Bias, robustness and scalability in single-cell differential expression analysis

By Charlotte Soneson, Mark D. Robinson. Nature Methods, 2018

666 citations recorded by the source corpus.

Many methods have been used to determine differential gene expression from single-cell RNA (scRNA)-seq data. We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed genes has important effects, particularly for some of the methods developed for bulk RNA-seq data analysis. However, we found that bulk RNA-seq analysis methods do not generally perform worse than those developed specifically for scRNA-seq. We also present conquer, a repository of consistently processed, analysis-ready public scRNA-seq data sets that is aimed at simplifying method evaluation and reanalysis of published results. Each data set provides abundance estimates for both genes and transcripts, as well as quality control and exploratory analysis reports.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ba0452a487f67e7c233967f4c5b56d1c786631b0

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Citations in the corpus

Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, positive): “Notably, edgeR was shown to perform well on scRNA-seq data (Soneson & Robinson, 2018) and uses a log-linear model to control for technical sample-to-sample variation.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Data from full-length protocols may benefit from normalization methods that take into account gene length (e.g. Patel et al , 2014; Kowalczyk et al , 2015; Soneson & Robinson, 2018), while 3 0 enrichment data do not.”
A comparison of automatic cell identification methods for single-cell RNA sequencing data (2019, result): “In contrast to the extensive evaluations of clustering, differential expression, and trajectory inference methods [10–12], there is currently only a single attempt comparing methods to”
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, neutral): “76).”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Indeed, a recent comparison highlighted decent performance of ttests and Wilcoxon ranksum nonparametric tests in comparing single cell subsets [190] .”