RT Journal Article
SR Electronic
T1 Enhancing single-cell cellular state inference by incorporating molecular network features
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 699959
DO 10.1101/699959
A1 Ji Dong
A1 Peijie Zhou
A1 Yichong Wu
A1 Wendong Wang
A1 Yidong Chen
A1 Xin Zhou
A1 Haoling Xie
A1 Yuan Gao
A1 Jiansen Lu
A1 Jingwei Yang
A1 Xiannian Zhang
A1 Lu Wen
A1 Wei Fu
A1 Tiejun Li
A1 Fuchou Tang
YR 2019
UL http://biorxiv.org/content/early/2019/10/15/699959.abstract
AB In biological systems, genes function in conjunction rather than in isolation. However, traditional single-cell RNA-seq (scRNA-seq) analyses heavily rely on the transcriptional similarity of individual genes, ignoring the inherent gene-gene interactions. Here, we present SCORE, a network-based method, which incorporates the validated molecular network features to infer cellular states. Using real scRNA-seq datasets, SCORE outperforms existing methods in accuracy, robustness, scalability, data integration and removal of batch effect. When applying SCORE to a newly generated human ileal scRNA-seq dataset, we identified several novel stem/progenitor clusters, including a Cripto-1+ cluster. Moreover, two distinct groups of goblet cells were identified and only one of them tended to secrete mucus. Besides, we found that the recently identified BEST4+OTOP2+ microfold cells also highly expressed CFTR, which is different from their colonic counterparts. In summary, SCORE enhances cellular state inference by simulating the dynamic changes of molecular networks, providing more biological insights beyond statistical interpretations.