Current best practices in single‐cell RNA‐seq analysis: a tutorial

By Malte D. Luecken, Fabian J Theis. Molecular Systems Biology, 2019

1,950 citations recorded by the source corpus.

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 https://www.github.com/theislab/single-cell-tutorial. This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d197473a348efadb23a6bb52de3476fdfdfe7dec

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Citations in the corpus

SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics (2019, methodology): “In principle, we encourage users to apply advanced data normalization, clustering and cell-type calling tools (12,34), and to submit preprocessed count matrices to Sin-gleCellSignalR to perform LR interaction inference and visualization.”
The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution (2020, positive): “, 2007) and newer methods developed for single-cell genomics (Luecken and Theis, 2019).”
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data (2020, methodology): “In this case, for count-based assays such as (single-cell) RNA-seq, we recommend size factor normalization followed by a variance stabilization transformation [58].”
Benchmarking atlas-level data integration in single-cell genomics (2020, methodology): “All scRNA-seq datasets were quality controlled and normalized in the same way according to published best practices 7 .”
A single–cell type transcriptomics map of human tissues (2021, positive): “INTRODUCTION The marked improvements in massive parallel sequencing coupled with single-cell sample preparations and data deconvolution have allowed single-cell RNA sequencing (scRNA-seq) to become a powerful approach to characterize the gene expression profile in single cells (1, 2).”
Best practices for single-cell analysis across modalities (2023, methodology): “Batch effects can be observable after clustering and visualization and should be removed to ensure that they are not mistaken as actual biological insight 5 .”
CellRank for directed single-cell fate mapping (2020, neutral): “The single-cell field has reached a consensus that clustering cells in 2D or 3D representations must be avoided 32 and, similarly, we argue that velocity vectors projected onto two or three dimensions must not be used to address detailed questions of trajectory inference.”
Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender (2019, background): “Removal of ambient noise has been advocated as an important step in single-cell analysis workflows and protocols [45, 46] and is increasingly becoming a standard part of single-cell data analysis.”