Stem Cell Transcriptional Networks pp 115-130

Part of the Methods in Molecular Biology book series (MIMB, volume 1150)

Identifying Stem Cell Gene Expression Patterns and Phenotypic Networks with AutoSOME



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.

Key words

Stem cells Pluripotency Cluster analysis Gene expression patterns Transcriptome networks Fuzzy clustering AutoSOME 


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute for Stem Cell Biology and Regenerative MedicineStanford University School of MedicineStanfordUSA
  2. 2.Department of Molecular, Cellular, and Developmental BiologyUniversity of California at Santa BarbaraSanta BarbaraUSA

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