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      Best practices for single-cell analysis across modalities

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          Abstract

          Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

          Abstract

          Practitioners in the field of single-cell omics are now faced with diverse options for analytical tools to process and integrate data from various molecular modalities. In an Expert Recommendation article, the authors provide guidance on robust single-cell data analysis, including choices of best-performing tools from benchmarking studies.

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          Most cited references254

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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              Gene Ontology: tool for the unification of biology

              Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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                Author and article information

                Contributors
                fabian.theis@helmholtz-muenchen.de
                Journal
                Nat Rev Genet
                Nat Rev Genet
                Nature Reviews. Genetics
                Nature Publishing Group UK (London )
                1471-0056
                1471-0064
                31 March 2023
                : 1-23
                Affiliations
                [1 ]GRID grid.4567.0, ISNI 0000 0004 0483 2525, Institute of Computational Biology, Department of Computational Health, , Helmholtz Munich, ; Munich, Germany
                [2 ]GRID grid.452624.3, Institute of Lung Health and Immunity and Comprehensive Pneumology Center, , Helmholtz Munich; Member of the German Center for Lung Research (DZL), ; Munich, Germany
                [3 ]GRID grid.6936.a, ISNI 0000000123222966, TUM School of Life Sciences Weihenstephan, , Technical University of Munich, ; Munich, Germany
                [4 ]GRID grid.6936.a, ISNI 0000000123222966, Department of Mathematics, School of Computation, Information and Technology, , Technical University of Munich, ; Garching, Germany
                [5 ]GRID grid.6936.a, ISNI 0000000123222966, Munich Center for Machine Learning, , Technical University of Munich, ; Garching, Germany
                [6 ]GRID grid.411095.8, ISNI 0000 0004 0477 2585, Department of Paediatrics, Dr von Hauner Children’s Hospital, , University Hospital, Ludwig-Maximilians-Universität München, ; Munich, Germany
                [7 ]Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
                [8 ]GRID grid.6936.a, ISNI 0000000123222966, Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, , Technical University of Munich, ; Munich, Germany
                [9 ]GRID grid.6936.a, ISNI 0000000123222966, TranslaTUM, Center for Translational Cancer Research, , Technical University of Munich, ; Munich, Germany
                [10 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Institute for Computational Biomedicine, , Heidelberg University and Heidelberg University Hospital, ; Heidelberg, Germany
                [11 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, , University of Bonn, ; Bonn, Germany
                [12 ]GRID grid.424247.3, ISNI 0000 0004 0438 0426, Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), ; Bonn, Germany
                [13 ]GRID grid.10306.34, ISNI 0000 0004 0606 5382, Wellcome Sanger Institute, Hinxton, ; Cambridge, UK
                [14 ]GRID grid.419548.5, ISNI 0000 0000 9497 5095, Department of Translational Psychiatry, , Max Planck Institute of Psychiatry, and International Max Planck Research School for Translational Psychiatry (IMPRS-TP), ; Munich, Germany
                [15 ]GRID grid.164295.d, ISNI 0000 0001 0941 7177, Department of Cell Biology and Molecular Genetics, , University of Maryland, ; College Park, MD USA
                [16 ]GRID grid.164295.d, ISNI 0000 0001 0941 7177, Center for Bioinformatics and Computational Biology, , University of Maryland, ; College Park, MD USA
                [17 ]GRID grid.168010.e, ISNI 0000000419368956, Center for Personal Dynamic Regulomes, , Stanford University School of Medicine, ; Stanford, CA USA
                [18 ]GRID grid.6936.a, ISNI 0000000123222966, Department of Computer Science, School of Computation, Information and Technology, , Technical University of Munich, ; Garching, Germany
                [19 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Department of Statistics, , Ludwig-Maximilians-Universität München, ; Munich, Germany
                [20 ]GRID grid.430264.7, ISNI 0000 0004 4648 6763, Center for Computational Mathematics, , Flatiron Institute, ; New York, NY USA
                [21 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, School of Computer Science and Engineering, , The Hebrew University of Jerusalem, ; Jerusalem, Israel
                [22 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, Racah Institute of Physics, , The Hebrew University of Jerusalem, ; Jerusalem, Israel
                [23 ]GRID grid.164295.d, ISNI 0000 0001 0941 7177, Department of Computer Science, , University of Maryland, ; College Park, MD USA
                [24 ]GRID grid.185006.a, ISNI 0000 0004 0461 3162, La Jolla Institute for Immunology, ; La Jolla, CA USA
                [25 ]GRID grid.1957.a, ISNI 0000 0001 0728 696X, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, , RWTH Aachen University, ; Aachen, Germany
                [26 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA USA
                [27 ]GRID grid.429884.b, ISNI 0000 0004 1791 0895, New York Genome Center, ; New York, NY USA
                Author information
                http://orcid.org/0000-0002-8937-3457
                http://orcid.org/0000-0003-1063-005X
                http://orcid.org/0000-0002-1275-9802
                http://orcid.org/0000-0002-2600-4048
                http://orcid.org/0000-0001-7744-8565
                http://orcid.org/0000-0001-7464-7921
                http://orcid.org/0000-0003-2502-8803
                http://orcid.org/0000-0002-2419-1943
                Article
                586
                10.1038/s41576-023-00586-w
                10066026
                37002403
                0d8d1293-cad0-462b-85ed-cf21e7979e1f
                © Springer Nature Limited 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 14 February 2023
                Categories
                Expert Recommendation

                software,rna sequencing,functional genomics,machine learning

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