Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets.

ABSTRACT:

Background

Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches.

Results

We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to tr

SUBMITTER: Aybey B 

PROVIDER: S-EPMC10441575 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature