Extending differential gene expression testing to handle genome aneuploidy in cancer
Fig 3
Impact of CN corrections on DGE analysis in lung adenocarcinoma (LUAD).
(A) Distribution of CN states across LUAD tumor samples. (B) Gene categorization by CN status and DeConveil class (DSGs, DIGs, DCGs). Genes with CN loss (CN = 0 or 1 in ≥25% of samples) are explicitly shown here despite having near-diploid mean CN values. Stacked bars indicate proportions of CN states (loss, neutral, gain, amplification), with percentages denoting fractions of the total gene set. (C) Volcano plots comparing PyDESeq2 (CN-naive) and DeConveil (CN-aware) DE analyses. Genes are plotted by log₂FC and FDR; significance thresholds are |log₂FC| > 1 and FDR < 0.05. (D) Comparison of effect size (log₂FC) and FDR (bottom row) estimates between PyDESeq2 and DeConveil across CN states (loss, neutral, gain, and amplification). The diagonal reference line represents a one-to-one correlation. (E) Distribution of effect size differences (log₂FC) between methods across CN states. (F) Sankey diagram showing reassignment of genes between expression categories (upregulated, downregulated, non-significant) when CN correction is applied.