Dear Fangxin,

 Thank you very much for looking into mail and replying promptly, I really
appreciate it.
For answers to question 2 and 3, I understood the points. I cross checked
that the data was normalized using MAS algorithm.

Regarding question 1 here are few more details -
I'm using two class data with Single origin to calculate Rank product. For
example, Class A contains 20 samples and Class B contains 10 samples. I'm
using following cutoff for the topGene functionality.

*classes - The vector to assign two classes labels.*
*RP.out <- RP(arab, classes, num.perm = 100, logged = TRUE,na.rm = FALSE,
plot = FALSE, rand = 123)
outPut <- topGene(RP.out, cutoff = 0.05, method = "pfp", logged = TRUE,
logbase = 2, gene.names = rownames(arab))*

Is the cutoff 0.05 enough or need to be more stringent?  Also, with this set
up is it possible to have FC value more than 1 in Table1 or FC value less
than 1 in Table2 for many probes?

Thank you once again.

Regards,
Rohan

On Tue, Dec 8, 2009 at 12:16 AM, Fangxin Hong <fxhong@jimmy.harvard.edu>wrote:

> Hi Rohan,
> Please see my comments below,
>
>
>
>>
>>> -----Original Message-----
>>> From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-
>>> bounces@stat.math.ethz.ch] On Behalf Of Rohan M
>>> Sent: Monday, December 07, 2009 6:55 AM
>>> To: bioconductor@stat.math.ethz.ch
>>> Subject: [BioC] Query related to topGene functionality in RankProd
>>> library.
>>>
>>> Dear Sir,
>>>
>>> I'm using RankProd library to find the significant genes in microarray
>>> studies.  I'm facing some problems in understanding the output of
>>> topGene
>>> functionality.
>>> Could you help me on following queries?
>>>
>>> 1) The topGene functionality outputs
>>> Table1: Genes called significant under class1 < class2 (Up regulated
>>> Genes)
>>> and Table2: Genes called significant under class1 > class2 (Down
>>> regulated
>>> genes). When I see the fold change value in both tables , there are
>>> some
>>> genes having fold change value less than 1 in Table 1 and some genes
>>> have
>>> fold change value greater than 1 in Table 2.
>>> "If the Gene has fold change value less than 1 then its down regulated"
>>> how
>>> can I interpret fold change value (up regulated or down regulated ) in
>>> such
>>> case?
>>>
>>>
>> Theoretically this shouldn't happen as expression level is suppressed when
> downregulated. I don't know what cutoff point you selected for topGene.
> If you use a loose criteria, which would lead to gene with not strong
> signal being identified out, this would happen.
> For example, if  gene A has 4 fold-change readout (in one-channel case) as
> 1.6,0.9,0.7,0.9 then the average fold-change is1.025. However, this gene
> might be identified in downregulation list as 3 out of 4 fold changes are
> less than 1.
>
>
> 2) In some cases both Table 1 and Table 2 contains same probe. Is it
>
>> possible to have one probe present in both tables? If Yes, then which
>>> one
>>> should be considered?
>>>
>>>
>> This type result would very much indicate this gene doesn't  have decent
> signal in either direction. This would happen when random variation gives
>  fake signal in both direction, when
> For example,  a gene with 4 fold-changes of 1.3, 0.6, 0.9, 1.2 would
> results in both list if a loose cut-off point is selected.
>
>  3) Sometime I see "Inf" as fold change value - must be infinity. Is it
>>> possible to have such value?
>>>
>>>
>> If there is 0 or negative value in the expression data (like the one
> normalized with MAS5), then it is possible.
>
>
> As stated in package manual, it is always a good to look at the data when
> such results coming out, which would help a lot in term of selecting cut-off
> point and interpret results
>
> Hope this would help, let me know if this is not clear or you prefer to
> send over your data for mt to take a look.
>
> Best,
> Fangxin
>
>  Could you please help me understanding the above points?
>>>
>>> Thanks and regards,
>>> Rohan
>>>
>>>        [[alternative HTML version deleted]]
>>>
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>>>
>>
> --
>
> Fangxin Hong Ph.D.
> Research Scientist
> Department of Biostatistics and Computational Biology
> Dana-Farber Cancer Institute, Harvard School of Public Health
> Phone: 617-632-3602
> Email: fxhong@jimmy.harvard.edu
>
>

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