edger_confects: Confident log2 fold change based on the edgeR...
In topconfects: Top Confident Effect Sizes

Description Usage Arguments Details Value Examples

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

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

`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.

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.

See nest_confects for details of how to interpret the result.

# 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))