Single-cell classification using graph convolutional networks.

ABSTRACT:

Background

Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures.

Results

In this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines

SUBMITTER: Wang T 

PROVIDER: S-EPMC8268184 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature