2.1 Citation info

If you use pcaExplorer for your analysis, please cite it as here below:

citation("pcaExplorer")

To cite package 'pcaExplorer' in publications use:

  Federico Marini (2018). pcaExplorer: Interactive Visualization of
  RNA-seq Data Using a Principal Components Approach. R package
  version 2.6.0. https://github.com/federicomarini/pcaExplorer

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach},
    author = {Federico Marini},
    year = {2018},
    note = {R package version 2.6.0},
    url = {https://github.com/federicomarini/pcaExplorer},
  }

4.1 App settings

Here are the parameters that set input values for most of the tabs. By hovering over with the mouse, the user can receive additional information on how to set the parameter, powered by the shinyBS package.

• x-axis PC - Select the principal component to display on the x axis
• y-axis PC - Select the principal component to display on the y axis
• Group/color by - Select the group of samples to stratify the analysis. Can also assume multiple values.
• Nr of (most variant) genes - Number of genes to select for computing the principal components. The top n genes are selected ranked by their variance inter-samples
• Alpha - Color transparency for the plots. Can assume values from 0 (transparent) to 1 (opaque)
• Labels size - Size of the labels for the samples in the principal components plots
• Points size - Size of the points to be plotted in the principal components plots
• Variable name size - Size of the labels for the genes PCA - correspond to the samples names
• Scaling factor - Scale value for resizing the arrow corresponding to the variables in the PCA for the genes. It should be used for mere visualization purposes
• Color palette - Select the color palette to be used in the principal components plots. The number of colors is selected automatically according to the number of samples and to the levels of the factors of interest and their interactions
• Plot style for gene counts - Plot either boxplots or violin plots, with jittered points superimposed

4.2 Plot export settings

Width and height for the figures to export are input here in cm.

Additional controls available in the single tabs are also assisted by tooltips that show on hovering the mouse. Normally they are tightly related to the plot/output they are placed nearby.

6.1 Data Upload

These file input controls are available when no dds or countmatrix + coldata are provided. Additionally, it is possible to upload the annotation data frame.

When the objects are already passed as parameters, a brief overview/summary for them is displayed.

6.2 Instructions

This is where you most likely are reading this text (otherwise in the package vignette).

6.3 Counts Table

Interactive tables for the raw, normalized or (r)log-transformed counts are shown in this tab. The user can also generate a sample-to-sample correlation scatter plot with the selected data.

6.4 Data Overview

This panel displays information on the objects in use, either passed as parameters or generated from the count matrix provided. Displayed information comprise the design metadata, a sample to sample distance heatmap, the number of million of reads per sample and some basic summary for the counts.

6.5 Samples View

This panel displays the PCA projections of sample expression profiles onto any pair of components, a scree plot, a zoomed PCA plot, a plot of the genes with top and bottom loadings. Additionally, this section presents a PCA plot where it is possible to remove samples deemed to be outliers in the analysis, which is very useful to check the effect of excluding them. If needed, an interactive 3D visualization of the principal components is also available.

6.6 Genes View

This panel displays the PCA projections of genes abundances onto any pair of components, with samples as biplot variables, to identify interesting groups of genes. Zooming is also possible, and clicking on single genes, a boxplot is returned, grouped by the factors of interest. A static and an interactive heatmap are provided, including the subset of selected genes, also displayed as (standardized) expression profiles across the samples. These are also reported in datatable objects, accessible in the bottom part of the tab.

6.7 GeneFinder

The user can search and display the expression values of a gene of interest, either by ID or gene name, as provided in the annotation. A handy panel for quick screening of shortlisted genes, again grouped by the factors of interest. The graphic can be readily exported as it is, and this can be iterated on a shortlisted set of genes. For each of them, the underlying data is displayed in an interactive table, also exportable with a click.

6.8 PCA2GO

This panel shows the functional annotation of the principal components, with GO functions enriched in the genes with high loadings on the selected principal components. It allows for the live computing of the object, that can otherwise provided as a parameter when launching the app. The panel displays a PCA plot for the samples, surrounded on each side by the tables with the functions enriched in each component and direction.

6.9 Multifactor Exploration

This panel allows for the multifactor exploration of datasets with 2 or more experimental factors. The user has to select first the two factors and the levels for each. Then, it is possible to combine samples from Factor1-Level1 in the selected order by clicking on each sample name, one for each level available in the selected Factor2. In order to build the matrix, an equal number of samples for each level of Factor 1 is required, to keep the design somehow balanced. A typical case for choosing factors 1 and 2 is for example when different conditions and tissues are present.

Once constructed, a plot is returned that tries to represent simultaneously the effect of the two factors on the data. Each gene is represented by a dot-line-dot structure, with the color that is indicating the tissue (factor 2) where the gene is mostly expressed. Each gene has two dots, one for each condition level (factor 1), and the position of the points is dictated by the scores of the principal components calculated on the matrix object. The line connecting the dots is darker when the tissue where the gene is mostly expressed varies throughout the conditions.

This representation is under active development, and it is promising for identifying interesting sets or clusters of genes according to their behavior on the Principal Components subspaces. Zooming and exporting of the underlying genes is also allowed by brushing on the main plot.

6.10 Report Editor

The report editor is the backbone for generating and editing the interactive report on the basis of the uploaded data and the current state of the application. General Markdown options and Editor options are available, and the text editor, based on the shinyAce package, contains a comprehensive template report, that can be edited to the best convenience of the user.

The editor supports R code autocompletion, making it easy to add new code chunks for additional sections. A preview is available in the tab itself, and the report can be generated, saved and subsequently shared with simple mouse clicks.

6.11 About

Contains general information on pcaExplorer, including the developer’s contact, the link to the development version in Github, as well as the output of sessionInfo, to use for reproducibility sake - or bug reporting. Information for citing pcaExplorer is also reported.

9.1 pcaplot

pcaplot plots the sample PCA for DESeqTransform objects, such as rlog-transformed data. This is the workhorse of the Samples View tab

pcaplot(rld_airway,intgroup = c("cell","dex"),ntop = 1000,
        pcX = 1, pcY = 2, title = "airway dataset PCA on samples - PC1 vs PC2")

# on a different set of principal components...
pcaplot(rld_airway,intgroup = c("dex"),ntop = 1000,
        pcX = 1, pcY = 4, title = "airway dataset PCA on samples - PC1 vs PC4",
        ellipse = TRUE)

9.2 pcaplot3d

Same as for pcaplot, but it uses the threejs package for the 3d interactive view.

pcaplot3d(rld_airway,intgroup = c("cell","dex"),ntop = 1000,
        pcX = 1, pcY = 2, pcZ = 3)
# will open up in the viewer

9.3 pcascree

pcascree produces a scree plot of the PC computed on the samples. A prcomp object needs to be passed as main argument

pcaobj_airway <- prcomp(t(assay(rld_airway)))
pcascree(pcaobj_airway,type="pev",
         title="Proportion of explained proportion of variance - airway dataset")

9.4 correlatePCs and plotPCcorrs

correlatePCs and plotPCcorrs respectively compute and plot significance of the (cor)relation of each covariate versus a principal component. The input for correlatePCs is a prcomp object

res_pcairway <- correlatePCs(pcaobj_airway,colData(dds_airway))

res_pcairway

     SampleName       cell        dex albut       Run avgLength Experiment
PC_1  0.4288799 0.68227033 0.02092134    NA 0.4288799 0.2554109  0.4288799
PC_2  0.4288799 0.11161023 0.56370286    NA 0.4288799 0.1993592  0.4288799
PC_3  0.4288799 0.10377716 0.38647623    NA 0.4288799 0.1864725  0.4288799
PC_4  0.4288799 0.08331631 0.56370286    NA 0.4288799 0.4635148  0.4288799
        Sample BioSample
PC_1 0.4288799 0.4288799
PC_2 0.4288799 0.4288799
PC_3 0.4288799 0.4288799
PC_4 0.4288799 0.4288799

plotPCcorrs(res_pcairway)

9.5 hi_loadings

hi_loadings extracts and optionally plots the genes with the highest loadings

# extract the table of the genes with high loadings
hi_loadings(pcaobj_airway,topN = 10,exprTable=counts(dds_airway))

                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
ENSG00000143127         11        108         24        485         41
ENSG00000168309         12        274         35        451          1
ENSG00000101347       1632      17126       2098      19694       1598
ENSG00000211445        916      15749       3142      24057       1627
ENSG00000096060        260       4652        381       3875        601
ENSG00000163884         70       1325         52        702         36
ENSG00000171819          4         50         19        543          1
ENSG00000127954         13        247         25        889          2
ENSG00000152583         62       2040         99       1172        100
ENSG00000109906          4        739          5        429          1
ENSG00000162692        914         62       1192         55       1359
ENSG00000178695       4746        830       4805        414       5321
ENSG00000214814        312         24        193         28        501
ENSG00000164742       1506        347        275         14        137
ENSG00000138316       1327        207       1521        118       1962
ENSG00000123610        444        136        303         36       1170
ENSG00000124766       2483        406       2057        185       2829
ENSG00000105989        562         47       1575        106        106
ENSG00000013293        268         23        435         56        558
ENSG00000146250        330         41        907         89        720
                SRR1039517 SRR1039520 SRR1039521
ENSG00000143127        607         77        660
ENSG00000168309         65          4        193
ENSG00000101347      17697       1683      32036
ENSG00000211445      16274       1741      24883
ENSG00000096060       5493        154       4118
ENSG00000163884        487         34       1355
ENSG00000171819         10         14       1067
ENSG00000127954        199         20        462
ENSG00000152583       1924         79       2138
ENSG00000109906        581         12       1113
ENSG00000162692        171        646         31
ENSG00000178695       1391       4411        606
ENSG00000214814         65        789         76
ENSG00000164742         37        475         56
ENSG00000138316        618       1045        152
ENSG00000123610        195        473         37
ENSG00000124766        870       1851        301
ENSG00000105989         24        382         46
ENSG00000013293         75        562         74
ENSG00000146250        123        439         60

# or alternatively plot the values
hi_loadings(pcaobj_airway,topN = 10,annotation = annotation_airway)

9.6 genespca

genespca computes and plots the principal components of the genes, eventually displaying the samples as in a typical biplot visualization. This is the function in action for the Genes View tab

groups_airway <- colData(dds_airway)$dex
cols_airway <- scales::hue_pal()(2)[groups_airway]
# with many genes, do not plot the labels of the genes
genespca(rld_airway,ntop=5000,
         choices = c(1,2),
         arrowColors=cols_airway,groupNames=groups_airway,
         alpha = 0.2,
         useRownamesAsLabels=FALSE,
         varname.size = 5
        )

# with a smaller number of genes, plot gene names included in the annotation
genespca(rld_airway,ntop=100,
         choices = c(1,2),
         arrowColors=cols_airway,groupNames=groups_airway,
         alpha = 0.7,
         varname.size = 5,
         annotation = annotation_airway
        )

9.7 topGOtable

topGOtable is a convenient wrapper for extracting functional GO terms enriched in a subset of genes (such as the differentially expressed genes), based on the algorithm and the implementation in the topGO package

# example not run due to quite long runtime
dds_airway <- DESeq(dds_airway)
res_airway <- results(dds_airway)
res_airway$symbol <- mapIds(org.Hs.eg.db,
                            keys=row.names(res_airway),
                            column="SYMBOL",
                            keytype="ENSEMBL",
                            multiVals="first")
res_airway$entrez <- mapIds(org.Hs.eg.db,
                            keys=row.names(res_airway),
                            column="ENTREZID",
                            keytype="ENSEMBL",
                            multiVals="first")
resOrdered <- as.data.frame(res_airway[order(res_airway$padj),])
head(resOrdered)
# extract DE genes
de_df <- resOrdered[resOrdered$padj < .05 & !is.na(resOrdered$padj),]
de_symbols <- de_df$symbol
# extract background genes
bg_ids <- rownames(dds_airway)[rowSums(counts(dds_airway)) > 0]
bg_symbols <- mapIds(org.Hs.eg.db,
                     keys=bg_ids,
                     column="SYMBOL",
                     keytype="ENSEMBL",
                     multiVals="first")
# run the function
topgoDE_airway <- topGOtable(de_symbols, bg_symbols,
                             ontology = "BP",
                             mapping = "org.Hs.eg.db",
                             geneID = "symbol")

9.8 pca2go

pca2go provides a functional interpretation of the principal components, by extracting the genes with the highest loadings for each PC, and then runs internally topGOtable on them for efficient functional enrichment analysis. Needs a DESeqTransform object as main parameter

pca2go_airway <- pca2go(rld_airway,
                        annotation = annotation_airway,
                        organism = "Hs",
                        ensToGeneSymbol = TRUE,
                        background_genes = bg_ids)
# for a smooth interactive exploration, use DT::datatable
datatable(pca2go_airway$PC1$posLoad)
# display it in the normal R session...
head(pca2go_airway$PC1$posLoad)
# ... or use it for running the app and display in the dedicated tab
pcaExplorer(dds_airway,rld_airway,
            pca2go = pca2go_airway,
            annotation = annotation_airway)

9.9 limmaquickpca2go

limmaquickpca2go is an alternative to pca2go, used in the live running app, thanks to its fast implementation based on the limma::goana function.

goquick_airway <- limmaquickpca2go(rld_airway,
                                   pca_ngenes = 10000,
                                   inputType = "ENSEMBL",
                                   organism = "Hs")
# display it in the normal R session...
head(goquick_airway$PC1$posLoad)
# ... or use it for running the app and display in the dedicated tab
pcaExplorer(dds_airway,rld_airway,
            pca2go = goquick_airway,
            annotation = annotation_airway)

9.10 makeExampleDESeqDataSet_multifac

makeExampleDESeqDataSet_multifac constructs a simulated DESeqDataSet of Negative Binomial dataset from different conditions. The fold changes between the conditions can be adjusted with the betaSD_condition betaSD_tissue arguments

dds_simu <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3,betaSD_tissue = 0.5)
dds_simu

class: DESeqDataSet 
dim: 1000 12 
metadata(1): version
assays(1): counts
rownames(1000): gene1 gene2 ... gene999 gene1000
rowData names(4): trueIntercept trueBeta_condition trueBeta_tissue
  trueDisp
colnames(12): sample1 sample2 ... sample11 sample12
colData names(2): condition tissue

dds2_simu <- makeExampleDESeqDataSet_multifac(betaSD_condition = 0.5,betaSD_tissue = 2)
dds2_simu

class: DESeqDataSet 
dim: 1000 12 
metadata(1): version
assays(1): counts
rownames(1000): gene1 gene2 ... gene999 gene1000
rowData names(4): trueIntercept trueBeta_condition trueBeta_tissue
  trueDisp
colnames(12): sample1 sample2 ... sample11 sample12
colData names(2): condition tissue

rld_simu <- rlogTransformation(dds_simu)
rld2_simu <- rlogTransformation(dds2_simu)
pcaplot(rld_simu,intgroup = c("condition","tissue")) + 
  ggplot2::ggtitle("Simulated data - Big condition effect, small tissue effect")

pcaplot(rld2_simu,intgroup = c("condition","tissue")) + 
  ggplot2::ggtitle("Simulated data - Small condition effect, bigger tissue effect")

9.11 distro_expr

Plots the distribution of expression values, either with density lines, boxplots or violin plots.

distro_expr(rld_airway,plot_type = "density")
distro_expr(rld_airway,plot_type = "violin")

distro_expr(rld_airway,plot_type = "boxplot")

9.12 geneprofiler

Plots the profile expression of a subset of genes, optionally as standardized values

dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3,betaSD_tissue = 1)
rlt <- DESeq2::rlogTransformation(dds)
set.seed(42)
geneprofiler(rlt,paste0("gene",sample(1:1000,20)), plotZ = FALSE)

you provided 20 unique identifiers
20 out of 20 provided genes were found in the data

9.13 get_annotation and get_annotation_orgdb

These two wrapper functions retrieve the latest annotations for the dds object, to be used in the call to the pcaExplorer function. They use respectively the biomaRt package and the org.XX.eg.db packages.

anno_df_biomart <- get_annotation(dds = dds_airway,
                                  biomart_dataset = "hsapiens_gene_ensembl",
                                  idtype = "ensembl_gene_id")

anno_df_orgdb <- get_annotation_orgdb(dds = dds_airway,
                                      orgdb_species = "org.Hs.eg.db",
                                      idtype = "ENSEMBL")

9.14 pair_corr

Plots the pairwise scatter plots and computes the correlation coefficient on the expression matrix provided.

# use a subset of the counts to reduce plotting time, it can be time consuming with many samples
pair_corr(counts(dds_airway)[1:100,])