دورية أكاديمية
Dimensionality reduction methods for extracting functional networks from large‐scale CRISPR screens
| العنوان: | Dimensionality reduction methods for extracting functional networks from large‐scale CRISPR screens |
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| المؤلفون: | Arshia Zernab Hassan, Henry N Ward, Mahfuzur Rahman, Maximilian Billmann, Yoonkyu Lee, Chad L Myers |
| المصدر: | Molecular Systems Biology, Vol 19, Iss 11, Pp n/a-n/a (2023) |
| بيانات النشر: | Springer Nature, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Biology (General) LCC:Medicine (General) |
| مصطلحات موضوعية: | auto‐encoder, gene co‐essentiality network, normalization, robust principal component analysis, unsupervised dimensionality reduction, Biology (General), QH301-705.5, Medicine (General), R5-920 |
| الوصف: | Abstract 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1744-4292 |
| Relation: | https://doaj.org/toc/1744-4292 |
| DOI: | 10.15252/msb.202311657 |
| URL الوصول: | https://doaj.org/article/becc7c92c97a46618bd84cf4cd986a98 |
| رقم الأكسشن: | edsdoj.becc7c92c97a46618bd84cf4cd986a98 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17444292 |
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| DOI: | 10.15252/msb.202311657 |