Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data.

ABSTRACT: Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve c

SUBMITTER: Vasighizaker A 

PROVIDER: S-EPMC8742092 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature