Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA.
Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, USA.
Mol Syst Biol. 2024 Nov;20(11):1173-1186. doi: 10.1038/s44320-024-00069-y. Epub 2024 Sep 30.
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 determine experimental factors that predispose a dataset for this artifact and suggest a set of experimental and computational guidelines for performing pooled chemical genomics experiments that will maximize the potential of this powerful technique.
化学生物学是一种强大且越来越容易获取的技术，可以用于探测基因功能、基因-基因相互作用以及抗生素协同作用和拮抗作用。事实上，已经发表了多个在不同生物体中进行的大规模 pooled 数据集。在这里，我们发现了此类数据集内实验中细胞倍增次数差异未被校正而导致的一个假象。我们证明了这种假象是普遍存在的，展示了它如何导致基因-基因和药物-药物之间的虚假相关性，并提出了一种简单但有效的事后方法来消除其影响。使用几个已发表的数据集，我们证明了这种校正可以去除基因和条件之间的虚假相关性，提高数据的可解释性，并揭示新的生物学见解。最后，我们确定了使数据集容易受到这种假象影响的实验因素，并提出了一组进行 pooled 化学生物学实验的实验和计算指南，这些指南将最大限度地发挥这一强大技术的潜力。
