A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.

ABSTRACT:

Motivation

Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is to accurately annotate the cell labels. In recent years, many supervised annotation methods have been developed and shown to be more convenient and accurate than unsupervised cell clustering. One challenge faced by all the supervised annotation methods is the identification of the novel cell type, which is defined as the cell type that is not present in the training data, only exists in the testing data. Existing methods usually label the cells simply based on the correlation coefficients or confidence scores, which sometimes results in an excessive number of unlabeled c

SUBMITTER: Li Z 

PROVIDER: S-EPMC9801963 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature