Summarize DEGs Across Selected Assays

Summarizes the number of DEGs in each assay of a geneda object according to thresholds for adjusted p-value and log2 fold-change, separately reporting upregulated and downregulated genes. Thresholds are inclusive (padj <= alpha, |log2FoldChange| >= lfc), matching FilterDEGs(). Genes with a missing log2FoldChange or padj are not counted; missing values in other columns (e.g., gene annotations) are ignored.

Usage

SummarizeDEGs(object, alpha = 0.05, lfc1 = 1, lfc2 = 2, assays = NULL)

Arguments

object

A geneda object containing DEGs in its DEGs slot.

alpha

Numeric; adjusted p-value threshold. Default is 0.05.

lfc1

Numeric; lower log2 fold-change threshold. Default is 1.

lfc2

Numeric; higher log2 fold-change threshold. Default is 2.

assays

Optional character vector of assay names to summarize. Defaults to all assays in object@DEGs.

Value

A data.frame of DEG counts by assay and criteria.

Examples

data(Snail1KO)
counts <- as.matrix(Snail1KO$rawCounts)
counts <- counts[rowSums(counts >= 10) >= 3, ]
meta <- Snail1KO$metadata
rownames(meta) <- meta$Sample
meta$Genotype <- factor(meta$Genotype, levels = c("WT", "Snai1_KO"))
norm <- log2(t(t(counts) / colSums(counts)) * 1e6 + 1)
obj <- GenEDA(normalized = norm, metadata = meta, counts = counts)

# Differential expression with DESeq2 (Snai1_KO vs WT)
dds <- DESeq2::DESeqDataSetFromMatrix(counts, meta, design = ~Genotype)
dds <- DESeq2::DESeq(dds, quiet = TRUE)
res <- as.data.frame(DESeq2::results(dds))
obj <- SetDEGs(obj, res, assay = "KO_vs_WT")
SummarizeDEGs(obj, alpha = 0.05, lfc1 = 1, lfc2 = 2)
#>                                 KO_vs_WT
#> padj<=0.05 & log2FC >=1 (Up)         656
#> padj<=0.05 & log2FC <=-1 (Down)      542
#> padj<=0.05 & log2FC >=2 (Up)         228
#> padj<=0.05 & log2FC <=-2 (Down)      181