Zernab Hassan Arshia, Ward Henry N, Rahman Mahfuzur, Billmann Maximilian, Lee Yoonkyu, Myers Chad L
Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA.
Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, Minnesota, USA.
bioRxiv. 2023 Mar 19:2023.02.22.529573. doi: 10.1101/2023.02.22.529573.
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.
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筛选数据集，旨在识别跨人类细胞系的癌症特异性基因依赖性。此前有报道称，线粒体相关偏差会掩盖参与其他功能的基因的信号，因此，使这种主导信号归一化以改善共必需网络的方法备受关注。在本研究中，我们探索了三种无监督降维方法——自动编码器、稳健主成分分析和经典主成分分析（PCA）——用于使DepMap归一化，以改善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”归一化技术，将几个归一化数据层组合成一个单一网络。基准分析表明，稳健主成分分析与洋葱归一化相结合优于现有的DepMap归一化方法。我们的工作证明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可推广归一化工具。
