从大规模CRISPR屏幕中提取功能网络的降维方法。

IF 8.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Molecular Systems Biology Pub Date : 2023-11-09 Epub Date: 2023-09-26 DOI:10.15252/msb.202311657
Arshia Zernab Hassan, Henry N Ward, Mahfuzur Rahman, Maximilian Billmann, Yoonkyu Lee, Chad L Myers
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引用次数: 0

摘要

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筛选简编,旨在识别人类细胞系中癌症特异性遗传依赖性。先前已经报道了线粒体相关的偏倚来掩盖参与其他功能的基因的信号,因此,对这种显性信号进行归一化以改善共本质网络的方法是令人感兴趣的。在这项研究中,我们探索了三种无监督降维方法——自动编码器、稳健和经典主成分分析(PCA)——用于规范DepMap,以改进从这些数据中提取的函数网络。我们提出了一种新的“洋葱”归一化技术,将几个归一化的数据层组合到一个网络中。基准分析表明,与洋葱归一化相结合的稳健PCA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,并提供了可推广的基于降维的归一化工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens.

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.

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来源期刊
Molecular Systems Biology
Molecular Systems Biology 生物-生化与分子生物学
CiteScore
18.50
自引率
1.00%
发文量
62
审稿时长
6-12 weeks
期刊介绍: Systems biology is a field that aims to understand complex biological systems by studying their components and how they interact. It is an integrative discipline that seeks to explain the properties and behavior of these systems. Molecular Systems Biology is a scholarly journal that publishes top-notch research in the areas of systems biology, synthetic biology, and systems medicine. It is an open access journal, meaning that its content is freely available to readers, and it is peer-reviewed to ensure the quality of the published work.
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