plotCompareP-method: Compare p-values from two analyses

In GabrielHoffman/variancePartition: Quantify and interpret drivers of variation in multilevel gene expression experiments

Compare p-values from two analyses

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)

library(BiocParallel)

# 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)

GabrielHoffman/variancePartition documentation built on April 10, 2024, 5:01 a.m.