Verify reported sex against inferred biological sex from expression

Source: R/sex_check.R

Compares the sex recorded in sample metadata against sex inferred from expression of XIST and a panel of Y-chromosome marker genes. Designed to catch sample swaps and metadata entry errors before they propagate into downstream differential expression or survival analysis.

sex_check(
  se,
  sex_col = "sex",
  xist_gene = "XIST",
  y_genes = c("RPS4Y1", "DDX3Y", "KDM5D")
)

Arguments

se

A SummarizedExperiment with gene symbols as rownames(se) and a numeric (counts or normalized) assay as its first assay.

sex_col

Character. Name of the colData(se) column holding reported sex, coded as "M" / "F". Defaults to "sex".

xist_gene

Character. Gene symbol for XIST. Defaults to "XIST".

y_genes

Character vector of Y-chromosome marker gene symbols. Defaults to c("RPS4Y1", "DDX3Y", "KDM5D").

Value

A data.frame with one row per sample and columns sample_id, reported_sex, inferred_sex, and status (one of "OK", "MISMATCH", "AMBIGUOUS", or "MISSING" when reported sex is absent).

Details

Inference rule: a sample is called "F" when XIST expression is high relative to the Y-gene panel, "M" when the reverse holds, and "ambiguous" when neither signal is clearly dominant (for example, low-quality or highly degraded samples). The decision uses the sign of each sample's XIST-minus-Y-panel rank score: a score at least one rank unit above zero is called "F", at least one rank unit below zero "M", and a score within one rank unit of zero "ambiguous". Because the rule is based on ranks rather than hard-coded expression cutoffs, it is robust across different normalizations and sequencing depths.

Examples

library(SummarizedExperiment)
genes <- c("XIST", "RPS4Y1", "DDX3Y", "KDM5D", "GAPDH")
samples <- paste0("S", 1:6)
expr <- matrix(0, nrow = length(genes), ncol = length(samples),
                dimnames = list(genes, samples))
# samples 1-3 look female (high XIST, low Y genes)
expr["XIST", 1:3] <- c(950, 900, 875)
expr[c("RPS4Y1", "DDX3Y", "KDM5D"), 1:3] <- 5
# samples 4-6 look male, but S6's metadata will be mislabeled below
expr["XIST", 4:6] <- 5
expr[c("RPS4Y1", "DDX3Y", "KDM5D"), 4:6] <- 400
expr["GAPDH", ] <- 1000

coldata <- DataFrame(sex = c("F", "F", "F", "M", "M", "F"),
                      row.names = samples)  # S6 mislabeled as F
se <- SummarizedExperiment(assays = list(counts = expr), colData = coldata)

sex_check(se)
#>   sample_id reported_sex inferred_sex   status
#> 1        S1            F            F       OK
#> 2        S2            F            F       OK
#> 3        S3            F            F       OK
#> 4        S4            M            M       OK
#> 5        S5            M            M       OK
#> 6        S6            F            M MISMATCH