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      Model-based understanding of single-cell CRISPR screening

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          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.

          Abstract

          Single-cell CRISPR screening combines pooled CRISPR screening with scRNA-seq analysis to expand the resolution power of genetic screening. Here, the authors develop MUSIC, a computational pipeline for analyzing single-cell CRISPR screening data.

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          Accounting for technical noise in single-cell RNA-seq experiments.

          Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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            SAVER: Gene expression recovery for single-cell RNA sequencing

            In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of lowly and moderately expressed genes which hinders downstream analysis. To address this challenge, we introduce SAVER (Single-cell Analysis Via Expression Recovery), an expression recovery method for UMI-based scRNA-seq data that borrows information across genes and cells to obtain accurate expression estimates for all genes.
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              A correlated topic model of Science

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                Author and article information

                Contributors
                pwangecnu@163.com
                shuyangs@shsmu.edu.cn
                qiliu@tongji.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 May 2019
                20 May 2019
                2019
                : 10
                : 2233
                Affiliations
                [1 ]ISNI 0000000123704535, GRID grid.24516.34, Department of Endocrinology and Metabolism, Shanghai Tenth People’s Hospital, Bioinformatics Department, , College of Life Science, Tongji University, ; Shanghai, China
                [2 ]Department of Ophthalmology, Ninghai First Hospital, Ninghai Zhejiang, China
                [3 ]ISNI 0000 0004 0527 0050, GRID grid.412538.9, Tongji University Cancer Center, , Shanghai Tenth People’s Hospital of Tongji University, ; Shanghai, China
                [4 ]ISNI 0000000123704535, GRID grid.24516.34, School of Medicine Tongji University, ; Shanghai, China
                [5 ]ISNI 0000 0004 0489 6406, GRID grid.458463.8, Institute of Applied Mathematics, , Academy of Mathematics and Systems Science, ; Beijing, China
                [6 ]ISNI 0000 0004 0632 3994, GRID grid.412524.4, Shanghai Chest Hospital Shanghai Jiaotong University, ; Shanghai, China
                [7 ]GRID grid.440637.2, School of Life Science and Technology ShanghaiTech University, ; Shanghai, China
                [8 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Department of Oral and Maxillofacial-Head Neck Oncology, Shanghai Ninth People’s Hospital, College of Stomatology, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                Author information
                http://orcid.org/0000-0003-0192-7118
                Article
                10216
                10.1038/s41467-019-10216-x
                6527552
                31110232
                414f12c4-eaaa-4ae7-a0e2-c763184b3644
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 May 2018
                : 30 April 2019
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                © The Author(s) 2019

                Uncategorized
                bioinformatics,computational biology and bioinformatics
                Uncategorized
                bioinformatics, computational biology and bioinformatics

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