ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering.

ABSTRACT:

Motivation

Categorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes.

Result

To achieve single-cell type detection, we propose a meta-lear

SUBMITTER: Jiang H 

PROVIDER: S-EPMC10629177 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature