Before this study no systematic approach for the classification of stem cells and their pluripotency capacities was introduced. The authors in this paper employ gene expression profiling as a generic method for such classifications. They generate a "stem cell matrix" in which many different types of stem cells are profiled along side differentiated tissues as controls. Subsequently, they use machine learning approaches for an unsupervised clustering of the cell lines based on their expression profiles. 12 distinct classes were identified... While a number of pluripotent stem cells (PSC) were grouped in specific clusters, some like neural stem cells are distributed in all classes. Then, they used an additional 66 profiles as a cross validation phase.
In the end, they use GSEA and MATISSE algorithm to find the pathways and regulatory networks that are associated with different phenotypes in their stem cell matrix.
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