A transcriptional plasticity-aware framework for RNA-seq differential expression analysis

Differential expression (DE) analysis based on transcriptomic data provides a genome-wide assessment of gene responsiveness. We recently characterized transcriptional plasticity (TP)-the variability of gene expression in response to environmental changes-but its impact on DE analysis remained unexplored. In this work, we examined how TP affects DE analysis and introduced a TP-aware framework to improve the interpretation of DE results. We revealed correlations between fold change of gene expression and TP in 238 experiments with Mycobacterium tuberculosis (Mtb) and Escherichia coli (E. coli), which carried inherent biases, favoring genes with high TP while overlooking those with low TP. Therefore, we employed Locally Estimated Scatterplot Smoothing on TP to adjust the fold change of gene expression. Adjusted DE analyses identified new responsive pathways and yielded higher overall statistical significance and enrichment scores, especially for pathways with low-TP genes. Specifically, adjusted DE results revealed that bedaquiline treatment of Mtb induced cholesterol degradation, linezolid repressed acetate metabolism, and infection of macrophages upregulated fatty acid metabolism while downregulating cofactor biosynthesis. We also demonstrate that the adjustment strategy can be applied to other bacterial species and is compatible with various RNA-seq quantification approaches. In summary, we introduce a TP-aware approach that normalizes DE analysis by correcting for inherent transcriptional variability.