Question: Ranked genes generated by learning datasets and Differentially expressed genes generated by original data
5.1 years ago by
Guest User • 12k
Guest User • 12k wrote:
Dear R helpers, I'm confused about the applications of ranked top genes generated from multiple learning datasets normally used for supervised classification and those directly acquired from differential gene expression test from original data. With the same cut-off (like FDR<0.05) and nice classification result, are the ranked gene list better candidate for further biological validation (PCR) and gene enrichment analysis? With Respects, Kaj -- output of sessionInfo(): R version 3.1.0 (2014-04-10) Platform: x86_64-pc-linux-gnu (64-bit) locale:  LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C  LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8  LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8  LC_PAPER=en_GB.UTF-8 LC_NAME=C  LC_ADDRESS=C LC_TELEPHONE=C  LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C attached base packages:  parallel stats graphics grDevices utils datasets methods  base other attached packages:  plsgenomics_1.2-6 MASS_7.3-33 limma_3.20.8  RankProd_2.36.0 CMA_1.22.0 Biobase_2.24.0  BiocGenerics_0.10.0 e1071_1.6-3 loaded via a namespace (and not attached):  class_7.3-10 tools_3.1.0 -- Sent via the guest posting facility at bioconductor.org.
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