bulkAnalyseR (version 1.1.0)

DEanalysis: Perform differential expression (DE) analysis on an expression matrix

Description

This function performs DE analysis on an expression using edgeR or DESeq2, given a vector of sample conditions.

Usage

DEanalysis_edger(expression.matrix, condition, var1, var2, anno)
DEanalysis_deseq2(expression.matrix, condition, var1, var2, anno)

Value

A tibble with the differential expression results for all genes. Columns are

• gene_id (usually ENSEMBL ID matching one of the rows of the expression matrix)

• gene_name (name matched through the annotation)

• log2exp (average log2(expression) of the gene across samples)

• log2FC (log2(fold-change) of the gene between conditions)

• pval (p-value of the gene being called DE)

• pvalAdj (adjusted p-value using the Benjamini Hochberg correction)

Arguments

expression.matrix

the expression matrix; rows correspond to genes and columns correspond to samples; usually preprocessed by preprocessExpressionMatrix; a list (of the same length as modality) can be provided if #' length(modality) > 1

condition

a vector of the same length as the number of columns of expression.matrix, containing the sample conditions; this is usually the last column of the metadata

var1, var2

conditions (contained in condition) to perform DE between; note that DESeq2 requires at least two replicates per condition

anno

annotation data frame containing a match between the row names of the expression.matrix (usually ENSEMBL IDs) and the gene names that should be rendered within the app and in output files; this object is created by generateShinyApp using the org.db specified

Examples

expression.matrix.preproc <- as.matrix(read.csv(
  system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"), 
  row.names = 1
))[1:100, 1:4]

anno <- AnnotationDbi::select(
  getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
  keys = rownames(expression.matrix.preproc),
  keytype = 'ENSEMBL',
  columns = 'SYMBOL'
) %>%
  dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
  dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
  
edger <- DEanalysis_edger(
  expression.matrix = expression.matrix.preproc,
  condition = rep(c("0h", "12h"), each = 2),
  var1 = "0h",
  var2 = "12h",
  anno = anno
)
deseq <- DEanalysis_edger(
  expression.matrix = expression.matrix.preproc,
  condition = rep(c("0h", "12h"), each = 2),
  var1 = "0h",
  var2 = "12h",
  anno = anno
)
# DE genes with log2(fold-change) > 1 in both pipelines
intersect(
  dplyr::filter(edger, abs(log2FC) > 1, pvalAdj < 0.05)$gene_name,
  dplyr::filter(deseq, abs(log2FC) > 1, pvalAdj < 0.05)$gene_name
)