Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens.

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• CRISPR-Cas9 screens are crucial for identifying gene functions and cancer dependencies.
• The Cancer Dependency Map (DepMap) is a large dataset of these screens.
• Mitochondrial bias in DepMap data can obscure important biological signals.

Conclusions:

• Removing low-dimensional signals, like mitochondrial bias, is essential for accurate functional gene network construction.
• Dimensionality reduction techniques offer generalizable tools for normalizing large-scale biological datasets.
• This approach enhances the utility of the DepMap for cancer dependency discovery.