edger_confects: Confident log2 fold change based on the edgeR...

In pfh/topconfects: Top Confident Effect Sizes

Confident log2 fold change based on the edgeR Quasi-Likelihood method

Description

For all possible absolute log2 fold changes (LFC), which genes have at least this fold change at a specified False Discovery Rate?

Usage

edger_confects(
  fit,
  coef = NULL,
  contrast = NULL,
  fdr = 0.05,
  step = 0.01,
  null = c("worst.case", "interval")
)

Arguments

fit	An edgeR DGEGLM object produced using glmQLFit.
coef	Coefficient to test, as per glmTreat. Use either coef or contrast or effect.
contrast	Contrast to test, as per glmTreat. Use either coef or contrast or effect.
fdr	False Discovery Rate to control for.
step	Granularity of log2 fold changes to test.
null	"null" parameter passed through to edger::glmTreat (if coef or contrast given). Choices are "worst.case" or "interval". Note that the default here is "worst.case", to be consistent with other functions in topconfects. This differs from the default for glmTreat.

Details

Results are presented in a table such that for any given LFC, if the reader chooses the genes with abs(confect) less than this they are assured that this set of genes has at least this LFC (with the specified FDR). The confect column may also be viewed as a confidence bound on the LFC of each gene, with a dynamic correction for multiple testing.

Value

See nest_confects for details of how to interpret the result.

Examples

# Generate some random data
n <- 100
folds <- seq(-4,4,length.out=n)
row_means <- runif(n, min=0, max=5)
lib_scale <- c(1,2,3,4)
means <- 2^(outer(folds, c(-0.5,-0.5,0.5,0.5))) *
    row_means * rep(lib_scale,each=n)
counts <- rnbinom(length(means), mu=means, size=1/0.1)
dim(counts) <- dim(means)

design <- cbind(c(1,1,0,0), c(0,0,1,1))

# Fit data using edgeR quasi-likelihood
library(edgeR)
y <- DGEList(counts)
y <- calcNormFactors(y)
y <- estimateDisp(y, design)
fit <- glmQLFit(y, design)

# Find top confident effect sizes
edger_confects(fit, contrast=c(-1,1))

pfh/topconfects documentation built on Nov. 18, 2023, 9:49 p.m.