plotCompareP-method: Compare p-values from two analyses

In variancePartition: Quantify and interpret divers of variation in multilevel gene expression experiments

Description

Plot -log10 p-values from two analyses and color based on donor component from variancePartition analysis

Usage

plotCompareP(
  p1,
  p2,
  vpDonor,
  dupcorvalue,
  fraction = 0.2,
  xlabel = bquote(duplicateCorrelation ~ (-log[10] ~ p)),
  ylabel = bquote(dream ~ (-log[10] ~ p))
)

Arguments

p1	p-value from first analysis
p2	p-value from second analysis
vpDonor	donor component for each gene from variancePartition analysis
dupcorvalue	scalar donor component from duplicateCorrelation
fraction	fraction of highest/lowest values to use for best fit lines
xlabel	for x-axis
ylabel	label for y-axis

Value

ggplot2 plot

Examples

# load library
# library(variancePartition)

# Intialize parallel backend with 4 cores
library(BiocParallel)
register(SnowParam(4))

# load simulated data:
# geneExpr: matrix of gene expression values
# info: information/metadata about each sample
data(varPartData)

# Perform very simple analysis for demonstration

# Analysis 1
form <- ~ Batch 
fit = dream( geneExpr, form, info)
fit = eBayes( fit )
res = topTable( fit, number=Inf, coef="Batch3" )

# Analysis 2
form <- ~ Batch + (1|Tissue)
fit2 = dream( geneExpr, form, info)
res2 = topTable( fit2, number=Inf, coef="Batch3" )

# Compare p-values
plotCompareP( res$P.Value, res2$P.Value, runif(nrow(res)), .3 )

variancePartition documentation built on Nov. 8, 2020, 5:18 p.m.