Radley Arthur H, Schwab Remy M, Tan Yuqi, Kim Jeesoo, Lo Emily K W, Cahan Patrick
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Nat Protoc. 2017 May;12(5):1089-1102. doi: 10.1038/nprot.2017.022. Epub 2017 Apr 27.
CellNet is a computational platform designed to assess cell populations engineered by either directed differentiation of pluripotent stem cells (PSCs) or direct conversion, and to suggest specific hypotheses to improve cell fate engineering protocols. CellNet takes as input gene expression data and compares them with large data sets of normal expression profiles compiled from public sources, in regard to the extent to which cell- and tissue-specific gene regulatory networks are established. CellNet was originally designed to work with human or mouse microarray expression data for 21 cell or tissue (C/T) types. Here we describe how to apply CellNet to RNA-seq data and how to build a completely new CellNet platform applicable to, for example, other species or additional cell and tissue types. Once the raw data have been preprocessed, running CellNet takes only several minutes, whereas the time required to create a completely new CellNet is several hours.
细胞网络（CellNet）是一个计算平台，旨在评估通过多能干细胞（PSC）定向分化或直接转化构建的细胞群体，并提出具体假设以改进细胞命运工程方案。细胞网络将基因表达数据作为输入，并将其与从公共来源汇编的正常表达谱大数据集进行比较，比较细胞和组织特异性基因调控网络的建立程度。细胞网络最初设计用于处理21种细胞或组织（C/T）类型的人类或小鼠微阵列表达数据。在这里，我们描述了如何将细胞网络应用于RNA测序数据，以及如何构建一个全新的、适用于例如其他物种或更多细胞和组织类型的细胞网络平台。一旦原始数据经过预处理，运行细胞网络只需要几分钟，而创建一个全新的细胞网络则需要几个小时。
