title: Identifying stem cell gene expression patterns and phenotypic networks with AutoSOME.
abstract: 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.
authors.affiliation: Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA, amnewman@stanford.edu.
authors.forename: Aaron M
authors.initials: AM
authors.lastname: Newman
authors.forename: James B
authors.initials: JB
authors.lastname: Cooper
mesh_terms: D001185:Artificial Intelligence; D056808:Biostatistics / Q000379:methods*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D017423:Sequence Analysis, RNA; D013234:Stem Cells / Q000378:metabolism*