1. Resources
  2. Models, Tools & Databases
  3. scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes

Content posted to this wiki are contributions made by the IMAG research community.
Any questions or concerns should be directed to the individual authors. Full disclaimer statement found here. 

scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes

What is being modeled?
Transition states in single-cell transcriptomes
Description & purpose of resource

scRCMF is an unsupervised method that identifies stable cell states and transition cells by adopting a nonlinear optimization model that infers the latent
substructures from a gene-cell matrix.

Spatial scales
cellular
tissue
Temporal scales
1 - 103 s
hours
days
weeks to months
This resource is currently
mature and useful in ongoing research
Has this resource been validated?
Yes
Link to Resource Credibility Assessment
scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
Yes
Key publications (e.g. describing or using resource)

Zheng, Xiaoying, et al. "scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes." IEEE Transactions on Biomedical Engineering 67.5 (2019): 1418-1428.

DOI link to publication describing this resource

10.1109/TBME.2019.2937228

Link to resource
scRCMF package
Collaborators
Qing Nie
PI contact information
qnie@uci.edu
Table sorting checkbox
Off