Stem cells have the unique property of differentiation and self-renewal and play critical roles in normal development, tissue repair, and disease. To promote systems-wide analysis of cells and tissues, we developed AutoSOME, a machine-learning method for identifying coordinated gene expression patterns and correlated cellular phenotypes in whole-transcriptome data, without prior knowledge of cluster number or structure. Here, we present a facile primer demonstrating the use of AutoSOME for identification and characterization of stem cell gene expression signatures and for visualization of transcriptome networks using Cytoscape. This protocol should serve as a general foundation for gene expression cluster analysis of stem cells, with applications for studying pluripotency, multi-lineage potential, and neoplastic disease.
CITATION STYLE
Newman, A. M., & Cooper, J. B. (2014). Identifying stem cell gene expression patterns and phenotypic networks with AutoSOME. Methods in Molecular Biology, 1150, 115–130. https://doi.org/10.1007/978-1-4939-0512-6_6
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