Stem Cell Transcriptional Networks

Volume 1150 of the series Methods in Molecular Biology pp 115-130


Identifying Stem Cell Gene Expression Patterns and Phenotypic Networks with AutoSOME

  • Aaron M. NewmanAffiliated withInstitute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine Email author 
  • , James B. CooperAffiliated withDepartment of Molecular, Cellular, and Developmental Biology, University of California at Santa Barbara

* Final gross prices may vary according to local VAT.

Get Access


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