Kleinberg, G.; Shinde, A.; Batchu, S.; Diaz, M.; Root, K.; Lucke-Wold, B. Bioinformatic Methods for Identifying Differentially Abundant Subpopulations in scRNA-seq Data. Preprints2022, 2022090327. https://doi.org/10.20944/preprints202209.0327.v1
APA Style
Kleinberg, G., Shinde, A., Batchu, S., Diaz, M., Root, K., & Lucke-Wold, B. (2022). Bioinformatic Methods for Identifying Differentially Abundant Subpopulations in scRNA-seq Data. Preprints. https://doi.org/10.20944/preprints202209.0327.v1
Chicago/Turabian Style
Kleinberg, G., Kevin Root and Brandon Lucke-Wold. 2022 "Bioinformatic Methods for Identifying Differentially Abundant Subpopulations in scRNA-seq Data" Preprints. https://doi.org/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.
Medicine and Pharmacology, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
