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A Bioinformatic Toolkit for Single-Cell mRNA Analysis

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Book cover Single Cell Methods

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1979))

Abstract

The recent technological developments in the field of single-cell RNA-Seq enable us to assay the transcriptome of up to a million single cells in parallel. However, the analyses of such big datasets present a major challenge. During the last decade, a wide variety of strategies have been proposed covering different steps of the analysis. Here, we introduce a selection of computational tools to provide an overview of a generic analysis pipeline.

The first step of every scRNA-Seq experiment is proper study design, which does not require sophisticated experimental or informatics skills but is nonetheless presumably the most important step. The quality of the resulting data strictly depends on the proper planning of the experiment, including the selection of the most suitable technology for the biological question of interest as well as an elaborated study design to minimize the influence of confounding factors. Once the experiment has been conducted, the raw sequencing data needs to be processed to extract the gene expression information for each cell. This task comprises quality assessment of the sequenced reads, alignment against a reference genome, demultiplexing of the cell barcodes, and quantification of the reads/transcripts per gene. As any other transcriptomics technology, single-cell mRNA-Seq requires data normalization to assure sample-to-sample, here cell-to-cell, comparability and the consideration of confounding factors.

Once gene expression values have been extracted from the reads and normalized, the researcher has the agony of choosing between a plethora of analysis approaches to investigate diverse aspects of the single-cell transcriptomes, such as dimensionality reduction and clustering to explore cellular heterogeneity or trajectory analysis to model differentiation processes.

In this chapter, we present a wrap-up of the abovementioned steps to conduct single-cell RNA-Seq analyses and present a selection of existing tools.

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Acknowledgments

The authors would like to acknowledge Prof. Dr. med. Joachim L. Schultze for support and advice during the writing process. Moreover, the authors Paweł Biernat and Matthias Becker are supported by a grant from the Federal Ministry for Economic Affairs and Energy (BMWi Project FASTGenomics). The work of Jonas Schulte-Schrepping receives funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 733100 (SYSCID). The DFG graduate program 2168/1 (Bonn and Melbourne International Research and Training Group—Bo&MeRanG) supports Patrick Günther.

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Authors and Affiliations

  1. Department for Genomics and Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany

    Kevin Baßler, Patrick Günther, Jonas Schulte-Schrepping, Matthias Becker & Paweł Biernat

  2. Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE), University of Bonn, Bonn, Germany

    Matthias Becker

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  1. Kevin Baßler
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  2. Patrick Günther
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  3. Jonas Schulte-Schrepping
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Correspondence to Kevin Baßler .

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Editors and Affiliations

  1. Department of Life Sciences and System Biology, University of Turin, Italian Institute for Genomic Medicine, IIGM Turin, Torino, Italy

    Valentina Proserpio

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Baßler, K., Günther, P., Schulte-Schrepping, J., Becker, M., Biernat, P. (2019). A Bioinformatic Toolkit for Single-Cell mRNA Analysis. In: Proserpio, V. (eds) Single Cell Methods. Methods in Molecular Biology, vol 1979. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9240-9_26

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  • DOI: https://doi.org/10.1007/978-1-4939-9240-9_26

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