A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.

ABSTRACT: Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random fores

SUBMITTER: Aevermann B 

PROVIDER: S-EPMC8494219 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature