Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Bioinformatic Methods for Identifying Differentially Abundant Subpopulations in scRNA-seq Data

Version 1 : Received: 20 September 2022 / Approved: 21 September 2022 / Online: 21 September 2022 (11:22:50 CEST)

How to cite: Kleinberg, G.; Shinde, A.; Batchu, S.; Diaz, M.; Root, K.; Lucke-Wold, B. Bioinformatic Methods for Identifying Differentially Abundant Subpopulations in scRNA-seq Data. Preprints 2022, 2022090327 (doi: 10.20944/preprints202209.0327.v1). Kleinberg, G.; Shinde, A.; Batchu, S.; Diaz, M.; Root, K.; Lucke-Wold, B. Bioinformatic Methods for Identifying Differentially Abundant Subpopulations in scRNA-seq Data. Preprints 2022, 2022090327 (doi: 10.20944/preprints202209.0327.v1).

Abstract

Single-cell RNA sequencing data facilitates investigation of cell heterogeneity and subpopulations as well as differentially abundant states however modern single-cell RNA sequencing datasets are growing in size and complexity requiring advances in the bioinformatic methods that analyze them. Many methods exist for each step of analysis including read alignment, normalization, quality control, batch effect correction, imputation and dimensionality reduction. With so many options to choose from at each step of the analysis, benchmarking and a synthesis of the literature on the methods available is necessary to inform biological researchers on the most optimal workflow for their data. Here, recent key methods of analysis are highlighted with a focus on methods that facilitate identification of cell subpopulations and differentially abundant cell states. With a constantly expanding toolset for each step in single-cell RNA sequencing dataset analysis, biological researchers should stay informed to utilize the most applicable methods for their own analyses.

Keywords

scRNA-seq; bioinformatics; subpopulations; analysis methods; single-cell RNA sequencing

Subject

MEDICINE & PHARMACOLOGY, Behavioral Neuroscience

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