Feb 28, 2019

Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

BioRxiv : the Preprint Server for Biology
J. Javier Diaz-MejiaJohn H. Morris

Abstract

Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and that some dedicated methods are available only as web servers with limited cell type gene expression signatures. In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Our results show that, in general, all four methods show a high performance in the task as evaluated by Receiver Operating Characteristic curve analysis (average AUC = 0.94, sd = 0.036), whereas Precision-Recall curve analyses show a wide variation depending on the method and dat...Continue Reading

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Mentioned in this Paper

Study
Sequence Determinations, RNA
Research Personnel
Sequence Determinations
Gene Expression
Evaluation
Simulation
Analysis
Isotope Labeling
Cell Protein Complex

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