Consensus label propagation with graph convolutional networks for single-cell RNA sequencing cell type annotation.

ABSTRACT:

Motivation

Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective.

Results

We present a Graph Convolutional Network (GCN)-based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq datasets from different tissues, we show that our method improves accuracy over a simple consensus algorithm

SUBMITTER: Lewinsohn DP 

PROVIDER: S-EPMC10272704 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature