Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods.

ABSTRACT: An essential step in the analysis of single-cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single-cell predictive marker (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known

SUBMITTER: Yan H 

PROVIDER: S-EPMC9314150 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature