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      Single-cell data clustering based on sparse optimization and low-rank matrix factorization

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          Abstract

          Unsupervised clustering is a fundamental step of single-cell RNA-sequencing (scRNA-seq) data analysis. This issue has inspired several clustering methods to classify cells in scRNA-seq data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for scRNA-seq data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single- scRNA-seq data.

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          Most cited references41

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              SCANPY : large-scale single-cell gene expression data analysis

              Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press
                2160-1836
                June 2021
                31 March 2021
                31 March 2021
                : 11
                : 6
                : jkab098
                Affiliations
                [1 ] School of Mathematical Sciences, University of Science and Technology of China , 230026 Hefei, Anhui, China
                [2 ] Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China , 230021 Hefei, Anhui, China
                [3 ] School of Data Science, University of Science and Technology of China , 230026 Hefei, Anhui, China
                [4 ] CAS Center for Excellence in Molecular Cell Sciences, the CAS Key Laboratory of Innate Immunity and Chronic Disease, University of Science and Technology of China , 230027 Hefei, Anhui, China
                Author notes
                Corresponding author: Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, 230021 Hefei, Anhui, China. qukun@ 123456ustc.edu.cn (K.Q.); School of Mathematical Sciences, University of Science and Technology of China, 230026 Hefei, Anhui, China. chenfl@ 123456ustc.edu.cn (F.C.)

                Yinlei Hu and Bin Li authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-5555-8437
                Article
                jkab098
                10.1093/g3journal/jkab098
                8495739
                33787873
                4186946d-dad5-43be-b3c2-aabeeacdc7b0
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 15 February 2021
                : 20 March 2021
                Page count
                Pages: 7
                Funding
                Funded by: National Key R&D Program of China;
                Award ID: 2020YFA0112200
                Award ID: 2017YFA0102900
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 91940306
                Award ID: 81788101
                Award ID: 31970858
                Award ID: 31771428
                Award ID: 91640113
                Award ID: 61972368
                Award ID: 11571338
                Funded by: Fundamental Research Funds for the Central Universities, DOI 10.13039/501100012226;
                Award ID: YD2070002019
                Award ID: WK2070000158
                Award ID: WK9110000141
                Funded by: Anhui Provincial Natural Science Foundation, DOI 10.13039/501100003995;
                Award ID: BJ2070000097
                Categories
                Software and Data Resources
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI00010
                AcademicSubjects/SCI00960

                Genetics
                scso,single-cell cluster,spectral cluster,sparse optimization,scrna-seq
                Genetics
                scso, single-cell cluster, spectral cluster, sparse optimization, scrna-seq

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