Evaluation of single-cell classifiers for single-cell RNA sequencing data sets.

Clicks: 381
ID: 64382
2019
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Abstract
Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning 'unassigned' labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.
Reference Key
zhao2019evaluationbriefings Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhao, Xinlei;Wu, Shuang;Fang, Nan;Sun, Xiao;Fan, Jue;
Journal Briefings in bioinformatics
Year 2019
DOI
bbz096
URL
Keywords
classification benchmark comparative analysis single-cell rna-seq

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