Thanks Jim,
 I will try to do this. being a novice I am trying hard to find best
possible analysis option. I know this experiment is complicated.
Thanks for help.
best wishes,
Mitra


On 7 September 2012 14:55, James W. MacDonald <jmacdon@uw.edu> wrote:

>
>
> On 9/7/2012 7:26 AM, Sean Davis wrote:
>
>> On Fri, Sep 7, 2012 at 7:14 AM, suparna mitra<smitra@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@mail.nih.gov>  wrote:
>>>
>>>  On Fri, Sep 7, 2012 at 6:57 AM, suparna mitra<smitra@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@mail.nih.gov>  wrote:
>>>>>
>>>>>
>>>>>> On Fri, Sep 7, 2012 at 5:54 AM, suparna mitra<
>>>>>>
>>>>> smitra@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@liverpool.ac.uk
>>>>> Alternative Email id: suparna.mitra.sm@gmail.com
>>>>>
>>>>>          [[alternative HTML version deleted]]
>>>>>
>>>>> ______________________________**_________________
>>>>> Bioconductor mailing list
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>>>>> Search the archives:
>>>>> http://news.gmane.org/gmane.**science.biology.informatics.**conductor<http://news.gmane.org/gmane.science.biology.informatics.conductor>
>>>>>
>>>>>           [[alternative HTML version deleted]]
>>>>
>>>> ______________________________**_________________
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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@liverpool.ac.uk
>>> Alternative Email id: suparna.mitra.sm@gmail.com
>>>
>>> ______________________________**_________________
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>>>
>>>          [[alternative HTML version deleted]]
>>
>> ______________________________**_________________
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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
>
>


-- 
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@liverpool.ac.uk
Alternative Email id: suparna.mitra.sm@gmail.com

	[[alternative HTML version deleted]]

