plotFCPseudocolor: Create a pseudo-color image of simulation data

In TCC: TCC: Differential expression analysis for tag count data with robust normalization strategies

Description

This function creates a pseudo-color image of simulation data regarding the number of differentially expressed genes (DEGs) and the breakdowns for individual groups from a TCC-class object.

Usage

plotFCPseudocolor(tcc, main, xlab, ylab)

Arguments

tcc	TCC-class object.
main	character string indicating the plotting title.
xlab	character string indicating the x-label title.
ylab	character string indicating the y-label title.

Details

This function should be used after the simulateReadCounts function that generates simulation data with arbitrary defined conditions. The largest log fold-change (FC) values are in magenta and no-changes are in white.

Examples

# Generating a simulation data for comparing two groups
# (G1 vs. G2) with biological replicates.
# the first 200 genes are DEGs, where 180 are up in G1.
tcc <- simulateReadCounts(Ngene = 1000, PDEG = 0.2,
                          DEG.assign = c(0.9, 0.1),
                          DEG.foldchange = c(4, 4), 
                          replicates = c(3, 3))
plotFCPseudocolor(tcc)

# Generating a simulation data for comparing three groups
# (G1 vs. G2 vs. G3) with biological replicates.
# the first 300 genes are DEGs, where the 70%, 20%, and 10% are
# up-regulated in G1, G2, G3, respectively. The levels of DE are
# 3-, 10, and 6-fold in individual groups.
tcc <- simulateReadCounts(Ngene = 1000, PDEG = 0.3,
                          DEG.assign = c(0.7, 0.2, 0.1),
                          DEG.foldchange = c(3, 10, 6),
                          replicates = c(3, 3, 3))
plotFCPseudocolor(tcc)

TCC documentation built on Nov. 8, 2020, 8:20 p.m.