A clustering method for small scRNA-seq data based on subspace and weighted distance.

ABSTRACT:

Background

Identifying the cell types using unsupervised methods is essential for scRNA-seq research. However, conventional similarity measures introduce challenges to single-cell data clustering because of the high dimensional, high noise, and high dropout.

Methods

We proposed a clustering method for small ScRNA-seq data based on Subspace and Weighted Distance (SSWD), which follows the assumption that the sets of gene subspace composed of similar density-distributing genes can better distinguish cell groups. To accurately capture the intrinsic relationship among cells or genes, a new distance metric that combines Euclidean and Pearson distance through a weighting strategy was proposed. The relative Calinski-Harabasz (CH) index was used to estimat

SUBMITTER: Ning Z 

PROVIDER: S-EPMC9879162 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature