Newman Aaron M, Cooper James B
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA,
Methods Mol Biol. 2014;1150:115-30. doi: 10.1007/978-1-4939-0512-6_6.
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.
干细胞具有分化和自我更新的独特特性，在正常发育、组织修复和疾病中发挥着关键作用。为了促进对细胞和组织的全系统分析，我们开发了AutoSOME，这是一种机器学习方法，用于在全转录组数据中识别协调的基因表达模式和相关的细胞表型，而无需事先了解聚类数量或结构。在这里，我们展示了一个简便的引物，证明了AutoSOME在识别和表征干细胞基因表达特征以及使用Cytoscape可视化转录组网络方面的应用。该方案应作为干细胞基因表达聚类分析的一般基础，可应用于研究多能性、多谱系潜能和肿瘤疾病。
