Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4Z6, Canada.
STAR Protoc. 2022 Feb 7;3(1):101168. doi: 10.1016/j.xpro.2022.101168. eCollection 2022 Mar 18.
Advances in high-throughput sequencing technologies now yield unprecedented volumes of OMICs data with opportunities to conduct systematic data analyses and derive novel biological insights. Here, we provide protocols to perform differential-expressed gene analysis of TCGA and GTEx RNA-Seq data from human cancers, complete integrative GO and network analyses with focus on clinical and survival data, and identify differential correlation of trait-associated biomarkers. For complete details on the use and execution of this protocol, please refer to Chen and MacDonald (2021).
高通量测序技术的进步现在产生了前所未有的 OMICs 数据量，为进行系统的数据分析和获得新的生物学见解提供了机会。在这里，我们提供了从人类癌症的 TCGA 和 GTEx RNA-Seq 数据中进行差异表达基因分析的方案，完成了对临床和生存数据的全面综合 GO 和网络分析，并确定了与特征相关的生物标志物的差异相关性。有关此方案的使用和执行的完整详细信息，请参阅 Chen 和 MacDonald（2021）。
