CEDA: integrating gene expression data with CRISPR-pooled screen data identifies essential genes with higher expression.

ABSTRACT:

Motivation

Clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic perturbation screen is a powerful tool to probe gene function. However, experimental noises, especially for the lowly expressed genes, need to be accounted for to maintain proper control of false positive rate.

Methods

We develop a statistical method, named CRISPR screen with Expression Data Analysis (CEDA), to integrate gene expression profiles and CRISPR screen data for identifying essential genes. CEDA stratifies genes based on expression level and adopts a three-component mixture model for the log-fold change of single-guide RNAs (sgRNAs). Empirical Bayesian prior and expectation-maximization algorithm are used for parameter estimation and false discovery rate inference.

Result

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC9710553 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature