Paper Info
Title
Single cell clustering based on cell-pair differentiability correlation and variance analysis.
Abstract
Motivation: The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. Results: We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the frame-work of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm 'Corr' that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: shared nearest neighbor (SNN)-Cliq and several other state-of-the-art clustering methods, on many benchmark or real single cell RNA-sequencing datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell datasets even with strong noise. Prognosis analyses from independent datasets of cancers confirmed the effectiveness of our 'Corr' method.
Year
DOI
Venue
2018
10.1093/bioinformatics/bty390
BIOINFORMATICS
Field
DocType
Volume
Data mining,Pattern recognition,Computer science,Differentiable function,Correlation,Artificial intelligence,Cluster analysis,Analysis of variance
Journal
34
Issue
ISSN
Citations 
21
1367-4803
2
PageRank 
References 
Authors
0.37
2
4
Name
Order
Citations
PageRank
hao jiang15917.96
Lydia L. Sohn220.37
Haiyan Huang31027.22
Luonan Chen41485145.71