA$data.test$mrna,
miRNA = breast.TCGA$data.test$mirna)
predict.diablo.tcga <- predict(diablo.tcga, newdata = data.test.tcga)
# The warning message will inform us that one block is missing
#predict.diablo # List the different outputs
The following output is a confusion matrix that compares the real subtypes with the predicted subtypes for a 2 component model, for the distance of interest centroids.dist
and the prediction scheme WeightedVote
:
confusion.mat.tcga <- get.confusion_matrix(truth = breast.TCGA$data.test$subtype,
predicted = predict.diablo.tcga$WeightedVote$centroids.dist[,2])
confusion.mat.tcga
## predicted.as.Basal predicted.as.Her2 predicted.as.LumA
## Basal 20 1 0
## Her2 0 13 1
## LumA 0 3 32
From this table, we see that one Basal and one Her2 sample are wrongly predicted as Her2 and Lum A respectively, and 3 LumA samples are wrongly predicted as Her2. The balanced prediction error rate can be obtained as:
get.BER(confusion.mat.tcga)
## [1] 0.06825397
It would be worthwhile at this stage to revisit the chosen design of the multiblock sPLS-DA model to assess the influence of the design on the prediction performance on this test set - even though this back and forth analysis is a biased criterion to choose the design!
We integrate four transcriptomics studies of microarray stem cells (125 samples in total). The original data set from the Stemformatics database1 www.stemformatics.org (Wells et al. 2013) was reduced to fit into the package, and includes a randomly-chosen subset of the expression levels of 400 genes. The aim is to classify three types of human cells: human fibroblasts (Fib) and human induced Pluripotent Stem Cells (hiPSC & hESC).
There is a biological hierarchy among the three cell types. On one hand, differences between pluripotent (hiPSC and hESC) and non-pluripotent cells (Fib) are well-characterised and are expected to contribute to the main biological variation. On the other hand, hiPSC are genetically reprogrammed to behave like hESC and both cell types are commonly assumed to be alike. However, differences have been reported in the literature (Chin et al. (2009), Newman and Cooper (2010)). We illustrate the use of MINT to address sub-classification problems in a single analysis.
We first load the data from the package and set up the categorical outcome \(\boldsymbol Y\) and the study
membership:
library(mixOmics)
data(stemcells)
# The combined data set X
X <- stemcells$gene
dim(X)
## [1] 125 400
# The outcome vector Y:
Y <- stemcells$celltype
length(Y)
## [1] 125
summary(Y)
## Fibroblast hESC hiPSC
## 30 37 58
We then store the vector indicating the sample membership of each independent study:
study <- stemcells$study
# Number of samples per study:
summary(study)
## 1 2 3 4
## 38 51 21 15
# Experimental design
table(Y,study)
## study
## Y 1 2 3 4
## Fibroblast 6 18 3 3
## hESC 20 3 8 6
## hiPSC 12 30 10 6
We first perform a MINT PLS-DA with all variables included in the model and ncomp = 5
components. The perf()
function is used to estimate the performance of the model using LOGOCV, and to choose the optimal number of components for our final model (see Fig 48).
mint.plsda.stem <- mint.plsda(X = X, Y = Y, study = study, ncomp = 5)
set.seed(2543) # For reproducible results here, remove for your own analyses
perf.mint.plsda.stem <- perf(mint.plsda.stem)
plot(perf.mint.plsda.stem)
Figure 48: Choosing the number of components in mint.plsda
using perf()
with LOGOCV in the stemcells
study. Classification error rates (overall and balanced, see Module 2) are represented on the y-axis with respect to the number of components on the x-axis for each prediction distance (see Module 3 and Extra Reading material ‘PLS-DA appendix’). The plot shows that the error rate reaches a minimum from 1 component with the BER and centroids distance.
Based on the performance plot (Figure 48), ncomp = 2
seems to achieve the best performance for the centroid distance, and ncomp = 1
for the Mahalanobis distance in terms of BER. Additional numerical outputs such as the BER and overall error rates per component, and the error rates per class and per prediction distance, can be output:
perf.mint.plsda.stem$global.error$BER
# Type also:
# perf.mint.plsda.stem$global.error
## max.dist centroids.dist mahalanobis.dist
## comp1 0.3803556 0.3333333 0.3333333
## comp2 0.3519556 0.3320000 0.3725111
## comp3 0.3499556 0.3384000 0.3232889
## comp4 0.3541111 0.3427778 0.3898000
## comp5 0.3353778 0.3268667 0.3097111
While we may want to focus our interpretation on the first component, we run a final MINT PLS-DA model for ncomp = 2
to obtain 2D graphical outputs (Figure 49):
final.mint.plsda.stem <- mint.plsda(X = X, Y = Y, study = study, ncomp = 2)
#final.mint.plsda.stem # Lists the different functions
plotIndiv(final.mint.plsda.stem, legend = TRUE, title = 'MINT PLS-DA',
subtitle = 'stem cell study', ellipse = T)
stemcells
gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate the study membership. Component 1 discriminates fibroblast vs. the others, while component 2 discriminates some of the hiPSC vs. hESC." width="75%" />
Figure 49: Sample plot from the MINT PLS-DA performed on the stemcells
gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate the study membership. Component 1 discriminates fibroblast vs. the others, while component 2 discriminates some of the hiPSC vs. hESC.
The sample plot (Fig 49) shows that fibroblast are separated on the first component. We observe that while deemed not crucial for an optimal discrimination, the second component seems to help separate hESC and hiPSC further. The effect of study after MINT modelling is not strong.
We can compare this output to a classical PLS-DA to visualise the study effect (Figure 50):
plsda.stem <- plsda(X = X, Y = Y, ncomp = 2)
plotIndiv(plsda.stem, pch = study,
legend = TRUE, title = 'Classic PLS-DA',
legend.title = 'Cell type', legend.title.pch = 'Study')
Figure 50: Sample plot from a classic PLS-DA performed on the stemcells
gene expression data that highlights the study effect (indicated by symbols). Samples are projected into the space spanned by the first two components. We still do observe some discrimination between the cell types.
The MINT PLS-DA model shown earlier is built on all 400 genes in \(\boldsymbol X\), many of which may be uninformative to characterise the different classes. Here we aim to identify a small subset of genes that best discriminate the classes.
We can choose the keepX
parameter using the tune()
function for a MINT object. The function performs LOGOCV for different values of test.keepX
provided on each component, and no repeat argument is needed. Based on the mean classification error rate (overall error rate or BER) and a centroids distance, we output the optimal number of variables keepX
to be included in the final model.
set.seed(2543) # For a reproducible result here, remove for your own analyses
tune.mint.splsda.stem <- tune(X = X, Y = Y, study = study,
ncomp = 2, test.keepX = seq(1, 100, 1),
method = 'mint.splsda', #Specify the method
measure = 'BER',
dist = "centroids.dist",
nrepeat = 3)
#tune.mint.splsda.stem # Lists the different types of outputs
# Mean error rate per component and per tested keepX value:
#tune.mint.splsda.stem$error.rate[1:5,]
The optimal number of variables to select on each specified component:
tune.mint.splsda.stem$choice.keepX
## comp1 comp2
## 24 1
plot(tune.mint.splsda.stem)
keepX
in MINT sPLS-DA performed on the stemcells
gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX
values (x-axis). The diamond indicates the optimal keepX
value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test across the studies." width="75%" />
Figure 51: Tuning keepX
in MINT sPLS-DA performed on the stemcells
gene expression data. Each coloured line represents the balanced error rate (y-axis) per component across all tested keepX
values (x-axis). The diamond indicates the optimal keepX
value on a particular component which achieves the lowest classification error rate as determined with a one-sided \(t-\)test across the studies.
The tuning plot in Figure 51 indicates the optimal number of variables to select on component 1 (24) and on component 2 (1). In fact, whilst the BER decreases with the addition of component 2, the standard deviation remains large, and thus only one component is optimal. However, the addition of this second component is useful for the graphical outputs, and also to attempt to discriminate the hESC and hiPCS cell types.
Note:
keepX
values.Following the tuning results, our final model is as follows (we still choose a model with two components in order to obtain 2D graphics):
final.mint.splsda.stem <- mint.splsda(X = X, Y = Y, study = study, ncomp = 2,
keepX = tune.mint.splsda.stem$choice.keepX)
#mint.splsda.stem.final # Lists useful functions that can be used with a MINT object
The samples can be projected on the global components or alternatively using the partial components from each study (Fig 52).
plotIndiv(final.mint.splsda.stem, study = 'global', legend = TRUE,
title = 'Stem cells, MINT sPLS-DA',
subtitle = 'Global', ellipse = T)
plotIndiv(final.mint.splsda.stem, study = 'all.partial', legend = TRUE,
title = 'Stem cells, MINT sPLS-DA',
subtitle = paste("Study",1:4))
stemcells
gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC." width="50%" />stemcells
gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC." width="50%" />
Figure 52: Sample plots from the MINT sPLS-DA performed on the stemcells
gene expression data. Samples are projected into the space spanned by the first two components. Samples are coloured by their cell types and symbols indicate study membership. (a) Global components from the model with 95% ellipse confidence intervals around each sample class. (b) Partial components per study show a good agreement across studies. Component 1 discriminates fibroblast vs. the rest, component 2 discriminates further hESC vs. hiPSC.
The visualisation of the partial components enables us to examine each study individually and check that the model is able to extract a good agreement between studies.
We can examine our molecular signature selected with MINT sPLS-DA. The correlation circle plot, presented in Module 2, highlights the contribution of each selected transcript to each component (close to the large circle), and their correlation (clusters of variables) in Figure 53:
plotVar(final.mint.splsda.stem)
stemcells
gene expression data to examine the association of the genes selected on the first two components. We mainly observe two groups of genes, either positively or negatively associated with component 1 along the x-axis. This graphic should be interpreted in conjunction with the sample plot." width="75%" />
Figure 53: Correlation circle plot representing the genes selected by MINT sPLS-DA performed on the stemcells
gene expression data to examine the association of the genes selected on the first two components. We mainly observe two groups of genes, either positively or negatively associated with component 1 along the x-axis. This graphic should be interpreted in conjunction with the sample plot.
We observe a subset of genes that are strongly correlated and negatively associated to component 1 (negative values on the x-axis), which are likely to characterise the groups of samples hiPSC and hESC, and a subset of genes positively associated to component 1 that may characterise the fibroblast samples (and are negatively correlated to the previous group of genes).
Note:
var.name
argument to show gene name ID, as shown in Module 3 for PLS-DA.The Clustered Image Map represents the expression levels of the gene signature per sample, similar to a PLS-DA object (see Module 3). Here we use the default Euclidean distance and Complete linkage in Figure 54 for a specific component (here 1):
# If facing margin issues, use either X11() or save the plot using the
# arguments save and name.save
cim(final.mint.splsda.stem, comp = 1, margins=c(10,5),
row.sideColors = color.mixo(as.numeric(Y)), row.names = FALSE,
title = "MINT sPLS-DA, component 1")
stemcells
gene expression data for component 1 only. A hierarchical clustering based on the gene expression levels of the selected genes on component 1, with samples in rows coloured according to cell type showing a separation of the fibroblast vs. the other cell types." width="75%" />
Figure 54: Clustered Image Map of the genes selected by MINT sPLS-DA on the stemcells
gene expression data for component 1 only. A hierarchical clustering based on the gene expression levels of the selected genes on component 1, with samples in rows coloured according to cell type showing a separation of the fibroblast vs. the other cell types.
As expected and observed from the sample plot Figure 52, we observe in the CIM that the expression of the genes selected on component 1 discriminates primarily the fibroblast vs. the other cell types.
Relevance networks can also be plotted for a PLS-DA object, but would only show the association between the selected genes and the cell type (dummy variable in \(\boldsymbol Y\) as an outcome category) as shown in Figure 55. Only the variables selected on component 1 are shown (comp = 1
):
# If facing margin issues, use either X11() or save the plot using the
# arguments save and name.save
network(final.mint.splsda.stem, comp = 1,
color.node = c(color.mixo(1), color.mixo(2)),
shape.node = c("rectangle", "circle"))
stemcells
gene expression data for component 1 only. Associations between variables from \(\boldsymbol X\) and the dummy matrix \(\boldsymbol Y\) are calculated as detailed in Extra Reading material from Module 2. Edges indicate high or low association between the genes and the different cell types." width="75%" />
Figure 55: Relevance network of the genes selected by MINT sPLS-DA performed on the stemcells
gene expression data for component 1 only. Associations between variables from \(\boldsymbol X\) and the dummy matrix \(\boldsymbol Y\) are calculated as detailed in Extra Reading material from Module 2. Edges indicate high or low association between the genes and the different cell types.
The selectVar()
function outputs the selected transcripts on the first component along with their loading weight values. We consider variables as important in the model when their absolute loading weight value is high. In addition to this output, we can compare the stability of the selected features across studies using the perf()
function, as shown in PLS-DA in Module 3.
# Just a head
head(selectVar(final.mint.plsda.stem, comp = 1)$value)
## value.var
## ENSG00000181449 -0.09764220
## ENSG00000123080 0.09606034
## ENSG00000110721 -0.09595070
## ENSG00000176485 -0.09457383
## ENSG00000184697 -0.09387322
## ENSG00000102935 -0.09370298
The plotLoadings()
function displays the coefficient weight of each selected variable in each study and shows the agreement of the gene signature across studies (Figure 56). Colours indicate the class in which the mean expression value of each selected gene is maximal. For component 1, we obtain:
plotLoadings(final.mint.splsda.stem, contrib = "max", method = 'mean', comp=1,
study="all.partial", title="Contribution on comp 1",
subtitle = paste("Study",1:4))
stemcells
data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types." width="75%" />
Figure 56: Loading plots of the genes selected by the MINT sPLS-DA performed on the stemcells
data, on component 1 per study. Each plot represents one study, and the variables are coloured according to the cell type they are maximally expressed in, on average. The length of the bars indicate the loading coefficient values that define the component. Several genes distinguish between fibroblast and the other cell types, and are consistently overexpressed in these samples across all studies. We observe slightly more variability in whether the expression levels of the other genes are more indicative of hiPSC or hESC cell types.
Several genes are consistently over-expressed on average in the fibroblast samples in each of the studies, however, we observe a less consistent pattern for the other genes that characterise hiPSC} and hESC. This can be explained as the discrimination between both classes is challenging on component 1 (see sample plot in Figure 52).
We assess the performance of the MINT sPLS-DA model with the perf()
function. Since the previous tuning was conducted with the distance centroids.dist
, the same distance is used to assess the performance of the final model. We do not need to specify the argument nrepeat
as we use LOGOCV in the function.
set.seed(123) # For reproducible results here, remove for your own study
perf.mint.splsda.stem.final <- perf(final.mint.plsda.stem, dist = 'centroids.dist')
perf.mint.splsda.stem.final$global.error
## $BER
## centroids.dist
## comp1 0.3333333
## comp2 0.3320000
##
## $overall
## centroids.dist
## comp1 0.456
## comp2 0.392
##
## $error.rate.class
## $error.rate.class$centroids.dist
## comp1 comp2
## Fibroblast 0.0000000 0.0000000
## hESC 0.1891892 0.4594595
## hiPSC 0.8620690 0.5517241
The classification error rate per class is particularly insightful to understand which cell types are difficult to classify, hESC and hiPS - whose mixture can be explained for biological reasons.
An AUC plot for the integrated data can be obtained using the function auroc()
(Fig 57).
Remember that the AUC incorporates measures of sensitivity and specificity for every possible cut-off of the predicted dummy variables. However, our PLS-based models rely on prediction distances, which can be seen as a determined optimal cut-off. Therefore, the ROC and AUC criteria may not be particularly insightful in relation to the performance evaluation of our supervised multivariate methods, but can complement the statistical analysis (from Rohart, Gautier, Singh, and Lê Cao (2017)).
auroc(final.mint.splsda.stem, roc.comp = 1)
We can also obtain an AUC plot per study by specifying the argument roc.study
:
auroc(final.mint.splsda.stem, roc.comp = 1, roc.study = '2')
stemcells
gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types." width="50%" />stemcells
gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types." width="50%" />
Figure 57: ROC curve and AUC from the MINT sPLS-DA performed on the stemcells
gene expression data for global and specific studies, averaged across one-vs-all comparisons. Numerical outputs include the AUC and a Wilcoxon test \(p-\)value for each ‘one vs. other’ class comparison that are performed per component. This output complements the sPLS-DA performance evaluation but should not be used for tuning (as the prediction process in sPLS-DA is based on prediction distances, not a cutoff that maximises specificity and sensitivity as in ROC). The plot suggests that the selected features are more accurate in classifying fibroblasts versus the other cell types, and less accurate in distinguishing hESC versus the other cell types or hiPSC versus the other cell types.
We use the predict()
function to predict the class membership of new test samples from an external study. We provide an example where we set aside a particular study, train the MINT model on the remaining three studies, then predict on the test study. This process exactly reflects the inner workings of the tune()
and perf()
functions using LOGOCV.
Here during our model training on the three studies only, we assume we have performed the tuning steps described in this case study to choose ncomp
and keepX
(here set to arbitrary values to avoid overfitting):
# We predict on study 3
indiv.test <- which(study == "3")
# We train on the remaining studies, with pre-tuned parameters
mint.splsda.stem2 <- mint.splsda(X = X[-c(indiv.test), ],
Y = Y[-c(indiv.test)],
study = droplevels(study[-c(indiv.test)]),
ncomp = 1,
keepX = 30)
mint.predict.stem <- predict(mint.splsda.stem2, newdata = X[indiv.test, ],
dist = "centroids.dist",
study.test = factor(study[indiv.test]))
# Store class prediction with a model with 1 comp
indiv.prediction <- mint.predict.stem$class$centroids.dist[, 1]
# The confusion matrix compares the real subtypes with the predicted subtypes
conf.mat <- get.confusion_matrix(truth = Y[indiv.test],
predicted = indiv.prediction)
conf.mat
## predicted.as.Fibroblast predicted.as.hESC predicted.as.hiPSC
## Fibroblast 3 0 0
## hESC 0 4 4
## hiPSC 2 2 6
Here we have considered a trained model with one component, and compared the cell type prediction for the test study 3 with the known cell types. The classification error rate is relatively high, but potentially could be improved with a proper tuning, and a larger number of studies in the training set.
# Prediction error rate
(sum(conf.mat) - sum(diag(conf.mat)))/sum(conf.mat)
## [1] 0.3809524
## [1] '6.28.0'
## R version 4.4.0 alpha (2024-03-27 r86216)
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##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] mixOmics_6.28.0 ggplot2_3.5.0 lattice_0.22-6 MASS_7.3-60.2
## [5] knitr_1.45 BiocStyle_2.32.0
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## loaded via a namespace (and not attached):
## [1] tidyr_1.3.1 sass_0.4.9 utf8_1.2.4
## [4] generics_0.1.3 stringi_1.8.3 digest_0.6.35
## [7] magrittr_2.0.3 evaluate_0.23 grid_4.4.0
## [10] RColorBrewer_1.1-3 bookdown_0.38 fastmap_1.1.1
## [13] plyr_1.8.9 jsonlite_1.8.8 Matrix_1.7-0
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## [22] scales_1.3.0 codetools_0.2-19 jquerylib_0.1.4
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## [34] reshape2_1.4.4 BiocParallel_1.38.0 dplyr_1.1.4
## [37] colorspace_2.1-0 corpcor_1.6.10 vctrs_0.6.5
## [40] R6_2.5.1 magick_2.8.3 matrixStats_1.2.0
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## [58] htmltools_0.5.8 igraph_2.0.3 labeling_0.4.3
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