In federicomarini/pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach

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opts_chunk$set(
  echo=input$report_echo,
  error=TRUE
)

Overview on the data

The data provided was used to construct the following objects

values$mydds

values$mydst

values$transformation_type

head(values$myannotation)

The following design were used:

DT::datatable(as.data.frame(colData(values$mydds)))

An overview of the table for the features is shown here, by displaying the r input$countstable_unit

if(input$countstable_unit=="raw_counts")
  currentMat <- counts(values$mydds,normalized=FALSE)
if(input$countstable_unit=="normalized_counts")
  currentMat <- counts(values$mydds,normalized=TRUE)
if(input$countstable_unit=="rlog_counts")
  currentMat <- assay(values$mydst)
if(input$countstable_unit=="log10_counts")
  currentMat <- log10(1 + counts(values$mydds,normalized=TRUE))

DT::datatable(currentMat)

This is how the samples cluster if we use euclidean distance on the rlog transformed values

if (!is.null(input$color_by)){
  expgroups <- as.data.frame(colData(values$mydst)[,input$color_by])
  # expgroups <- interaction(expgroups)
  rownames(expgroups) <- colnames(values$mydst)
  colnames(expgroups) <- input$color_by

  pheatmap(as.matrix(dist(t(assay(values$mydst)))),annotation_col = expgroups)
} else {
  pheatmap(as.matrix(dist(t(assay(values$mydst)))))
}

This is an overview of the number of available reads in each sample (normally these are only uniquely aligned reads)

rr <- colSums(counts(values$mydds))/1e6
if(is.null(names(rr)))
  names(rr) <- paste0("sample_",1:length(rr))
rrdf <- data.frame(Reads=rr,Sample=names(rr),stringsAsFactors = FALSE)
if (!is.null(input$color_by)) {
  selGroups <- as.data.frame(colData(values$mydds)[input$color_by])
  rrdf$Group <- interaction(selGroups)
  p <- ggplot(rrdf,aes_string("Sample",weight="Reads")) + geom_bar(aes_string(fill="Group")) + theme_bw()
  p
} else {
  p <- ggplot(rrdf,aes_string("Sample",weight="Reads")) + geom_bar() + theme_bw()
  p
}

print(colSums(counts(values$mydds)))
summary(colSums(counts(values$mydds))/1e6)      

This is a quick info on the number of detected genes

t1 <- rowSums(counts(values$mydds))
t2 <- rowMeans(counts(values$mydds,normalized=TRUE))

thresh_rowsums <- input$threshold_rowsums
thresh_rowmeans <- input$threshold_rowmeans
abs_t1 <- sum(t1 > thresh_rowsums)
rel_t1 <- 100 * mean(t1 > thresh_rowsums)
abs_t2 <- sum(t2 > thresh_rowmeans)
rel_t2 <- 100 * mean(t2 > thresh_rowmeans)

cat("Number of detected genes:\n")
# TODO: parametrize the thresholds
cat(abs_t1,"genes have at least a sample with more than",thresh_rowsums,"counts\n")
cat(paste0(round(rel_t1,3),"%"), "of the",nrow(values$mydds),"genes have at least a sample with more than",thresh_rowsums,"counts\n")
cat(abs_t2,"genes have more than",thresh_rowmeans,"counts (normalized) on average\n")
cat(paste0(round(rel_t2,3),"%"), "of the",nrow(values$mydds),"genes have more than",thresh_rowsums,"counts (normalized) on average\n")
cat("Counts are ranging from", min(counts(values$mydds)),"to",max(counts(values$mydds)))

PCA on the samples

This plot shows how the samples are related to each other by plotting PC r input$pc_x vs PC r input$pc_y, using the top r input$pca_nrgenes most variant genes

res <- pcaplot(values$mydst,intgroup = input$color_by,ntop = input$pca_nrgenes,
                     pcX = as.integer(input$pc_x),pcY = as.integer(input$pc_y),
                     text_labels = input$sample_labels,
                     point_size = input$pca_point_size, title="Samples PCA - zoom in",
                     ellipse = input$pca_ellipse, ellipse.prob = input$pca_cislider
      )
res <- res + theme_bw()
res

The scree plot helps determining the number of underlying principal components

rv <- rowVars(assay(values$mydst))
select <- order(rv, decreasing = TRUE)[seq_len(min(input$pca_nrgenes,length(rv)))]
pca <- prcomp(t(assay(values$mydst)[select, ]))

res <- pcascree(pca,type = input$scree_type, pc_nr = input$scree_pcnr, title="Scree plot for the samples PCA")
res <- res + theme_bw()
res

The genes with the highest loadings in the selected principal components are the following

rv <- rowVars(assay(values$mydst))
select <- order(rv, decreasing = TRUE)[seq_len(min(input$pca_nrgenes,length(rv)))]
pca <- prcomp(t(assay(values$mydst)[select, ]))

par(mfrow=c(2,1))
hi_loadings(pca,whichpc = as.integer(input$pc_x),topN = input$ntophiload,annotation = values$myannotation)
hi_loadings(pca,whichpc = as.integer(input$pc_y),topN = input$ntophiload,annotation = values$myannotation)

PCA on the genes

This plot illustrates how the top r input$pca_nrgenes variant genes are distributed in PC r input$pc_x vs PC r input$pc_y

if(!is.null(input$color_by)) {
        expgroups <- as.data.frame(colData(values$mydst)[,input$color_by])
        expgroups <- interaction(expgroups)
        expgroups <- factor(expgroups,levels=unique(expgroups))

      } else {
        expgroups <- colnames(values$mydst)
      }
      colGroups <- colSel()[factor(expgroups)]

res <- genespca(values$mydst,
                ntop = input$pca_nrgenes,
                choices = c(as.integer(input$pc_x),as.integer(input$pc_y)),
                biplot = TRUE,
                arrowColors = factor(colGroups,levels=unique(colGroups)),
                groupNames = expgroups,
                alpha=input$pca_point_alpha,coordEqual=FALSE,useRownamesAsLabels=FALSE,labels.size=input$pca_label_size,
                point_size=input$pca_point_size,varname.size=input$pca_varname_size, scaleArrow = input$pca_scale_arrow,
                annotation=values$myannotation)
res

For the selected genes, this is the overall profile across all samples

if(!is.null(input$pcagenes_brush) & length(input$color_by)>0)
      geneprofiler(values$mydst,
                   genelist = curData_brush()$ids,
                   intgroup = input$color_by,
                   plotZ = input$zprofile)

And here is an interactive heatmap for that subset

if(!is.null(input$pcagenes_brush))
{
  brushedObject <- curData_brush()
  if(nrow(brushedObject) > 1){
    selectedGenes <- brushedObject$ids
    toplot <- assay(values$mydst)[selectedGenes,]
    rownames(toplot) <- values$myannotation$gene_name[match(rownames(toplot),rownames(values$myannotation))]

    mycolss <- c("#313695","#4575b4","#74add1","#abd9e9","#e0f3f8","#fee090","#fdae61","#f46d43","#d73027","#a50026") # to be consistent with red/blue usual coding

    heatmaply(toplot,Colv = as.logical(input$heatmap_colv),colors = mycolss)
  }
}

Shortlisted genes

This gene was selected in the interactive session.

anno_id <- rownames(values$mydst)
      anno_gene <- values$myannotation$gene_name

      # if(is.null(input$color_by) & input$genefinder!="")
      #   return(ggplot() + annotate("text",label="Select a factor to plot your gene",0,0) + theme_bw())
      # if(is.null(input$color_by) & input$genefinder=="")
      #   return(ggplot() + annotate("text",label="Select a gene and a factor to plot gene",0,0) + theme_bw())
      # if(input$genefinder=="")
      #   return(ggplot() + annotate("text",label="Type in a gene name/id",0,0) + theme_bw())
      # if(!input$genefinder %in% anno_gene & !input$genefinder %in% anno_id)
      #   return(ggplot() + annotate("text",label="Gene not found...",0,0) + theme_bw())
if(input$genefinder!="") {

  if (input$genefinder %in% anno_id) {
    selectedGene <- rownames(values$mydst)[match(input$genefinder,rownames(values$mydst))]
    selectedGeneSymbol <- values$myannotation$gene_name[match(selectedGene,rownames(values$myannotation))]
    }
    if (input$genefinder %in% anno_gene) {
    selectedGeneSymbol <- values$myannotation$gene_name[which(values$myannotation$gene_name==input$genefinder)]
    if (length(selectedGeneSymbol) > 1) return(ggplot() + annotate("text",label=paste0("Type in a gene name/id of the following:\n",paste(selectedGene,collapse=", ")),0,0) + theme_bw())
    selectedGene <- rownames(values$myannotation)[which(values$myannotation$gene_name==input$genefinder)]
    }
    genedata <- plotCounts(values$mydds,gene=selectedGene,intgroup = input$color_by,returnData = TRUE)
    onlyfactors <- genedata[,match(input$color_by,colnames(genedata))]
    genedata$plotby <- interaction(onlyfactors)

    if(input$plot_style=="boxplot"){
    res <- ggplot(genedata,aes_string(x="plotby",y="count",fill="plotby")) +
      geom_boxplot(outlier.shape = NA,alpha=0.7) + theme_bw()
    if(input$ylimZero){
      res <- res + scale_y_log10(name="Normalized counts - log10 scale",limits=c(0.4,NA))
    } else {
      res <- res + scale_y_log10(name="Normalized counts - log10 scale")
    }

    res <- res +
      labs(title=paste0("Normalized counts for ",selectedGeneSymbol," - ",selectedGene)) +
      scale_x_discrete(name="") +
      geom_jitter(aes_string(x="plotby",y="count"),position = position_jitter(width = 0.1)) +
      scale_fill_discrete(name="Experimental\nconditions")
    # exportPlots$genesBoxplot <- res
    res

    } else if(input$plot_style=="violin plot"){
    res <- ggplot(genedata,aes_string(x="plotby",y="count",fill="plotby")) +
      geom_violin(aes_string(col="plotby"),alpha = 0.6) + theme_bw()
    if(input$ylimZero){
      res <- res + scale_y_log10(name="Normalized counts - log10 scale",limits=c(0.4,NA))
    } else {
      res <- res + scale_y_log10(name="Normalized counts - log10 scale")
    }

    res <- res +
      labs(title=paste0("Normalized counts for ",selectedGeneSymbol," - ",selectedGene)) +
      scale_x_discrete(name="") +
      geom_jitter(aes_string(x="plotby",y="count"),alpha = 0.8,position = position_jitter(width = 0.1)) +
      scale_fill_discrete(name="Experimental\nconditions") + scale_color_discrete(guide="none")
    # exportPlots$genefinder <- res
    res
  }
}

Repeat the same chunk of code and change the identifier of the gene to obtain the similar plot for the other candidates.

Functional interpretation of the principal components

These tables report the functional categories enriched in the genes with the top and bottom loadings in the selected principal components.

if(!is.null(values$mypca2go))
{
  goe <- values$mypca2go[[paste0("PC",input$pc_x)]][["posLoad"]]
  kable(goe, caption=paste0("Functional categories enriched in ","PC",input$pc_x, "- positive loadings"))
}

if(!is.null(values$mypca2go))
{
  goe <- values$mypca2go[[paste0("PC",input$pc_x)]][["negLoad"]]
  kable(goe, caption=paste0("Functional categories enriched in ","PC",input$pc_x, "- negative loadings"))
}

if(!is.null(values$mypca2go))
{
  goe <- values$mypca2go[[paste0("PC",input$pc_y)]][["posLoad"]]
  kable(goe, caption=paste0("Functional categories enriched in ","PC",input$pc_y, "- positive loadings"))
}

if(!is.null(values$mypca2go))
{
  goe <- values$mypca2go[[paste0("PC",input$pc_y)]][["negLoad"]]
  kable(goe, caption=paste0("Functional categories enriched in ","PC",input$pc_y, "- negative loadings"))
}

Multifactor exploration of the dataset

if(input$composemat > 0){
  pcmat <- obj3()[[1]]
  tcol <- obj3()[[2]]
  tcol2 <- obj3()[[3]]
  pres <- prcomp(t(pcmat),scale=FALSE)

  plot.index <- c(as.integer(input$pc_x_multifac),as.integer(input$pc_y_multifac))
  offset <- ncol(pcmat)/2
  gene.no <- offset
  pcx <- pres$x
  # set.seed(11)
    # for (i in 1:ncol(pcx)) {
    #   pcx[,i] <- pcx[,i] + rnorm(nrow(pcx),sd=diff(range(pcx[,i]))/100)
    # }
    plot(pcx[(offset+1):ncol(pcmat),plot.index[1]][1:gene.no],pcx[(offset+1):ncol(pcmat),plot.index[2]][1:gene.no],xlim=range(pcx[,plot.index[1]]),ylim=range(pcx[,plot.index[2]]),pch=20,col=tcol,cex=0.3)#,type="n")
    #plot(0,type="n",xlim=range(pres$x[,plot.index]),ylim=range(pres$x[,plot.index]))
    lcol <- ifelse(tcol != tcol2,"black","grey")
    for (i in 1:gene.no) {
      lines(pcx[c(i,offset+i),plot.index[1]],pcx[c(i,offset+i),plot.index[2]],col=lcol[i])
    }
    points(pcx[1:offset,plot.index[1]][1:gene.no],pcx[1:offset,plot.index[2]][1:gene.no],pch=20,col=tcol,cex=0.3)
    points(pcx[(offset+1):ncol(pcmat),plot.index[1]][1:gene.no],pcx[(offset+1):ncol(pcmat),plot.index[2]][1:gene.no],pch=20,col=tcol2,cex=0.3)}

About pcaExplorer

pcaExplorer is a Bioconductor package containing a Shiny application for analyzing expression data in different conditions and experimental factors.

pcaExplorer guides the user in exploring the Principal Components of the data, providing tools and functionality to detect outlier samples, genes that show particular patterns, and additionally provides a functional interpretation of the principal components for further quality assessment and hypothesis generation on the input data.

Thanks to its interactive/reactive design, it is designed to become a practical companion to any RNA-seq dataset analysis, making exploratory data analysis accessible also to the bench biologist, while providing additional insight also for the experienced data analyst.

pcaExplorer was developed in the Bioinformatics Division led by Harald Binder at the IMBEI (Institut für Medizinische Biometrie, Epidemiologie und Informatik) in the University Medical Center of the Johannes Gutenberg University Mainz.

Developers

pcaExplorer is currently maintained by Federico Marini at the IMBEI (www.imbei.uni-mainz.de). You can contact him by clicking on the button below.

Federico Marini

Code

pcaExplorer is a part of the Bioconductor project (www.bioconductor.org). All code for pcaExplorer, especially for the development version, is available on GitHub.

Citation info

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

citation("pcaExplorer")

Session Information

sessionInfo()

library(shiny)
footertemplate <- function(){
  tags$div(
    class = "footer",
    style = "text-align:center",
    tags$div(
      class = "foot-inner",
      list(
        hr(),
        "This report was generated with", tags$a(href="http://bioconductor.org/packages/pcaExplorer/", "pcaExplorer"), br(),
        "pcaExplorer is a project developed by Federico Marini in the Bioinformatics division of the ",
        tags$a(href="http://www.unimedizin-mainz.de/imbei","IMBEI"),br(),
        "Development of the pcaExplorer package is on ",
        tags$a(href="https://github.com/federicomarini/pcaExplorer", "GitHub")
      )
    )
  )
}

footertemplate()

federicomarini/pcaExplorer documentation built on April 8, 2024, 3:15 a.m.