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Updated: Jul 15, 2025
Pooled CRISPR-Based Genetic Screens in Mammalian Cells
Published on: September 4, 2019
Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens
Arshia Zernab Hassan1, Henry N Ward2, Mahfuzur Rahman1
1Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
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.
Insights
We developed new methods to normalize the Cancer Dependency Map (DepMap) by reducing noise from mitochondrial signals. Robust PCA with onion normalization improves gene functional networks for cancer research.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- CRISPR-Cas9 screens are vital for discovering gene functions and cancer dependencies.
- The Cancer Dependency Map (DepMap) is a large dataset of these screens but contains a mitochondria-associated bias.
- This bias can obscure important biological signals, necessitating improved normalization techniques.
Purpose of the Study:
- To evaluate unsupervised dimensionality reduction methods for normalizing the DepMap dataset.
- To improve the accuracy of co-essentiality networks derived from CRISPR screen data.
- To develop and validate a novel normalization strategy for large-scale cancer dependency datasets.
Main Methods:
- Exploration of autoencoders, robust principal component analysis (PCA), and classical PCA for data normalization.
- Development of a novel 'onion' normalization technique to integrate multiple normalized data layers.
- Benchmarking of normalization methods against existing approaches using DepMap data.
Main Results:
- Robust PCA combined with the proposed onion normalization significantly outperformed other methods.
- Dimensionality reduction effectively removed confounding low-dimensional signals, such as mitochondrial bias.
- Normalized DepMap data yielded improved functional gene networks.
Conclusions:
- Unsupervised dimensionality reduction is valuable for normalizing large CRISPR screen datasets like the DepMap.
- The robust PCA and onion normalization method offers a superior approach for enhancing cancer dependency network analysis.
- These tools provide a generalizable strategy for improving the interpretation of functional genomics data.
