Quality-control audit of a bulk RNA-seq SummarizedExperiment

Source: R/qc_explore.R

Runs a battery of pre-analysis checks on a raw-counts SummarizedExperiment: duplicate sample identifiers, missing values in sample metadata, non-integer count values, and library-size outliers detected via a robust (median absolute deviation-based) rule. Every check is reported with the statistic that triggered it, not just a pass/fail flag, so the result can be inspected or logged rather than trusted blindly.

qc_explore(se, mad_threshold = 3)

Arguments

se

A SummarizedExperiment with a raw-count assay as its first assay (assay(se, 1)). Counts should be non-negative integers (or values coercible to integers without loss).

mad_threshold

Numeric scalar. Samples whose log10 library size deviates from the median by more than mad_threshold robust median absolute deviations are flagged as library-size outliers. Defaults to 3.

Value

An object of class "rnaSentry_qc" (a list) with elements:

n_samples

Number of samples (columns) in se.

n_genes

Number of genes (rows) in se.

duplicate_samples

Character vector of duplicated sample IDs, or character(0) if none.

non_integer_counts

Logical; TRUE if the count assay contains non-integer values.

missing_summary

A data.frame reporting the proportion of missing values per colData(se) column.

library_size_outliers

Character vector of sample IDs flagged as library-size outliers.

flags

A data.frame summarizing every issue raised, with columns check, severity, detail and stage.

Examples

library(SummarizedExperiment)
set.seed(1)
counts <- matrix(rpois(60, lambda = 200), nrow = 6, ncol = 10,
                  dimnames = list(paste0("gene", 1:6), paste0("S", 1:10)))
counts[, 1] <- counts[, 1] * 50L  # inject one library-size outlier
coldata <- DataFrame(condition = rep(c("A", "B"), each = 5),
                      row.names = paste0("S", 1:10))
se <- SummarizedExperiment(assays = list(counts = counts), colData = coldata)

qc <- qc_explore(se)
qc$flags
#>                  check severity
#> 1 library_size_outlier  warning
#>                                                   detail      stage
#> 1 Samples with library size > 3 MADs from the median: S1 qc_explore