Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMF.

ABSTRACT:

Motivation

The rapid proliferation of single-cell RNA-sequencing (scRNA-Seq) technologies has spurred the development of diverse computational approaches to detect transcriptionally coherent populations. While the complexity of the algorithms for detecting heterogeneity has increased, most require significant user-tuning, are heavily reliant on dimension reduction techniques and are not scalable to ultra-large datasets. We previously described a multi-step algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally coherent cell populations from an intuitive graphical user interface.

Results

We describe a new iteration of ICGS that outperforms state-of-the-art scRNA-Se

SUBMITTER: Venkatasubramanian M 

PROVIDER: S-EPMC7320606 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature