Author
Listed:
- Bin Duan
(College of Life Science, Tongji University
Ninghai First Hospital)
- Chi Zhou
(College of Life Science, Tongji University)
- Chengyu Zhu
(College of Life Science, Tongji University)
- Yifei Yu
(College of Life Science, Tongji University)
- Gaoyang Li
(Shanghai Tenth People’s Hospital of Tongji University
School of Medicine Tongji University)
- Shihua Zhang
(Academy of Mathematics and Systems Science)
- Chao Zhang
(College of Life Science, Tongji University)
- Xiangyun Ye
(Shanghai Chest Hospital Shanghai Jiaotong University)
- Hanhui Ma
(School of Life Science and Technology ShanghaiTech University)
- Shen Qu
(College of Life Science, Tongji University)
- Zhiyuan Zhang
(Shanghai Jiao Tong University School of Medicine)
- Ping Wang
(Shanghai Tenth People’s Hospital of Tongji University
School of Medicine Tongji University)
- Shuyang Sun
(Shanghai Jiao Tong University School of Medicine)
- Qi Liu
(College of Life Science, Tongji University
Ninghai First Hospital)
Abstract
The recently developed single-cell CRISPR screening techniques, independently termed Perturb-Seq, CRISP-seq, or CROP-seq, combine pooled CRISPR screening with single-cell RNA-seq to investigate functional CRISPR screening in a single-cell granularity. Here, we present MUSIC, an integrated pipeline for model-based understanding of single-cell CRISPR screening data. Comprehensive tests applied to all the publicly available data revealed that MUSIC accurately quantifies and prioritizes the individual gene perturbation effect on cell phenotypes with tolerance for the substantial noise that exists in such data analysis. MUSIC facilitates the single-cell CRISPR screening from three perspectives, i.e., prioritizing the gene perturbation effect as an overall perturbation effect, in a functional topic-specific way, and quantifying the relationships between different perturbations. In summary, MUSIC provides an effective and applicable solution to elucidate perturbation function and biologic circuits by a model-based quantitative analysis of single-cell-based CRISPR screening data.
Suggested Citation
Bin Duan & Chi Zhou & Chengyu Zhu & Yifei Yu & Gaoyang Li & Shihua Zhang & Chao Zhang & Xiangyun Ye & Hanhui Ma & Shen Qu & Zhiyuan Zhang & Ping Wang & Shuyang Sun & Qi Liu, 2019.
"Model-based understanding of single-cell CRISPR screening,"
Nature Communications, Nature, vol. 10(1), pages 1-11, December.
Handle:
RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10216-x
DOI: 10.1038/s41467-019-10216-x
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Citations
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Cited by:
- Zhaowei Yu & Qi Wang & Qichen Zhang & Yawen Tian & Guo Yan & Jidong Zhu & Guangya Zhu & Yong Zhang, 2024.
"Decoding the genomic landscape of chromatin-associated biomolecular condensates,"
Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Rhymes, Jennifer M. & Arnott, David & Chadwick, David R. & Evans, Christopher D. & Jones, David L., 2023.
"Assessing the effectiveness, practicality and cost effectiveness of mitigation measures to reduce greenhouse gas emissions from intensively cultivated peatlands,"
Land Use Policy, Elsevier, vol. 134(C).
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