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

[Kaj Chokeshaiusaha]

Thank you very much. I'll keep that in my mind.

With Respects,
Kaj

2557-07-29 22:06 GMT+07:00, Sean Davis <sdavis2 at mail.nih.gov>:
> 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 at 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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>