scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq

Author:

Meng Qiuchen12ORCID,Wei Lei12ORCID,Ma Kun345,Shi Ming12,Lin Xinyi345,Ho Joshua W K345,Li Yinqing678,Zhang Xuegong12910

Affiliation:

1. MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST , Department of Automation, , Beijing 100084 , China

2. Tsinghua University , Department of Automation, , Beijing 100084 , China

3. School of Biomedical Sciences , Li Ka Shing Faculty of Medicine, , Pokfulam, Hong Kong SAR , China

4. The University of Hong Kong , Li Ka Shing Faculty of Medicine, , Pokfulam, Hong Kong SAR , China

5. Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park , Hong Kong SAR , China

6. MOE Key Laboratory of Bioinformatics, Tsinghua University , Beijing 100084 , China

7. IDG-McGovern Institute for Brain Research , Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, , Beijing 100084 , China

8. Tsinghua University , Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, , Beijing 100084 , China

9. Center for Synthetic and Systems Biology , School of Life Sciences and School of Medicine, , Beijing 100084 , China

10. Tsinghua University , School of Life Sciences and School of Medicine, , Beijing 100084 , China

Abstract

Abstract Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Tsinghua University Initiative Scientific Research Program

Beijing Natural Science Foundation

Tsinghua University Spring Breeze Fund

Innovation and Technology Commission of Hong Kong

Publisher

Oxford University Press (OUP)

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