Shulou(Shulou.com)06/01 Report--

This article mainly introduces the relevant knowledge of "how to choose a single factor in R language". The editor shows you the operation process through an actual case, and the operation method is simple, fast and practical. I hope this article "how to choose a single factor in R language" can help you solve the problem.

It is necessary to use some dimensionality reduction methods and select some of the more important single factors for downstream analysis. There are many methods, among which R-packet rbsurv is more common.

The specific operations are as follows:

# first look at the structure of the data box of the operation > head (exprSet) [0:6 0:6] bcr_patient_barcode time status NEAT1 MIR205HG LRRC75A_AS11 TCGA-2W-A8YY 148 0 37464.39 19.90881 24651.082 TCGA-4J-AA1J 226 0 81280.86 30453.54242 12288.183 TCGA-BI-A0VR 1505 0 57859.60 23164.53371 22721.614 TCGA-BI-A0VS 925 0 39732.40 34618.98183 13592.705 TCGA-BI-A20A 72 0 78514.49 29501.88646 14386.926 TCGA-C5-A0TN 348 1 47486.18 23953.34249 20335.92 significant genes in a single factor Further robust screening (it is better to select all samples at this time) sign_gene_expr

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