Adversarial dense graph convolutional networks for single-cell classification.

ABSTRACT:

Motivation

In single-cell transcriptomics applications, effective identification of cell types in multicellular organisms and in-depth study of the relationships between genes has become one of the main goals of bioinformatics research. However, data heterogeneity and random noise pose significant difficulties for scRNA-seq data analysis.

Results

We have proposed an adversarial dense graph convolutional network architecture for single-cell classification. Specifically, to enhance the representation of higher-order features and the organic combination between features, dense connectivity mechanism and attention-based feature aggregation are introduced for feature learning in convolutional neural networks. To preserve the features of the original data, we use a feature reconst

SUBMITTER: Wang K 

PROVIDER: S-EPMC9919433 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature