mixOmics 6.36.0
multidrug study
library(BiocParallel)
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.
mixOmicsEach 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 negatively both to PC1 and PC2, and a cluster of ABCC12 and ABCD2 that contributes negatively to PC1 and positively to 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 10 10
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 10 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$X
## PC1 PC2
## 0.11716935 0.09609604
Overall when considering two components, we lose approximately 1.5 % of explained variance compared to a full PCA, but the aim of this analysis is to identify key transporters driving the variation in the data, as we show below.
We first examine the sPCA sample plot:
plotIndiv(final.spca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$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
## ABCB5 0.4948993
## ABCA9 0.4923489
## ABCD1 0.4290877
## ABCC2 0.4128214
## ABCA3 -0.3334742
## ABCD2 -0.1209625
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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" 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$summary
## feature comp mean sd
## 1 Y 1 0.6916155 0.03566959
# To extract values across all repeats:
# perf.pls1.liver$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 0.6108651 0.02002857
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 = 2)
)
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
## 0.19812868 0.08139331
##
## $Y
## comp1 comp2
## 0.3650052 0.2171062
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
## 21.22874 19.70991 14.91812 14.38746 13.63174 13.01466
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]
| x |
|---|
| 0.90 |
| 1.00 |
| 0.78 |
| 0.96 |
| 0.92 |
| 0.98 |
| 0.88 |
| 0.86 |
| 0.82 |
| 0.74 |
| 0.58 |
| 0.68 |
| 0.66 |
| 0.66 |
| 0.46 |
| NA |
| NA |
| NA |
| NA |
| NA |
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
## 0.56303442 0.26062500 0.04599638
As well as the error rate per class across each component:
perf.final.plsda.srbct$error.rate.class$max.dist
## comp1 comp2 comp3
## EWS 0.2913043 0.1000 0.09565217
## BL 0.8375000 0.5125 0.00000000
## NB 0.4083333 0.3750 0.03333333
## RMS 0.7150000 0.0550 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 0.5964946 0.2991123 0.050108696 0.009945652
## 2 0.5537500 0.3007065 0.031358696 0.009945652
## 3 0.5301630 0.2971920 0.023858696 0.007608696
## 4 0.5211413 0.2893478 0.016268116 0.011032609
## 5 0.5208696 0.2822192 0.012681159 0.012119565
## 6 0.5146014 0.2798822 0.008514493 0.011032609
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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" 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$choice.keepX
## comp1 comp2 comp3 comp4
## 50 90 40 3
Here is our final sPLS-DA model with three components and the optimal keepX obtained from our tuning step.
You can choose to skip the tuning step, and input your arbitrarily chosen parameters in the following code (simply specify your own ncomp and keepX values):
# Optimal number of components based on t-tests on the error rate
ncomp <- tune.splsda.srbct$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
## 50 90 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 0.42222222
## comp2 0.19365079
## comp3 0.01269841
##
## $BER
## max.dist
## comp1 0.507264493
## comp2 0.271222826
## comp3 0.008695652
We can also examine the error rate per class:
perf.splsda.srbct$error.rate.class
## $max.dist
## comp1 comp2 comp3
## EWS 0.0173913 0.0173913 0.03478261
## BL 0.5250000 0.4125000 0.00000000
## NB 0.9666667 0.5750000 0.00000000
## RMS 0.5200000 0.0800000 0.00000000
These results can be compared with the performance of PLS-DA and show the benefits of variable selection to not only obtain a parsimonious model, but also to improve the classification error rate (overall and per class).
During the repeated cross-validation process in perf() we can record how often the same variables are selected across the folds. This information is important to answer the question: How reproducible is my gene signature when the training set is perturbed via cross-validation?.
par(mfrow=c(1,2))
# For component 1
stable.comp1 <- perf.splsda.srbct$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 0.3043756 g123 0.48
## g846 0.2678550 g846 0.48
## g758 0.2372147 g758 0.50
## g836 0.2360363 g836 0.48
## g1606 0.2350958 g1606 0.48
## g335 0.2334439 g335 0.48
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) 0.5576 4.493e-01
## BL vs Other(s) 1.0000 5.586e-06
## NB vs Other(s) 0.5180 8.473e-01
## RMS vs Other(s) 0.6814 2.125e-02
##
## $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) 0.6569 9.289e-02
## RMS vs Other(s) 0.9698 2.442e-09
##
## $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$WeightedVote
## max.dist centroids.dist mahalanobis.dist
## Overall.ER 3 3 3
## Overall.BER 3 2 4
Thus, here we choose our final ncomp value:
ncomp <- perf.diablo.tcga$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).plotDiabloplotDiablo() 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.
plotIndivThe 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.plotArrowIn 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.
plotVarThe 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.
circosPlotThe 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.
networkRelevance 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")
plotLoadingsplotLoadings() 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.
cimDiabloThe 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 0.02888889 0.04444444
## Her2 0.23000000 0.12333333
## LumA 0.05066667 0.01200000
## Overall.ER 0.08000000 0.04400000
## Overall.BER 0.10318519 0.05992593
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
## $centroids.dist
## comp1 comp2
## Basal 0.008888889 0.04444444
## Her2 0.166666667 0.10666667
## LumA 0.050666667 0.01200000
## Overall.ER 0.061333333 0.04066667
## Overall.BER 0.075407407 0.05437037
Compared to the previous majority vote output, we can see that the classification accuracy is slightly better on component 2 for the subtype Her2.
An AUC plot per block is plotted using the function auroc(). We have already mentioned in Module 3 for PLS-DA, the interpretation of this output may not be particularly insightful in relation to the performance evaluation of our methods, but can complement the statistical analysis. For example, here for the miRNA data set once we have reached component 2 (Figure 47):
auc.diablo.tcga <- auroc(diablo.tcga, roc.block = "miRNA", roc.comp = 2,
print = FALSE)
Figure 47: ROC and AUC based on multiblock sPLS-DA performed on the breast.TCGA study for the miRNA data set after 2 components. The function calculates the ROC curve and AUC for one class vs. the others. If we set print = TRUE, the Wilcoxon test p-value that assesses the differences between the predicted components from one class vs. the others is output.
Figure 47 shows that the Her2 subtype is the most difficult to classify with multiblock sPLS-DA compared to the other subtypes.
The predict() function associated with a block.splsda() object predicts the class of samples from an external test set. In our specific case, one data set is missing in the test set but the method can still be applied. We need to ensure the names of the blocks correspond exactly to those from the training set:
# Prepare test set data: here one block (proteins) is missing
data.test.tcga <- list(mRNA = breast.TCGA$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%" />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] '6.36.0'
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