PuSH - Publikationsserver des Helmholtz Zentrums München

Luecken, M. ; Theis, F.J.

Current best practices in single-cell RNA-seq analysis: A tutorial.

Mol. Syst. Biol. 15:e8746 (2019)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
Schlagwörter Analysis Pipeline Development ; Computational Biology ; Data Analysis Tutorial ; Single-cell Rna-seq; Gene-expression; Sequencing Data; Regulatory Network; Heterogeneity; Visualization; Tool; Normalization; Extraction; Definition; Programs
ISSN (print) / ISBN 1744-4292
e-ISSN 1744-4292
Zeitschrift Molecular Systems Biology
Quellenangaben Band: 15, Heft: 6, Seiten: , Artikelnummer: e8746 Supplement: ,
Verlag EMBO Press
Verlagsort 111 River St, Hoboken 07030-5774, Nj Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)