CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.

ABSTRACT:

Motivation

Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets.

Results

We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple c

SUBMITTER: Wang X 

PROVIDER: S-EPMC10873586 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature