ScHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.

ABSTRACT:

Motivation

The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant. However, existing methods utilizing neural networks typically focus on minimizing reconstruction loss and maintaining similarity in embeddings of directly related cells, but fail to consider dissimilarity, thus lacking separability and limiting the performance of clustering.

Results

We propose a novel clustering algorithm, called scHNTL (scRNA-seq data clusteri

SUBMITTER: Meng H 

PROVIDER: S-EPMC11878765 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature