Research article: Self-organizing map of gene regulatory networks for cell phenotypes during reprogramming

Authors:
Leping Zhang;Yufang Zheng;Dongfang Li;Yang Zhong
Affiliations:
School of Life Sciences, Fudan University, Shanghai 200433, People's Republic of China and College of Basic Medical Sciences, Second Military Medical University, Shanghai 200433, People's Republic ...;School of Life Sciences, Fudan University, Shanghai 200433, People's Republic of China;College of Basic Medical Sciences, Second Military Medical University, Shanghai 200433, People's Republic of China;Institute of Biodiversity Science and Geobiology, Tibet University, Lhasa 850000, People's Republic of China and School of Life Sciences, Fudan University, Shanghai 200433, People's Republic of Ch ...
Venue:
Computational Biology and Chemistry
Year:
2011

Citing 2
Cited 0

Pvclust: an R package for assessing the uncertainty in hierarchical clustering

Bioinformatics
Improving cluster visualization in self-organizing maps: Application in gene expression data analysis

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The induced pluripotent cells (iPSCs) are derived from somatic cells by reprogramming their genetic profiles. Such a process requires coordinated dynamic expression of hundreds of genes and proteins. As both deterministic and stochastic elements control the reprogramming process, it is not easy to have a way to reflect the status of gene regulatory network in those reprogramming cells. In this study, we applied self-organizing maps (SOMs) on those complex gene expression data from different pluripotent cells, including partially reprogrammed and fully reprogrammed induced pluripotent cells (iPSCs), embryonic stem cells (ESCs), and adult stem cells came from different tissues. We showed that our SOMs have good correlation with the previously reported PluriNet of stem cells and they are pictorial diagrams which can reflect the intrinsic status of cells.