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Updated: Jul 15, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

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.

Molecular Systems Biology

|September 26, 2023

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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.

Keywords:

auto-encoder gene co-essentiality network normalization robust principal component analysis unsupervised dimensionality reduction

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Last Updated: Jul 15, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

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Published on: September 4, 2019

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

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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.