meanvar: Explore the Mean-Variance Relationship for DGE Data

In hiraksarkar/edgeR_fork: Empirical Analysis of Digital Gene Expression Data in R

Description

Appropriate modelling of the mean-variance relationship in DGE data is important for making inferences about differential expression. Here are functions to compute gene means and variances, as well at looking at these quantities when data is binned based on overall expression level.

Usage

plotMeanVar(object, meanvar=NULL, show.raw.vars=FALSE, show.tagwise.vars=FALSE,
            show.binned.common.disp.vars=FALSE, show.ave.raw.vars=TRUE,
            scalar=NULL, NBline=FALSE, nbins=100, log.axes="xy", xlab=NULL,
            ylab=NULL,  ...)
binMeanVar(x, group, nbins=100, common.dispersion=FALSE, object=NULL)

Arguments

object	DGEList object containing the raw data and dispersion value. According the method desired for computing the dispersion, either estimateCommonDisp and (possibly) estimateTagwiseDisp should be run on the DGEList object before using plotMeanVar. The argument object must be supplied in the function binMeanVar if common dispersion values are to be computed for each bin.
meanvar	list (optional) containing the output from binMeanVar or the returned value of plotMeanVar. Providing this object as an argument will save time in computing the gene means and variances when producing a mean-variance plot.
show.raw.vars	logical, whether or not to display the raw (pooled) genewise variances on the mean-variance plot. Default is FALSE.
show.tagwise.vars	logical, whether or not to display the estimated genewise variances on the mean-variance plot (note that ‘tag’ and ‘gene’ are synonymous). Default is FALSE.
show.binned.common.disp.vars	logical, whether or not to compute the common dispersion for each bin of genes and show the variances computed from those binned common dispersions and the mean expression level of the respective bin of genes. Default is FALSE.
show.ave.raw.vars	logical, whether or not to show the average of the raw variances for each bin of genes plotted against the average expression level of the genes in the bin. Averages are taken on the square root scale as regular arithmetic means are likely to be upwardly biased for count data, whereas averaging on the square scale gives a better summary of the mean-variance relationship in the data. The default is TRUE.
scalar	vector (optional) of scaling values to divide counts by. Would expect to have this the same length as the number of columns in the count matrix (i.e. the number of libraries).
NBline	logical, whether or not to add a line on the graph showing the mean-variance relationship for a NB model with common dispersion.
nbins	scalar giving the number of bins (formed by using the quantiles of the genewise mean expression levels) for which to compute average means and variances for exploring the mean-variance relationship. Default is 100 bins
log.axes	character vector indicating if any of the axes should use a log scale. Default is "xy", which makes both y and x axes on the log scale. Other valid options are "x" (log scale on x-axis only), "y" (log scale on y-axis only) and "" (linear scale on x- and y-axis).
xlab	character string giving the label for the x-axis. Standard graphical parameter. If left as the default NULL, then the x-axis label will be set to "logConc".
ylab	character string giving the label for the y-axis. Standard graphical parameter. If left as the default NULL, then the x-axis label will be set to "logConc".
...	further arguments passed on to plot
x	matrix of count data, with rows representing genes and columns representing samples
group	factor giving the experimental group or condition to which each sample (i.e. column of x or element of y) belongs
common.dispersion	logical, whether or not to compute the common dispersion for each bin of genes.

Details

This function is useful for exploring the mean-variance relationship in the data. Raw variances are, for each gene, the pooled variance of the counts from each sample, divided by a scaling factor (by default the effective library size). The function will plot the average raw variance for genes split into nbins bins by overall expression level. The averages are taken on the square-root scale as for count data the arithmetic mean is upwardly biased. Taking averages on the square-root scale provides a useful summary of how the variance of the gene counts change with respect to expression level (abundance). A line showing the Poisson mean-variance relationship (mean equals variance) is always shown to illustrate how the genewise variances may differ from a Poisson mean-variance relationship. Optionally, the raw variances and estimated genewise variances can also be plotted. Estimated genewise variances can be calculated using either qCML estimates of the genewise dispersions (estimateTagwiseDisp) or Cox-Reid conditional inference estimates (CRDisp). A log-log scale is used for the plot.

Value

plotMeanVar produces a mean-variance plot for the DGE data using the options described above. plotMeanVar and binMeanVar both return a list with the following components:

avemeans	vector of the average expression level within each bin of genes, with the average taken on the square-root scale
avevars	vector of the average raw pooled gene-wise variance within each bin of genes, with the average taken on the square-root scale
bin.means	list containing the average (mean) expression level for genes divided into bins based on amount of expression
bin.vars	list containing the pooled variance for genes divided into bins based on amount of expression
means	vector giving the mean expression level for each gene
vars	vector giving the pooled variance for each gene
bins	list giving the indices of the genes in each bin, ordered from lowest expression bin to highest

Author(s)

Davis McCarthy

See Also

plotMeanVar2 is related in purpose but works from raw counts and uses standardized residuals.

plotMDS.DGEList, plotSmear and maPlot provide more ways of visualizing DGE data.

Examples

y <- matrix(rnbinom(1000,mu=10,size=2),ncol=4)
d <- DGEList(counts=y,group=c(1,1,2,2),lib.size=c(1000:1003))
plotMeanVar(d) # Produce a straight-forward mean-variance plot
# Produce a mean-variance plot with the raw variances shown and save the means
# and variances for later use
meanvar <- plotMeanVar(d, show.raw.vars=TRUE) 
## If we want to show estimated genewise variances on the plot, we must first estimate them!
d <- estimateCommonDisp(d) # Obtain an estimate of the dispersion parameter
d <- estimateTagwiseDisp(d)  # Obtain genewise dispersion estimates
# Use previously saved object to speed up plotting
plotMeanVar(d, meanvar=meanvar, show.tagwise.vars=TRUE, NBline=TRUE) 
## We could also estimate common/genewise dispersions using the Cox-Reid methods with an
## appropriate design matrix

hiraksarkar/edgeR_fork documentation built on Dec. 20, 2021, 3:52 p.m.