[BioC] Analysis affymetrix of experiment....help please
James W. MacDonald
jmacdon at uw.edu
Fri Sep 7 15:55:05 CEST 2012
On 9/7/2012 7:26 AM, Sean Davis wrote:
> On Fri, Sep 7, 2012 at 7:14 AM, suparna mitra<smitra at liverpool.ac.uk>wrote:
>
>> Oh thanks.. I missed this point. But can you suggest me one more thing...
>> when I tried adjust = "BH" (Benjamini-Hochberg I suppose) I got the same
>> result as adjust = "fdr". for topTable. Is it normal?
>>
> Yes. They are the same. See the help for p.adjust for details.
>
>
>> Further when I tried to do vennDiagram I was surprized to see 0 in all
>> circles. Thus I thought I must be doing something wrong. Sorry if my
>> question is silly.
>>
> Unfortunately, you have no significantly differentially-expressed genes.
> Note that all of the adjusted p-values are pretty high. You can try to
> filter your genes based on variance before testing to try to reduce the
> number of genes entering your test and multiple correction. However,
> having worked with this kind of biological system (patients), you may
> suffering from a problem of a small biological effect in the setting of
> large biological variation. A larger sample size may be necessary.
You may also be suffering from large technical variation, which could be
helped by applying array weights. See ?arrayWeights for more information.
Best,
Jim
>
> Sean
>
>
>> Here is what I tried.
>>
>>> topTable(fit2.invivo, coef = 1, adjust = "fdr")
>> ID logFC AveExpr t P.Value adj.P.Val
>> B
>>
>> 8819 7943047 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>> -2.023533
>>
>> 9675 7950951 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>> -2.023533
>>
>> 18889 8043581 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>> -2.023533
>>
>> 19899 8053785 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>> -2.023533
>>
>> 3713 7896238 0.7731154 2.999029 4.796490 1.434510e-04 0.9552974
>> -2.323922
>>
>> 19926 8054075 -0.3816217 4.062936 -4.557543 2.424324e-04 0.9998796
>> -2.454618
>>
>> 18660 8041642 -1.0007299 4.220083 -4.290346 4.379518e-04 0.9998796
>> -2.607991
>>
>> 3759 7896284 -0.7555604 5.727302 -4.159251 5.861601e-04 0.9998796
>> -2.685960
>>
>> 6238 7917530 0.5596335 11.170012 4.117421 6.433789e-04 0.9998796
>> -2.711203
>>
>> 15545 8010622 -0.3324189 3.771856 -3.971869 8.899739e-04 0.9998796
>> -2.800385
>>
>>> topTable(fit2.invivo, coef = 2, adjust = "fdr")
>> ID logFC AveExpr t P.Value adj.P.Val
>> B
>>
>> 621 7893126 -0.5848178 4.412764 -4.577179 0.0002321630 0.9999684
>> -2.469821
>>
>> 6238 7917530 -0.5783362 11.170012 -4.255023 0.0004737013 0.9999684
>> -2.652426
>>
>> 26642 8120756 -1.0354557 5.439265 -4.238568 0.0004913467 0.9999684
>> -2.662042
>>
>> 1687 7894197 -0.9004303 2.631359 -4.169362 0.0005731153 0.9999684
>> -2.702782
>>
>> 2353 7894871 0.8441561 4.815714 4.161413 0.0005833454 0.9999684
>> -2.707492
>>
>> 3641 7896166 -0.6206262 7.735431 -4.144225 0.0006060986 0.9999684
>> -2.717698
>>
>> 2088 7894602 0.4713716 2.841855 4.115413 0.0006462632 0.9999684
>> -2.734873
>>
>> 5638 7911243 -0.7263053 5.676410 -4.053352 0.0007421075 0.9999684
>> -2.772143
>>
>> 7851 7933619 0.4194965 8.480778 4.040446 0.0007637691 0.9999684
>> -2.779941
>>
>> 20151 8056222 -0.8981049 7.892249 -4.031734 0.0007787485 0.9999684
>> -2.785214
>>
>>> topTable(fit2.invivo, coef = 3, adjust = "fdr")
>> ID logFC AveExpr t P.Value adj.P.Val
>> B
>>
>> 2590 7895109 -0.9415442 4.766552 -5.803704 1.670491e-05 0.5562234
>> -0.6982314
>>
>> 6210 7917182 -0.2981341 3.273225 -5.028595 8.656989e-05 0.6545102
>> -1.2472882
>>
>> 27812 8132245 -0.4595908 5.409405 -4.995303 9.304487e-05 0.6545102
>> -1.2727646
>>
>> 867 7893372 1.3251627 3.017891 4.981783 9.581361e-05 0.6545102
>> -1.2831553
>>
>> 26802 8122099 -0.4740894 4.548920 -4.828048 1.338927e-04 0.6545102
>> -1.4031177
>>
>> 808 7893313 1.0125247 7.938503 4.739949 1.623493e-04 0.6545102
>> -1.4733549
>>
>> 26093 8115516 -0.5100673 6.294000 -4.703760 1.757561e-04 0.6545102
>> -1.5025187
>>
>> 587 7893092 -0.9608515 6.013864 -4.631511 2.059886e-04 0.6545102
>> -1.5612836
>>
>> 22913 8084605 -0.3491973 6.211757 -4.519801 2.634837e-04 0.6545102
>> -1.6535466
>>
>> 3828 7896353 0.6239117 4.207636 4.504578 2.724902e-04 0.6545102
>> -1.6662493
>>
>>> results<- decideTests(fit2.invivo)
>>> vennDiagram(results)
>> see the plot attached.
>> Thanks,
>> Mitra
>>
>>
>> On 7 September 2012 12:03, Sean Davis<sdavis2 at mail.nih.gov> wrote:
>>
>>> On Fri, Sep 7, 2012 at 6:57 AM, suparna mitra<smitra at liverpool.ac.uk
>>>> wrote:
>>>> Dear Sean,
>>>> I have been reading Bioconductor and limma user guide and thus this
>> is
>>> I
>>>> tried.
>>>> But just being a novice, wanted to make sure if I am right.
>>>> I know I have perform t-test when I created the contrast, but can you
>>>> please help me how can I perform unpaired t-test here. I am concerned
>> as
>>>> the patients in groups are not same.
>>>>
>>>
>>> The t-test you performed was unpaired; unpaired is the "default".
>>>
>>> Sean
>>>
>>>
>>>> Thanks,
>>>> Mitra
>>>>
>>>> On 7 September 2012 11:41, Sean Davis<sdavis2 at mail.nih.gov> wrote:
>>>>
>>>>>
>>>>> On Fri, Sep 7, 2012 at 5:54 AM, suparna mitra<
>> smitra at liverpool.ac.uk
>>>>> wrote:
>>>>>
>>>>>> Hello Group,
>>>>>> I am trying t analyze my affymetrix (HuGene-1_0-st-v1) data using
>> BiC.
>>>>>> Previously i was using different softwares for this. And this is my
>>>> first
>>>>>> try with Bioconductor for big experiment. So thought to get some
>>> advice
>>>> in
>>>>>> the beginning.
>>>>>> I have Three groups of patient: (In-vivo)
>>>>>> A-Acute reaction. Patient taking a drug X develops reaction.
>>>>>> R-recovered (6 weeks after acute reaction-not longer taking drug
>> X).
>>>>>> T-Tolerant. Patient on X and tolerating treatment.
>>>>>>
>>>>>> Now in in-vitro study we used another constant Y
>>>>>> RXY recovered and challenged with X+Y
>>>>>> RY recovered challenged with only Y. RXY vs RY are to exclude
>>> effects
>>>>>> by
>>>>>> Y.
>>>>>> TXY tolerant and challenged with X+Y,
>>>>>> TY tolerant challenged with only Y. TXY vs TY are to exclude
>> effects
>>>> by
>>>>>> Y.
>>>>>>
>>>>>> No I want to check the cross relation and effects A vs R, RvsT and
>>> Avs T
>>>>>> and differentially expressed genes for each comparison. And the
>> same
>>> in
>>>>>> invitro. There are not same patients in different groups, thus I
>>> think I
>>>>>> want to apply unpaired-t test.
>>>>>>
>>>>>> This is what I tried:
>>>>>>> sessionInfo()
>>>>>> R version 2.15.1 (2012-06-22)
>>>>>> Platform: i386-apple-darwin9.8.0/i386 (32-bit)
>>>>>>
>>>>>> locale:
>>>>>> [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
>>>>>>
>>>>>> attached base packages:
>>>>>> [1] stats graphics grDevices utils datasets methods base
>>>>>>
>>>>>> other attached packages:
>>>>>> [1] statmod_1.4.15 limma_3.12.1
>>>>>> annotate_1.34.1 hugene10stprobeset.db_8.0.1
>>>>>> org.Hs.eg.db_2.7.1
>>>>>>
>>>>>> [6] BiocInstaller_1.4.7 affycoretools_1.28.0
>>>> KEGG.db_2.7.1
>>>>>> GO.db_2.7.1 AnnotationDbi_1.18.1
>>>>>> [11] affy_1.34.0 Biobase_2.16.0
>>>>>> BiocGenerics_0.2.0 pd.hugene.1.0.st.v1_3.6.0
>>> RSQLite_0.11.1
>>>>>> [16] DBI_0.2-5 oligo_1.20.4
>>>>>> oligoClasses_1.18.0
>>>>>>
>>>>>>
>>>>>> rmaOligoinvivo = oligo::rma(InVivodat1)
>>>>>> Background correcting
>>>>>> Normalizing
>>>>>> Calculating Expression
>>>>>>
>>>>>>> rmaOligoinvitro = oligo::rma(InVitrodat1)
>>>>>> Background correcting
>>>>>> Normalizing
>>>>>> Calculating Expression
>>>>>>
>>>>>>> maplot(rmaOligoinvivo)
>>>>>>> maplot(rmaOligoinvitro)
>>>>>>> InVivoTargets
>>>>>> FileName Treatment
>>>>>> 1 MC1 A
>>>>>> 2 MC2 A
>>>>>> 3 MC3 A
>>>>>> 4 MC4 A
>>>>>> 5 MC5 A
>>>>>> 6 MC6 A
>>>>>> 7 MC7 R
>>>>>> 8 MC8 R
>>>>>> 9 MC9 R
>>>>>> 10 MC10 R
>>>>>> 11 MC11 R
>>>>>> 12 MC12 R
>>>>>> 13 MC13 T
>>>>>> 14 MC14 T
>>>>>> 15 MC15 T
>>>>>> 16 MC16 T
>>>>>> 17 MC17 T
>>>>>> 18 MC18 T
>>>>>>
>> InVitroTargets=readTargets("~/Desktop/Recent/Liverpool-work-related/Micro_RawData/InVitroTargets.txt")
>>>>>>> InVitroTargets
>>>>>> FileName Treatment Batch CD4
>>>>>> 1 MC19 RY 1 High
>>>>>> 2 MC20 TY 1 Low
>>>>>> 3 MC21 RY 2 High
>>>>>> 4 MC22 TY 2 High
>>>>>> 5 MC23 TY 2 Low
>>>>>> 6 MC24 RY 2 High
>>>>>> 7 MC25 TXY 1 Low
>>>>>> 8 MC26 RXY 1 High
>>>>>> 9 MC27 RXY 2 Low
>>>>>> 10 MC28 TXY 2 High
>>>>>> 11 MC29 RXY 2 High
>>>>>> 12 MC30 TXY 2 High
>>>>>>
>>>>>> f.invivo<- factor(InVivoTargets$Treatment, levels = c("A", "R",
>> "T"))
>>>>>> design.invivo<- model.matrix(~0 + f.invivo)
>>>>>>
>>>>>>> colnames(design.invivo)<- c("A", "R", "T")
>>>>>>> fit.invivo<- lmFit(rmaOligoinvivo, design.invivo)
>>>>>>> contrast.matrix.invivo<- makeContrasts(R-A, T-R, T-A,levels =
>>>>>> design.invivo)
>>>>>>
>>>>>>> fit2.invivo<- contrasts.fit(fit.invivo, contrast.matrix.invivo)
>>>>>>> fit2.invivo<-eBayes(fit2.invivo)
>>>>>>> topTable(fit2.invivo, coef = 1, adjust = "fdr")
>>>>>> ID logFC AveExpr t P.Value adj.P.Val
>>>>>> B
>>>>>>
>>>>>> 8819 7943047 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>>>>>> -2.023533
>>>>>>
>>>>>> 9675 7950951 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>>>>>> -2.023533
>>>>>>
>>>>>> 18889 8043581 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>>>>>> -2.023533
>>>>>>
>>>>>> 19899 8053785 -0.3640702 4.177681 -5.395110 3.942713e-05 0.3282013
>>>>>> -2.023533
>>>>>>
>>>>>> 3713 7896238 0.7731154 2.999029 4.796490 1.434510e-04 0.9552974
>>>>>> -2.323922
>>>>>>
>>>>>> 19926 8054075 -0.3816217 4.062936 -4.557543 2.424324e-04 0.9998796
>>>>>> -2.454618
>>>>>>
>>>>>> 18660 8041642 -1.0007299 4.220083 -4.290346 4.379518e-04 0.9998796
>>>>>> -2.607991
>>>>>>
>>>>>> 3759 7896284 -0.7555604 5.727302 -4.159251 5.861601e-04 0.9998796
>>>>>> -2.685960
>>>>>>
>>>>>> 6238 7917530 0.5596335 11.170012 4.117421 6.433789e-04 0.9998796
>>>>>> -2.711203
>>>>>>
>>>>>> 15545 8010622 -0.3324189 3.771856 -3.971869 8.899739e-04 0.9998796
>>>>>> -2.800385
>>>>>> I am progressing in a right way? Further I want to perform unpaired
>> t
>>>> test
>>>>>> for comparing AvsT and so on. Any help will be really great.
>>>>>>
>>>>> Hi, Mitra. I think that looks about right. You have already
>> performed
>>>>> the unpaired t-test of AvsT (well, actually TvsA, but the p-values
>> will
>>>> be
>>>>> the same) as coefficient 3.
>>>>>
>>>>> Sean
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Dr. Suparna Mitra
>>>> Wolfson Centre for Personalised Medicine
>>>> Department of Molecular and Clinical Pharmacology
>>>> Institute of Translational Medicine University of Liverpool
>>>> Block A: Waterhouse Buildings, L69 3GL Liverpool
>>>>
>>>> Tel. +44 (0)151 795 5394, Internal ext: 55394
>>>> M: +44 (0) 7511387895
>>>> Email id: smitra at liverpool.ac.uk
>>>> Alternative Email id: suparna.mitra.sm at gmail.com
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
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>>>>
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>>>
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>>
>>
>> --
>> Dr. Suparna Mitra
>> Wolfson Centre for Personalised Medicine
>> Department of Molecular and Clinical Pharmacology
>> Institute of Translational Medicine University of Liverpool
>> Block A: Waterhouse Buildings, L69 3GL Liverpool
>>
>> Tel. +44 (0)151 795 5394, Internal ext: 55394
>> M: +44 (0) 7511387895
>> Email id: smitra at liverpool.ac.uk
>> Alternative Email id: suparna.mitra.sm at gmail.com
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
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--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099
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