ScSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging.

Results

We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully des

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10214737 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature