plotFunctions: Visualization functions for OUTRIDER

In gagneurlab/OUTRIDER: OUTRIDER - OUTlier in RNA-Seq fInDER

Visualization functions for OUTRIDER

Description

The OUTRIDER package provides mutliple functions to visualize the data and the results of a full data set analysis.

This is the list of all plotting function provided by OUTRIDER:

• plotAberrantPerSample()

• plotVolcano()

• plotExpressionRank()

• plotQQ()

• plotExpectedVsObservedCounts()

• plotCountCorHeatmap()

• plotCountGeneSampleHeatmap()

• plotSizeFactors()

• plotFPKM()

• plotExpressedGenes()

• plotDispEsts()

• plotPowerAnalysis()

• plotEncDimSearch()

For a detailed description of each plot function please see the details. Most of the functions share the same parameters.

Usage

plotAberrantPerSample(object, ...)

plotCountCorHeatmap(object, ...)

plotManhattan(object, ...)

plotEncDimSearch(object, ...)

plotQQ(object, ...)

plotVolcano(object, ...)

## S4 method for signature 'OutriderDataSet'
plotVolcano(
  object,
  sampleID,
  main,
  padjCutoff = 0.05,
  zScoreCutoff = 0,
  label = "aberrant",
  xaxis = c("zscore", "log2fc", "fc"),
  pch = 16,
  basePlot = FALSE,
  col = c("gray", "firebrick"),
  subsetName = NULL
)

## S4 method for signature 'OutriderDataSet'
plotQQ(
  object,
  geneID,
  main,
  global = FALSE,
  padjCutoff = 0.05,
  zScoreCutoff = 0,
  samplePoints = TRUE,
  legendPos = "topleft",
  outlierRatio = 0.001,
  conf.alpha = 0.05,
  subsetName = NULL,
  pch = 16,
  xlim = NULL,
  ylim = NULL,
  col = NULL
)

plotExpectedVsObservedCounts(
  ods,
  geneID,
  main,
  basePlot = FALSE,
  log = TRUE,
  groups = c(),
  groupColSet = "Set1",
  label = "aberrant",
  subsetName = NULL,
  ...
)

plotExpressionRank(
  ods,
  geneID,
  main,
  padjCutoff = 0.05,
  zScoreCutoff = 0,
  normalized = TRUE,
  basePlot = FALSE,
  log = TRUE,
  col = c("gray", "firebrick"),
  groups = c(),
  groupColSet = "Accent",
  label = "aberrant",
  subsetName = NULL
)

## S4 method for signature 'OutriderDataSet'
plotCountCorHeatmap(
  object,
  normalized = TRUE,
  rowCentered = TRUE,
  rowGroups = NA,
  rowColSet = NA,
  colGroups = NA,
  colColSet = NA,
  nRowCluster = 4,
  nColCluster = 4,
  main = "Count correlation heatmap",
  basePlot = TRUE,
  nBreaks = 50,
  show_names = c("none", "row", "col", "both"),
  ...
)

plotCountGeneSampleHeatmap(
  ods,
  normalized = TRUE,
  rowCentered = TRUE,
  rowGroups = NA,
  rowColSet = NA,
  colGroups = NA,
  colColSet = NA,
  nRowCluster = 4,
  nColCluster = 4,
  main = "Count Gene vs Sample Heatmap",
  bcvQuantile = 0.9,
  show_names = c("none", "col", "row", "both"),
  nGenes = 500,
  nBreaks = 50,
  ...
)

## S4 method for signature 'OutriderDataSet'
plotAberrantPerSample(
  object,
  main = "Aberrant Genes per Sample",
  outlierRatio = 0.001,
  col = "Dark2",
  yadjust = 1.2,
  ylab = "Aberrantly expressed genes",
  subsetName = NULL,
  ...
)

plotFPKM(ods, bins = 100)

## S4 method for signature 'OutriderDataSet'
plotDispEsts(
  object,
  compareDisp,
  xlim,
  ylim,
  main = "Dispersion estimates versus mean expression",
  ...
)

plotPowerAnalysis(ods)

## S4 method for signature 'OutriderDataSet'
plotEncDimSearch(object)

plotExpressedGenes(ods, main = "Statistics of expressed genes")

plotSizeFactors(ods, basePlot = TRUE)

## S4 method for signature 'OutriderDataSet'
plotManhattan(
  object,
  sampleID,
  value = "pvalue",
  chr = NULL,
  main = paste0("Sample: ", sampleID),
  featureRanges = rowRanges(object),
  subsetName = NULL,
  chrColor = c("black", "darkgrey")
)

Arguments

...	Additional parameters passed to plot() or plot_ly() if not stated otherwise in the details for each plot function
sampleID, geneID	A sample or gene ID, which should be plotted. Can also be a vector. Integers are treated as indices.
main	Title for the plot, if missing a default title will be used.
padjCutoff, zScoreCutoff	Significance or Z-score cutoff to mark outliers
label	Indicates which genes or samples should be labeled. By default all aberrant genes/samples are labelled. Can be set to NULL for no labels. Provide a vector of geneIDs/sampleIDs to label specific genes/samples.
xaxis	Indicates which assay should be shown on the x-axis of the volcano plot. Defaults to 'zscore'. Other options are 'fc' and 'log2fc' for the fold-change or log2 fold-change.
pch	Integer or character to be used for plotting the points
basePlot	if TRUE, use the R base plot version, else use the plotly framework, which is the default
col	Set color for the points. If set, it must be a character vector of length 2. (1. normal point; 2. outlier point) or a single character referring to a color palette from RColorBrewer.
subsetName	The name of a subset of genes of interest for which FDR corrected pvalues were previously computed. Those FDR values on the subset will then be used to determine aberrant status. Default is NULL (using transcriptome-wide FDR corrected pvalues).
global	Flag to plot a global Q-Q plot, default FALSE
samplePoints	Sample points for Q-Q plot, defaults to max 30k points
legendPos	Set legendpos, by default topleft.
outlierRatio	The fraction to be used for the outlier sample filtering
conf.alpha	If set, a confidence interval is plotted, defaults to 0.05
xlim, ylim	The x/y limits for the plot or NULL to use the full data range
ods, object	An OutriderDataSet object.
log	If TRUE, the default, counts are plotted in log10.
groups	A character vector containing either group assignments of samples or sample IDs. Is empty by default. If group assignments are given, the vector must have the same length as the number of samples. If sample IDs are provided the assignment will result in a binary group assignemt.
groupColSet	A color set from RColorBrewer or a manual vector of colors, which length must match the number of categories from groups.
normalized	If TRUE, the normalized counts are used, the default, otherwise the raw counts
rowCentered	If TRUE, the counts are row-wise (gene-wise) centered
rowGroups, colGroups	A vector of co-factors (colnames of colData) for color coding the rows. It also accepts a data.frame of dim = (#samples, #groups). Must have more than 2 groups.
rowColSet, colColSet	A color set from RColorBrewer/colorRampPalette
nRowCluster, nColCluster	Number of clusters to show in the row and column dendrograms. If this argument is set the resulting cluster assignments are added to the OutriderDataSet.
nBreaks	number of breaks for the heatmap color scheme. Default to 50.
show_names	character string indicating whether to show 'none', 'row', 'col', or 'both' names on the heatmap axes.
bcvQuantile	quantile for choosing the cutoff for the biological coefficient of variation (BCV)
nGenes	upper limit of number of genes (defaults to 500). Subsets the top n genes based on the BCV.
yadjust	Option to adjust position of Median and 90 percentile labels.
ylab	The y axis label
bins	Number of bins used in the histogram. Defaults to 100.
compareDisp	If TRUE, the default, and if the autoCorrect normalization was used it computes the dispersion without autoCorrect and plots it for comparison.
value	Indicates which assay is shown in the manhattan plot. Defaults to 'pvalue'. Other options are 'zScore' and 'log2fc'.
featureRanges	A GRanges object of the same length as the OutriderDataSet object that contains the genomic positions of features that are shown in the manhattan plot.
chrColor	A vector of length 2 giving the two colors used for coloring alternating chromosomes in the manhattan plot. Default colors are 'black' and 'darkgrey'.

Details

plotAberrantPerSample: The number of aberrant events per sample are plotted sorted by rank. The ... parameters are passed on to the aberrant function.

plotVolcano: the volcano plot is sample-centric. It plots for a given sample the negative log10 nominal P-values against the Z-scores for all genes.

plotExpressionRank: This function plots for a given gene the expression level against the expression rank for all samples. This can be used with normalized and unnormalized expression values.

plotQQ: the quantile-quantile plot for a given gene or if global is set to TRUE over the full data set. Here the observed P-values are plotted against the expected ones in the negative log10 space.

plotExpectedVsObservedCounts: A scatter plot of the observed counts against the predicted expression for a given gene.

plotCountCorHeatmap: The correlation heatmap of the count data of the full data set. Default the values are log transformed and row centered. This function returns an OutriderDataSet with annotated clusters if requested. The ... arguments are passed to the pheatmap function.

plotCountGeneSampleHeatmap: A gene x sample heatmap of the raw or normalized counts. By default they are log transformed and row centered. Only the top 500 viable genes based on the BCV (biological coefficient of variation) is used by default.

plotSizeFactors: The sizefactor distribution within the dataset.

plotFPKM: The distribution of FPKM values. If the OutriderDataSet object contains the passedFilter column, it will plot both FPKM distributions for the expressed genes and for the filtered genes.

plotExpressedGenes: A summary statistic plot on the number of genes expressed within this dataset. It plots the sample rank (based on the number of expressed genes) against the accumulated statistics up to the given sample.

plotDispEsts: Plots the dispersion of the OutriderDataSet model against the normalized mean count. If autoCorrect is used it will also estimate the dispersion without normalization for comparison.

plotPowerAnalysis: The power analysis plot should give the user a ruff estimate of the events one can be detected with OUTRIDER. Based on the dispersion of the provided OUTRIDER data set the theoretical P-value over the mean expression is plotted. This is done for different expression levels. The curves are smooths to make the reading of the plot easier.

plotEncDimSearch: Visualization of the hyperparameter optimization. It plots the encoding dimension against the achieved loss (area under the precision-recall curve). From this plot the optimum should be choosen for the q in fitting process.

plotManhattan: Visualizes different metrics for each gene (pvalue, log2 fold-change, z-score) along with the genomic coordinates of the respective gene as a manhattan plot. Detected outlier genes are highlighted in red.

Value

If base R graphics are used nothing is returned else the plotly or the gplot object is returned.

Examples

ods <- makeExampleOutriderDataSet(dataset="Kremer")
implementation <- 'autoencoder'


mcols(ods)$basepairs <- 300 # assign pseudo gene length for filtering
ods <- filterExpression(ods)
# restrict FDR correction to set of genes of interest per sample
genesOfInterest <- list(MUC1372 = c("ATPIF1", "UROD", "YBX1", 
                                     sample(rownames(ods), 25)),
                        MUC1360 = sample(rownames(ods), 50),
                        MUC1350 = sample(rownames(ods), 75),
                        X76619 = sample(rownames(ods), 20),
                        X74172 = sample(rownames(ods), 150))
ods <- OUTRIDER(ods, implementation=implementation, subsets=list("exampleGenes"=genesOfInterest))

plotAberrantPerSample(ods)
plotAberrantPerSample(ods, subsetName="exampleGenes")

plotVolcano(ods, 49)
plotVolcano(ods, 'MUC1365', basePlot=TRUE)
plotVolcano(ods, 'MUC1351', basePlot=TRUE, xaxis="log2fc", label=c("NBPF16"))
plotVolcano(ods, 'MUC1372', basePlot=TRUE, subsetName="exampleGenes")

plotExpressionRank(ods, 35)
plotExpressionRank(ods, 35, subsetName="exampleGenes")
plotExpressionRank(ods, "NDUFS5", normalized=FALSE, label="MUC1372",
    log=FALSE, main="Over expression outlier", basePlot=TRUE)

plotQQ(ods, 149)
plotQQ(ods, 149, subsetName="exampleGenes")
plotQQ(ods, global=TRUE, outlierRatio=0.001)

plotExpectedVsObservedCounts(ods, 149)
plotExpectedVsObservedCounts(ods, "ATAD3C", basePlot=TRUE)
plotExpectedVsObservedCounts(ods, "UROD", subsetName="exampleGenes")

plotExpressedGenes(ods)

sex <- sample(c("female", "male"), dim(ods)[2], replace=TRUE)
colData(ods)$Sex <- sex
ods <- plotCountCorHeatmap(ods, nColCluster=4, normalized=FALSE)
ods <- plotCountCorHeatmap(ods, colGroup="Sex", colColSet="Set1")
table(colData(ods)$clusterNumber_4)

plotCountGeneSampleHeatmap(ods, normalized=FALSE)
plotCountGeneSampleHeatmap(ods, rowGroups="theta", 
        rowColSet=list(c("white", "darkgreen")))

plotSizeFactors(ods)

mcols(ods)$basepairs <- 1
mcols(ods)$passedFilter <- rowMeans(counts(ods)) > 10
plotFPKM(ods)

plotDispEsts(ods, compareDisp=FALSE)

plotPowerAnalysis(ods)

## Not run: 
# for speed reasons we only search for 5 different dimensions
ods <- findEncodingDim(ods, params=c(3, 10, 20, 35, 50), 
        implementation=implementation)
plotEncDimSearch(ods)

## End(Not run)

# To show the pvalues of a sample in a manhattan plot, rowRanges(ods) must 
# contain the genomic position of each feature or a GRanges object must 
# be provided 
## Not run: 
# in case rowRanges(ods) is a GRangesList, run this first once to speed up:
rowRanges(ods) <- unlist(endoapply(rowRanges(ods), range))

## End(Not run)
gr <- GRanges(
         seqnames=sample(paste0("chr", 1:22), nrow(ods), replace=TRUE),
         ranges=IRanges(start=runif(nrow(ods), min=0, max=1e5), width=100))
plotManhattan(ods, "MUC1350", value="pvalue", featureRanges=gr)
plotManhattan(ods, "MUC1350", value="l2fc", featureRanges=gr)
plotManhattan(ods, "MUC1372", featureRanges=gr, subsetName="exampleGenes")

gagneurlab/OUTRIDER documentation built on April 19, 2024, 12:20 a.m.