deseq2_confects: Confident log2 fold changes based on a DESeq2 analysis
In topconfects: Top Confident Effect Sizes

Description Usage Arguments Details Value Examples

For all possible absolute log2 fold changes, which genes have at least this fold change at a specified False Discovery Rate? This is built by repeatedly calling DESeq2::results with the "greaterAbs" alternative hypothesis.

1	deseq2_confects(object, ..., fdr = 0.05, step = 0.01)

`object`	Object produced by the `DESeq2::DESeq` function.
`...`	Further arguments to `DESeq2::results`. At a minimum you should specify either `contrast=` or `name=`.
`fdr`	False Discovery Rate to control for.
`step`	Granularity of log2 fold changes to test.

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.

The filtered column in the result indicates whether DESeq2 filtered the gene. Such genes do not count toward the total number of genes when controlling FDR. If your intention is to obtain a ranking of all genes, you should disable this with deseq2_confects(..., cooksCutoff=Inf, independentFiltering=FALSE).

# Generate some random data
n <- 20
folds <- seq(-8,8,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)

group <- factor(c("A","A","B","B"))

# Apply DESeq2
library(DESeq2)
dds <- DESeqDataSetFromMatrix(
    countData = counts,
    colData = data.frame(group=group),
    design = ~group)

dds <- DESeq(dds)

# Find top confident effect sizes
deseq2_confects(dds, name="group_B_vs_A", step=0.1)