esetVis
Visualizations of expressionSet Bioconductor object

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Vignette of the esetVis package

Vignette of the esetVis package
In esetVis: Visualizations of expressionSet Bioconductor object

Introduction

This document explains the functionalities available in the esetVis package.

This package contains wrapper functions for three types of visualization: spectral map[@spm], tsne[@tsne] and linear discriminant analysis[@ldaFisher] for data contained in a expressionSet Bioconductor (or an SummarizedExperiment) object. The visualizations are available in two types: static, via the ggplot2 package or interactive, via the ggvis or rbokeh packages.

    library(knitr)
    opts_chunk$set(echo = TRUE, results = 'asis', warning = FALSE, 
        error = FALSE, message = FALSE, cache = FALSE,
        fig.width = 8, fig.height = 7,
        #out.width = '0.7\\textwidth', 
        fig.align = 'center')#, out.height = 0.5\\textwidth', fig.path = 'graphs/') 
    options(width = 170)#, stringsAsFactors = FALSE
    options(warn = 1)#instead of warn = 0 by default -> to have place where warnings occur in the call to Sweave function

    library(esetVis)
#   library(xtable)
    library(pander)

Example dataset

ExpressionSet object

The ALL dataset contains microarray results from 128 patients with acute lymphoblastic leukemia (ALL). The data is contained in a Bioconductor ExpressionSet object. Extra gene annotation is added to the object, via the annotation package hgu95av2.db.

    library(ALL)
    data(ALL)

    # to get gene annotation from probe IDs (from the paper HGU95aV2 gene chip was used)
    library("hgu95av2.db")
    library("AnnotationDbi")
    probeIDs <- featureNames(ALL)
    geneInfo <- AnnotationDbi::select(hgu95av2.db, probeIDs, 
        c("ENTREZID", "SYMBOL", "GENENAME"), "PROBEID")
    # 482 on the 12625 probe IDs don't have ENTREZ ID/SYMBOL/GENENAME

    # remove genes with duplicated annotation: 1214
    geneInfoWthtDuplicates <- geneInfo[!duplicated(geneInfo$PROBEID), ]

    # remove genes without annotation: 482
    genesWthtAnnotation <- rowSums(is.na(geneInfoWthtDuplicates)) > 0
    geneInfoWthtDuplicatesAndWithAnnotation <- geneInfoWthtDuplicates[!genesWthtAnnotation, ]

    probeIDsWithAnnotation <- featureNames(ALL)[featureNames(ALL) %in% 
        geneInfoWthtDuplicatesAndWithAnnotation$PROBEID]
    ALL <- ALL[probeIDsWithAnnotation, ]

    fData <- geneInfoWthtDuplicatesAndWithAnnotation[
        match(probeIDsWithAnnotation, geneInfoWthtDuplicatesAndWithAnnotation$PROBEID), ]
    rownames(fData) <- probeIDsWithAnnotation
    fData(ALL) <- fData

    # grouping variable: B = B-cell, T = T-cell
    groupingVariable <- pData(ALL)$BT

    # create custom palette
    colorPalette <- c("dodgerblue", 
        colorRampPalette(c("white","dodgerblue2", "darkblue"))(5)[-1], 
        "red", colorRampPalette(c("white", "red3", "darkred"))(5)[-1])
    names(colorPalette) <- levels(groupingVariable)
    color <- groupingVariable; levels(color) <- colorPalette

    # reformat type of the remission
    remissionType <- ifelse(is.na(ALL$remission), "unknown", as.character(ALL$remission))
    ALL$remissionType <- factor(remissionType,
        levels = c("unknown", "CR", "REF"), 
        labels = c("unknown", "achieved", "refractory"))

    fvarMetadata(ALL)$labelDescription <- paste(rownames(fvarMetadata(ALL)),
        "obtained from the Bioconductor hgu95av2.db package")   

    rm(list = c("fData", ls(pattern = "^(geneInfo|probeIDs)")));tmp <- gc(verbose = FALSE)
    #for testing: sort(sapply(ls(), function(x) object.size(get(x))))

Following tables detail some sample and gene annotation contained in the ALL ExpressionSet used in the vignette.

    varLabelsUsed <- c("cod", "sex", "age", "BT", "remissionType")
    pander(head(pData(ALL)[, varLabelsUsed]),
        caption = "subset of the **phenoData** of the ALL dataset for the first genes")

    pander(head(fData(ALL)), 
        caption = "**featureData** of the ALL dataset for the first genes")

SummarizedExperiment object

The functions of the package also supports object of class: SummarizedExperiment. Note: In this case, the functions fData, pData, exprs should be replaced by their corresponding functions rowData, colData and assay.

Spectral map: esetSpectralMap

Simple spectral map

The function esetSpectralMap creates a spectral map[@spm] for the dataset. Some default parameters are set, e.g. to print the top 10 samples and top 10 genes, to display the first two dimensions of the analysis...

The resulting biplot contains two components:

Here is an example for the ALL dataset, with the default parameters.

    print(esetSpectralMap(eset = ALL))

Additional sample information

Several annotation variables are available in the eSet.

General

Four different aesthetics [aes] can be used to display these variables:

For each of this aesthetic [aes], several parameters are available:

Custom size and transparency

Custom size and the transparency (variables sizeVar and alphaVar) can be specified:

In the example, the type and stage of the disease (variable BT) is used for coloring, the remission type for the transparency, the sex for the shape and the age for the size of the points. A custom color palette is specified via the color argument.

    print(esetSpectralMap(eset = ALL, 
        title = "Acute lymphoblastic leukemia dataset \n Spectral map \n Sample annotation (1)",
        colorVar = "BT", color = colorPalette,
        shapeVar = "sex", 
        sizeVar = "age", sizeRange = c(0.1, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topSamples = 0, topGenes = 0, cloudGenes = FALSE))

Just for the demonstration, another visualization of the same dataset, using this time a continuous variable: age for coloring and transparency, a factor for the size and BT for the shape. Because the output is a ggplot object, any additional customization not implemented via specific parameters, the plot can be modified with additional ggplot2 functions, e.g. with the color of the gradient.

    gg <- esetSpectralMap(eset = ALL, 
        title = "Acute lymphoblastic leukemia dataset \n Spectral map \n Sample annotation (2)",
        colorVar = "age",
        shapeVar = "BT", shape = 15+1:nlevels(ALL$BT),
        sizeVar = "remissionType", size = c(2, 4, 6),
        alphaVar = "age", alphaRange = c(0.2, 0.8),
        topSamples = 0, topGenes = 0, cloudGenes = TRUE)
    # change color of gradient
    library(ggplot2)
    gg <- gg + scale_color_gradientn(colours = 
        colorRampPalette(c("darkgreen", "gold"))(2)
    )
    print(gg)

Custom gene representation

Several parameters related to the gene visualization are available:

The spectral map is done only on the subset of the genes, with the number of bins, color, and legend title modified.

    print(esetSpectralMap(eset = ALL,
        psids = 1:1000,
        title = "Acute lymphoblastic leukemia dataset \n Spectral map \n Custom cloud genes",
        topSamples = 0, topGenes = 0, 
        cloudGenes = TRUE, cloudGenesColor = "red", cloudGenesNBins = 50,
        cloudGenesIncludeLegend = TRUE, cloudGenesTitleLegend = "number of features"))

Label outlying elements

Parameters

Three different kind of elements can be labelled in the plot: genes, samples and gene sets.

For each [element], several parameters are available:

Method to select top elements

The distance (sum of squared coordinates) of the gene/sample/gene set to the origin of the plot is used to rank the elements, and to extract the top 'outlying' ones.

Package used for static plot

By default (and if installed), the package ggrepel is used for text labelling (as in this vignette), to avoid overlapping labels. The text labels can also be displayed with the ggplot2 by setting the parameter packageTextLabel to ggplot2 (default ggrepel).

Example

In the example, the top genes are labelled with gene symbols (SYMBOL column of the phenoData of the eSet), and the top samples with patient identifiers (cod column of the phenoData of the eSet).

    print(esetSpectralMap(eset = ALL, 
        title = paste("Acute lymphoblastic leukemia dataset \n",
            "Spectral map \n Label outlying samples and genes"),
        colorVar = "BT", color = colorPalette,
        shapeVar = "sex",
        sizeVar = "age", sizeRange = c(2, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topGenes = 10, topGenesVar = "SYMBOL",
        topGenesCex = 2, topGenesColor = "darkgrey",
        topSamples = 15, topSamplesVar = "cod", topSamplesColor = "chocolate4",
        topSamplesCex = 3
    ))

Gene sets annotation

Genes can be grouped into biologically meaningful gene sets, which can be labelled in the biplot.

Compared to previous section, two additional parameters are available:

The geneSets can be created with the getGeneSetsForPlot function (wrapper around the getGeneSets function of the MLP package), which can extract gene sets available in the Gene Ontology (Biological Process, Molecular Function and Cellular Component) and KEGG databases. Custom gene set lists can also be provided.

In the following example, only the pathways from the GOBP database are used.

    geneSets <- getGeneSetsForPlot(
        entrezIdentifiers = fData(ALL)$ENTREZID, 
        species = "Human", 
        geneSetSource = 'GOBP',
        useDescription = TRUE)

Then this list of gene sets is provided to the esetSpectralMap function.

    print(esetSpectralMap(eset = ALL, 
        title = "Acute lymphoblastic leukemia dataset \n Spectral map \n Gene set annotation",
        colorVar = "BT", color = colorPalette,
        shapeVar = "sex",
        sizeVar = "age", sizeRange = c(2, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topGenes = 0,
        topGeneSets = 4, geneSets = geneSets, geneSetsVar = "ENTREZID", geneSetsMaxNChar = 30,
        topGeneSetsCex = 2, topGeneSetsColor = "purple"))

Note: because of the inherent hierarchical structure of the Gene Ontology database, sets of genes can be very similar, which can result in close (even overlapping) labels in the visualization.

    rm(geneSets);tmp <- gc(verbose = FALSE)

Dimensions of the biplot

In all previous plots, only the first dimensions of the principal component analysis were visualized, this can be specified via the dim parameter. The third and fourth dimensions are visualized in the next Figure. This parameter is only available for the spectral map visualization.

    print(esetSpectralMap(eset = ALL, 
        title = "Acute lymphoblastic leukemia dataset \n Spectral map \n Dimensions of the PCA",
        colorVar = "BT", color = colorPalette,
        shapeVar = "sex",
        sizeVar = "age", sizeRange = c(0.5, 4),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        dim = c(3, 4)))

Implementation

The function uses the mpm and plot.mpm function from the mpm package. Some default parameters are set for these two functions, they can be changed via the mpm.args and plot.mpm.args arguments. For further details, refer to the documentation of these two functions.

Note: One important argument is logtrans in mpm function, which indicates if the data should be first log-transformed before the computation. It is set by default to FALSE, assuming that the data are already in the log scale (it is the case for the ALL dataset).

Interactive spectral map

All plots available in the esetVis package can be static or interactive.

The argument typePlot can be set respectively to static (by default), in this case ggplot2 is used, or interactive, in this case either the ggvis or rbokeh package is used.

Two functionalities of the interactive plot can be of interest for such high-dimensional data:

The same spectral map than in previous section is used, this time in an interactive plot.

    # wrapper for rbokeh/ggvis example
    esetSPMInteractive <- function(...)
        esetSpectralMap(eset = ALL, 
            title = paste("Acute lymphoblastic leukemia dataset - spectral map"),
            colorVar = "BT", color = unname(colorPalette),
            shapeVar = "sex",
            alphaVar = "remissionType",
            typePlot = "interactive", ...
    )

rbokeh

    esetSPMInteractive(
        packageInteractivity = "rbokeh",
        figInteractiveSize = c(700, 600),
        size = 6,
        # use all phenoData variables for hoover
        interactiveTooltipExtraVars = varLabels(ALL)
    )

ggvis

Note: as ggvis plot requires to have a R session running, only a static version of the plot is included.

    # embed a static version of the plot
    library(ggvis)
    esetSPMInteractive(
        packageInteractivity = "ggvis",
        sizeVar = "age", sizeRange = c(2, 6),
        figInteractiveSize = c(700, 600)
    )

Tsne: esetTsne

General

Another unsupervised visualization is available in the package: t-Distributed Stochastic Neighbor Embedding (tsne). The function esetTnse uses the Rtsne function of the same package.

Most of the previous parameters discussed for the spectral map are available for this visualization, at the exception of:

Arguments to the Rtsne function can be specified via the Rtsne.args argument.

Here is an example of tsne, for the same dataset and same annotation/labelling.

    print(esetTsne(eset = ALL, 
        title = "Acute lymphoblastic leukemia dataset \n Tsne",
        colorVar = "BT", color = colorPalette,
        shapeVar = "sex",
        sizeVar = "age", sizeRange = c(2, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topSamplesVar = "cod"
    ))

Additional pre-processing step

The tsne can be quite time-consuming, especially for big data. As the Rtsne function used in the background can also uses an object of class dist, the data can be pre-transformed before running the tsne analysis. The argument fctTransformDataForInputTsne enables to specify a custom function to pre-transform the data.

    print(esetTsne(eset = ALL, 
        title = "Acute lymphoblastic leukemia dataset \n Tsne",
        colorVar = "BT", color = colorPalette,
        shapeVar = "sex",
        sizeVar = "age", sizeRange = c(2, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topSamplesVar = "cod",
        fctTransformDataForInputTsne = 
            function(mat)   as.dist((1 - cor(mat))/2)
    ))

Linear discriminant analysis: esetLda

Another visualization, this time semi-supervised (as a variable is used for the computation), is included: Linear Discriminant Analysis. This uses the lda function from the MASS package.

This function maximizes the variance between levels of a factor, here describing some sample annotation, specified via the ldaVar argument.

As this analysis can be quite time consuming, for the demonstration, the analysis is run only a random feature subset of the data.

All samples

The returnAnalysis parameter can be used, to extract the analysis which will be used as input for the esetPlotWrapper function, used in the background. It is available also for the esetSpectralMap and esetTnse functions.

    # extract random features, because analysis is quite time consuming
    retainedFeatures <- sample(featureNames(ALL), size = floor(nrow(ALL)/5))

    # run the analysis
    outputEsetLda <- esetLda(eset = ALL[retainedFeatures, ], ldaVar = "BT",
        title = paste("Acute lymphoblastic leukemia dataset \n",
            "Linear discriminant analysis on BT variable \n",
            "(Subset of the feature data)"),
        colorVar = "BT", color = colorPalette,
        shapeVar = "sex",
        sizeVar = "age", sizeRange = c(2, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topSamplesVar = "cod", topGenesVar = "SYMBOL",
        returnAnalysis = TRUE
    )

    # extract and print the ggplot object
    print(outputEsetLda$plot)

The top elements (here genes and samples) labelled in the plot can be accessed via the topElements slot of the object. These are labelled with the identifiers used in the plot and named with sample/gene identifiers of the eset.

    # extract top elements labelled in the plot
    pander(t(data.frame(topGenes = outputEsetLda$topElements[["topGenes"]])))
    pander(t(data.frame(topSamples = outputEsetLda$topElements[["topSamples"]])))

When returnAnalysis is set to TRUE, the output of the function can be used as input to the esetPlotWrapper function, and extra parameters can then be modified.

Here the variable used for the shape of the points for the samples is changed to type of remission (remissionType column).

    # to change some plot parameters
    esetPlotWrapper(
        dataPlotSamples = outputEsetLda$analysis$dataPlotSamples,
        dataPlotGenes = outputEsetLda$analysis$dataPlotGenes,
        esetUsed = outputEsetLda$analysis$esetUsed,
        title = paste("Acute lymphoblastic leukemia dataset \n",
            "Linear discriminant analysis on BT variable (2) \n",
            "(Subset of the feature data)"),
        colorVar = "BT", color = colorPalette,
        shapeVar = "remissionType", 
        sizeVar = "age", sizeRange = c(2, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topSamplesVar = "cod", topGenesVar = "SYMBOL"
    )

    rm(outputEsetLda);tmp <- gc(verbose = FALSE)

Data sample subset

From the previous visualization (obtained on a subset of the genes), the biggest difference between all levels of the type/stage of the disease seems to reside between all B-cells (tagged B) and T-cells (tagged T). It might be interesting to focus on a subset of the data, e.g. only one cell type.

    # keep only 'B-cell' samples
    ALLBCell <- ALL[, grep("^B", ALL$BT)]
    ALLBCell$BT <- factor(ALLBCell$BT)
    colorPaletteBCell <- colorPalette[1:nlevels(ALLBCell$BT )]

    print(esetLda(eset = ALLBCell[retainedFeatures, ], ldaVar = "BT",
        title = paste("Acute lymphoblastic leukemia dataset \n",
            "Linear discriminant analysis on BT variable \n B-cell only",
            "(Subset of the feature data)"
        ),
        colorVar = "BT", color = colorPaletteBCell,
        shapeVar = "sex",
        sizeVar = "age", sizeRange = c(2, 6),
        alphaVar = "remissionType", alpha = c(0.3, 0.6, 0.9),
        topSamplesVar = "cod", topGenesVar = "SYMBOL",
    ))

    rm(ALLBCell);tmp <- gc(verbose = FALSE)

The subcell type which seems to differ the most within all B-cell type is the first one: B1 (most of these samples at the right side of the plot).

References



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esetVis documentation built on Nov. 8, 2020, 4:51 p.m.

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