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

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

SUBMITTER: Zhao X 

PROVIDER: S-EPMC7947964 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature