Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data.

ABSTRACT:

Motivation

The rapid development of scRNA-seq technologies enables us to explore the transcriptome at the cell level on a large scale. Recently, various computational methods have been developed to analyze the scRNAseq data, such as clustering and visualization. However, current visualization methods, including t-SNE and UMAP, are challenged by the limited accuracy of rendering the geometric relationship of populations with distinct functional states. Most visualization methods are unsupervised, leaving out information from the clustering results or given labels. This leads to the inaccurate depiction of the distances between the bona fide functional states. In particular, UMAP and t-SNE are not optimal to preserve the global geometric structure. They may result in a contradiction

SUBMITTER: Zhai Z 

PROVIDER: S-EPMC9048682 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature