Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER.

Autor:	Liu S; Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.; Department of Biomedical Engineering, Duke University, Durham, NC, USA., Hamilton MC; University Program in Genetics and Genomics, Duke University Medical Center, Durham, NC, USA.; Department of Biomedical Engineering, Duke University, Durham, NC, USA., Cowart T; Computational Biology and Bioinformatics, Duke University, Durham, NC, USA., Barrera A; Computational Biology and Bioinformatics, Duke University, Durham, NC, USA., Bounds LR; Department of Biomedical Engineering, Duke University, Durham, NC, USA., Nelson AC; Department of Biomedical Engineering, Duke University, Durham, NC, USA., Doty RW; Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA., Allen AS; Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA., Crawford GE; Department of Pediatrics, Duke University, Durham, NC, USA., Majoros WH; Department of Biomedical Engineering, Duke University, Durham, NC, USA., Gersbach CA; Department of Biomedical Engineering, Duke University, Durham, NC, USA.; Center for Advanced Genomic Technologies, Duke University, Durham, NC USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Sep 04. Date of Electronic Publication: 2024 Sep 04.
DOI:	10.1101/2024.09.04.611293
Abstrakt:	Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.
Databáze:	MEDLINE
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