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Last seen 10.3 years ago
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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