[BioC] Ranked genes generated by learning datasets and Differentially expressed genes generated by original data

Kaj Chokeshaiusaha [guest] guest at bioconductor.org
Tue Jul 29 17:00:39 CEST 2014


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:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] plsgenomics_1.2-6   MASS_7.3-33         limma_3.20.8       
[4] RankProd_2.36.0     CMA_1.22.0          Biobase_2.24.0     
[7] BiocGenerics_0.10.0 e1071_1.6-3        

loaded via a namespace (and not attached):
[1] class_7.3-10 tools_3.1.0

--
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