CRISPR-Cas9 筛选有助于发现基因功能关系和表型特异性依赖性。癌症依赖性图谱 (DepMap) 是最大的全基因组 CRISPR 筛选概要，旨在识别人类细胞系中癌症特异性的遗传依赖性。先前已报道线粒体相关的偏差会掩盖参与其他功能的基因的信号，因此，标准化这种主导信号以改善共本质网络的方法令人感兴趣。在本研究中，我们探索了三种无监督降维方法——自动编码器、鲁棒性和经典主成分分析（PCA）——用于规范化 DepMap，以改进从这些数据中提取的功能网络。我们提出了一种新颖的“洋葱”标准化技术，将多个标准化数据层组合成一个网络。基准分析表明，稳健的 PCA 与洋葱归一化相结合优于现有的 DepMap 归一化方法。我们的工作展示了在构建功能基因网络之前从 DepMap 中去除低维信号的价值，并提供了基于降维的通用标准化工具。© 2023 作者。根据 CC BY 4.0 许可证条款发布。

CRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods-autoencoders, robust, and classical principal component analyses (PCA)-for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel "onion" normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.© 2023 The Authors. Published under the terms of the CC BY 4.0 license.