Hi, Kaj.

There is no general answer to your question, I do not think.  However,
assuming that a statistical test was applied in both cases, one would need
to evaluate the statistical evidence to make an informed decision.  I will
say that while machine learning approaches often include feature selection,
the most common way to get a set of genes that differentiates two
phenotypes or groups of samples is using differential gene expression
hypothesis testing.

Sean



On Tue, Jul 29, 2014 at 11:00 AM, Kaj Chokeshaiusaha [guest] <
guest@bioconductor.org> 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:
>  [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
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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