Correction of a widespread bias in pooled chemical genomics screens improves their interpretability.

ABSTRACT: Chemical genomics is a powerful and increasingly accessible technique to probe gene function, gene-gene interactions, and antibiotic synergies and antagonisms. Indeed, multiple large-scale pooled datasets in diverse organisms have been published. Here, we identify an artifact arising from uncorrected differences in the number of cell doublings between experiments within such datasets. We demonstrate that this artifact is widespread, show how it causes spurious gene-gene and drug-drug correlations, and present a simple but effective post hoc method for removing its effects. Using several published datasets, we demonstrate that this correction removes spurious correlations between genes and conditions, improving data interpretability and revealing new biological insights. Finally, we determi

SUBMITTER: Kim LM 

PROVIDER: S-EPMC11535069 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature