[BioC] Advice with RemoveBatchEffect and Rank Product package
Osee Sanogo
sanogo at life.illinois.edu
Mon Sep 10 13:23:04 CEST 2012
Dear Gordon,
>From what you said, it seems that I am oversimplifying my experiment by
attempting to analyze it with RankProd, which doesn't offer the option for
complex modeling.
Could you please explain to me how I could analyze the experiment using
Limma?
Please let me know if you'd like me to provide further details of the
experiment.
Thank you so much.
Osee
On 9/10/12 2:04 AM, "Gordon K Smyth" <smyth at wehi.EDU.AU> wrote:
> Dear Osee,
>
> No, you can't use removeBatchEffect to control for dye bias.
>
> Can you ignore the dye effect? Not in general, but who knows?
>
> Your experiment seems too complex to be properly analysed using RankProd.
> For one thing, it seems clear that you have obtained multiple parts of the
> brain from the same biological replicates, meaning that your samples are
> paired by fish number.
>
> I could explain how to analyse this experiment using limma. However, if
> you are determined that you will use RankProd, it might be best to email
> the authors of that package for advice.
>
> Best wishes
> Gordon
>
> ---------------------------------------------
> Professor Gordon K Smyth,
> Bioinformatics Division,
> Walter and Eliza Hall Institute of Medical Research,
> 1G Royal Parade, Parkville, Vic 3052, Australia.
> http://www.statsci.org/smyth
>
> On Sun, 9 Sep 2012, Osee Sanogo wrote:
>
>> Dear Gordon,
>>
>> Thank you for getting back to me about my questions.
>>
>> My experiment is trying to identify differentially expressed genes in four
>> regions of the brain in response to a stressor. I have 6 biol. replicates in
>> each brain region for the control and experimental groups in each region,
>> and the comparison is being done within brain region (i.e., T control vs T
>> exp, D ctrl vs D exp, C ctrl vs C exp, BS ctrl vs BS exp). The sample were
>> run in two-color Agilent Array.
>>
>> You're right that the design I sent was from the separate channel analysis,
>> in which I attempted to account for array and dye effect, and then run the
>> data in RankProd. Now I know that this is not right. Ok, I will use the
>> single channel analysis in Limma.
>>
>> I still would like to run the two-channel data (ratios) in RankProd, as my
>> previous experience found this useful for my dara (low replicate numbers).
>>
>> My questions: 1) Could I use RemoveBatchEffect to control for dye bias
>> before running the two-channel data in RankProd? If yes, how should I do
>> this using the RemoveBatch Effect function?
>> 2) I found that about 3% of my probes have dye effect. Can I
>> omit controlling for dye effect without compromising the result of my
>> analysis?
>>
>> The data were loess/scale normalized into an expression set (Data_RP).
>>
>> Here is the design of the experiment
>>
>> FileName Cy3 Cy5 Fish.Number Slide Brain.Part Weight Length
>> 1 1T.gpr 1 -1 1 2 T 39 0.63
>> 2 2T.gpr -1 1 2 1 T 39 0.63
>> 3 3T.gpr 1 -1 3 4 T 39 0.63
>> 4 4T.gpr -1 1 4 3 T 39 0.63
>> 5 5T.gpr 1 -1 5 6 T 39 0.63
>> 6 6T.gpr -1 1 6 5 T NA NA
>> 7 1D.gpr -1 1 1 5 D 47 1.21
>> 8 2D.gpr 1 -1 2 4 D 47 1.21
>> 9 3D.gpr -1 1 3 1 D 47 1.21
>> 10 4D.gpr 1 -1 4 6 D 47 1.21
>> 11 5D.gpr -1 1 5 3 D 47 1.21
>> 12 6D.gpr 1 -1 6 2 D NA NA
>> 13 1C.gpr 1 -1 1 4 C 47 1.31
>> 14 2C.gpr -1 1 2 3 C 47 1.31
>> 15 3C.gpr 1 -1 3 6 C 47 1.31
>> 16 4C.gpr -1 1 4 5 C 47 1.31
>> 17 5C.gpr 1 -1 5 2 C 47 1.31
>> 18 6C.gpr -1 1 6 1 C NA NA
>> 19 1BS.gpr -1 1 1 1 BS 89 1.44
>> 20 2BS.gpr 1 -1 2 2 BS 89 1.44
>> 21 3BS.gpr -1 1 3 3 BS 89 1.44
>> 22 4BS.gpr 1 -1 4 4 BS NA NA
>> 23 5BS.gpr -1 1 5 5 BS NA NA
>> 24 6BS.gpr 1 -1 6 6 BS NA NA
>>
>> Thank you for your help and please let me know if you need further
>> explanation of the experiment.
>>
>> Best regards,
>>
>> Osee
>>
>>>
>>
>>
>> On 9/9/12 7:24 PM, "Gordon K Smyth" <smyth at wehi.EDU.AU> wrote:
>>
>>> Dear Osee,
>>>
>>> You are attempting to do a number of things that don't seem correct to me.
>>>
>>> First, you seem to attempting a separate channel analysis of two color
>>> microarray data, but ignoring the pairing of the red and green channels.
>>> It isn't correct to do this. I don't see any way to use RankProd, or any
>>> other package designed for independent samples, correctly in this context.
>>> If you must do a separate channel analysis, you would be better off using
>>> the separate channel analysis facilities of the limma package.
>>>
>>> Second, when you set batch=rep(1,24), you are defining a batch that
>>> consists of every array in your data set. Obviously it doesn't make sense
>>> to remove batch effects unless there are at least two batches.
>>>
>>> Third, I don't follow your design matrix.
>>>
>>> It would be better to go back to the start, and describe in more basic
>>> terms what is the nature of your data and what comparison you are trying
>>> to make.
>>>
>>> Best wishes
>>> Gordon
>>>
>>>> Date: Sat, 8 Sep 2012 11:40:45 +0000
>>>> From: "Sanogo, Yibayiri O" <sanogo at illinois.edu>
>>>> To: "bioconductor at r-project.org" <bioconductor at r-project.org>
>>>> Subject: [BioC] Advice with RemoveBatchEffect and Rank Product package
>>>>
>>>> Dear Members of the list,
>>>>
>>>> (I apologize for posting this again -I sent it earlier to the list but
>>>> from another account and I was listed me as non-Member -and Member I am
>>>> since 2008:-)).
>>>>
>>>> I have been using Rank Prod to analyze Agilent two-color data. However, I
>>>> would like to remove the dye effect prior to analysis. I read on the forum
>>>> that RemoveBatchEffect should be used in the Limma linear model, a type of
>>>> modeling that is not in Rank Product.
>>>>
>>>> I have two questions:
>>>>
>>>> 1) Would it be appropriate to use RemoveBatchEffect to correct for dye
>>>> effect prior to running the expression data using Rank Prod?
>>>>
>>>> 2) a) If no, what other function could I use to do this?
>>>> b) If yes, I would like a help with the correct design and how to
>>>> properly indicate the batch.
>>>>
>>>> Here is my design indicating the two dyes (cy3=-1, cy5=1; T, D, C, BS =are
>>>> different areas of the brain):
>>>>
>>>> design1
>>>> BS C D T
>>>> 1 0 0 0 1
>>>> 2 0 0 0 -1
>>>> 3 0 0 0 1
>>>> 4 0 0 0 -1
>>>> 5 0 0 0 1
>>>> 6 0 0 0 -1
>>>> 7 0 0 -1 0
>>>> 8 0 0 1 0
>>>> 9 0 0 -1 0
>>>> 10 0 0 1 0
>>>> 11 0 0 -1 0
>>>> 12 0 0 1 0
>>>> 13 0 1 0 0
>>>> 14 0 -1 0 0
>>>> 15 0 1 0 0
>>>> 16 0 -1 0 0
>>>> 17 0 1 0 0
>>>> 18 0 -1 0 0
>>>> 19 -1 0 0 0
>>>> 20 1 0 0 0
>>>> 21 -1 0 0 0
>>>> 22 1 0 0 0
>>>> 23 -1 0 0 0
>>>> 24 1 0 0 0
>>>>
>>>> attr(,"assign")
>>>> [1] 1 1 1 1
>>>>
>>>> I've tried this (Data_RP are my data, the M values of the expression set):
>>>>
>>>> DYE_RP<-removeBatchEffect(Data_RP, batch=rep(1,24), batch2=NULL,
>>>> design=design1)
>>>>
>>>> but it is returning an error message
>>>> " Error in contr.sum(levels(batch)) :
>>>> not enough degrees of freedom to define contrasts"
>>>>
>>>> Please help me correct this code.
>>>>
>>>> Thank you so much for your help.
>>>>
>>>> Osee
>>>>
>>>> -- -- --
>>>> Y. Osee Sanogo
>>>> Integrative Biology
>>>> Institute for Genomic Biology
>>>> University of Illinois at Urbana
>>>> 505 S. Goodwin Ave
>>>> Urbana, IL-61801
>>>>
>>>> Tel: 217-333 2308 (Office)
>>>> 217-417 9593 (Cell)
>
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