[BioC] design matrix Limma design for paired t-test
Ingrid Mercier
Ingrid.Mercier at ipbs.fr
Fri Jun 15 11:55:59 CEST 2012
Thanks Moshe for your reply !
It's very clear ! As you wrote, I want to test is " if the effect of
the treatment at 4 hours is different from the one at 18 hours, between
Control and Treated cells ", but I don't see how change my design.
Somebody can help me ?
Cheers,
Ingrid
Ingrid MERCIER
Mycobacterial Interactions with Host Cells Team
Institute of Pharmacology& Structural Biology
CNRS - University of Toulouse
BP 64182
F-31077 Toulouse Cedex France
Tel +33 (0)5 61 17 54 63
Le 14/06/2012 03:44, Moshe Olshansky a écrit :
> Hi Ingrid,
>
> With your design your "base" level is patient 4, Control, 4 hours (let's
> call it B).
> The mean for, say, patient 6, Treatment, 18 hours is:
> B + Donor6 + TreatT + Time18
> where Donor6 is the difference between Donor4 and Donor6 (same for any
> treatment and time), TreatT is the difference between Treatment and
> Control (independent of patient and time) and Time18 is the difference
> between 18 hours and 4 hours (independent of patient and treatment).
>
> If you think that the effect of Treatment versus Control is the same at 4
> hours and 18 hours, then what you did is all right. If you think that the
> effect of the treatment at 4 hours may be different from the one at 18
> hours, you need to change your design.
>
> Best regards,
> Moshe.
>
>> Thanks a lot Belinda !!
>>
>> I mistaked so I replaced Time=Treat by Time only, and it's good.
>> So, I have a last question : I 'm confused with the differents coef in
>> topTable.
>> I get genes but I tested several coef without understanding their
>> significance.
>> Somebody can explain me what mean coef="TreatT", or coef= "Time18",coef=
>> " Donor5",coef= " Donor6", coef= "Donor7",coef= " Donor8".
>> My main objective is to identidy the differential expressed genes
>> between the Control donors and Treated Donors at 4 hours or 18 hours.
>> I have no idea, which coef I have to use it.
>>
>> Cheers,
>>
>> Ingrid
>>
>> Ingrid MERCIER
>> Mycobacterial Interactions with Host Cells Team
>> Institute of Pharmacology& Structural Biology
>> CNRS - University of Toulouse
>> BP 64182
>> F-31077 Toulouse Cedex France
>> Tel +33 (0)5 61 17 54 63
>>
>>
>>
>>
>> Le 13/06/2012 08:45, Belinda Phipson a écrit :
>>> Hi Ingrid
>>>
>>> The problem with your code is the following line:
>>>> Time=Treat=factor(Targets$Time)
>>> Where you essentially set the time factor equal to the treat factor.
>>>
>>> Cheers,
>>> Belinda
>>>
>>>
>>> -----Original Message-----
>>> From:bioconductor-bounces at r-project.org
>>> [mailto:bioconductor-bounces at r-project.org] On Behalf Of Ingrid Mercier
>>> Sent: Wednesday, 13 June 2012 1:02 AM
>>> To:bioconductor at r-project.org;smyth at wehi.edu.au
>>> Subject: [BioC] design matrix Limma design for paired t-test
>>>
>>> Dear list and Gordon,
>>>
>>> I have some troubles to computed a moderated paired t-test in the linear
>>> model.
>>> Here is my experimental plan :
>>>
>>> I used a single channel Agilent microarray.
>>> 2 types of cells : Control (S) and Treated (T)
>>> Fives human donors : 4-5-6-7-8
>>> Two times of treatment : 4 hours and 18 hours
>>>
>>> I want to compare teh differential expresed genes between my C versus T
>>> at 4
>>> hours and then at 18 hours.
>>>
>>> Here is my design :
>>>
>>>
>>> My targets frame is :
>>>> Targets
>>> X FileName
>>> Treatment
>>> Donor Time
>>> 1 DC_4_4 US10463851_252665214446_S01_GE1_1010_Sep10_1_2.txt T
>>> 4 4
>>> 2 SC_4_4 US10463851_252665214448_S01_GE1_1010_Sep10_1_2.txt C
>>> 4 4
>>> 3 DC_18_4 US10463851_252665214447_S01_GE1_1010_Sep10_1_2.txt T
>>> 4 18
>>> 4 SC_18_4 US10463851_252665214444_S01_GE1_1010_Sep10_1_3.txt C
>>> 4 18
>>> 5 DC_4_5 US10463851_252665214448_S01_GE1_1010_Sep10_1_4.txt T
>>> 5 4
>>> 6 SC_4_5 US10463851_252665214444_S01_GE1_1010_Sep10_1_1.txt C
>>> 5 4
>>> 7 DC_18_5 US10463851_252665214446_S01_GE1_1010_Sep10_1_3.txt T
>>> 5 18
>>> 8 SC_18_5 US10463851_252665214447_S01_GE1_1010_Sep10_1_4.txt C
>>> 5 18
>>> 9 DC_4_6 US10463851_252665214445_S01_GE1_1010_Sep10_1_4.txt T
>>> 6 4
>>> 10 SC_4_6 US10463851_252665214447_S01_GE1_1010_Sep10_1_3.txt C
>>> 6 4
>>> 11 DC_18_6 US10463851_252665214448_S01_GE1_1010_Sep10_1_3.txt T
>>> 6 18
>>> 12 SC_18_6 US10463851_252665214445_S01_GE1_1010_Sep10_1_3.txt C
>>> 6 18
>>> 13 DC_4_7 US10463851_252665214444_S01_GE1_1010_Sep10_1_4.txt T
>>> 7 4
>>> 14 SC_4_7 US10463851_252665214445_S01_GE1_1010_Sep10_1_2.txt C
>>> 7 4
>>> 15 DC_18_7 US10463851_252665214447_S01_GE1_1010_Sep10_1_1.txt T
>>> 7 18
>>> 16 SC_18_7 US10463851_252665214446_S01_GE1_1010_Sep10_1_1.txt C
>>> 7 18
>>> 17 DC_4_8 US10463851_252665214444_S01_GE1_1010_Sep10_1_2.txt T
>>> 8 4
>>> 18 SC_4_8 US10463851_252665214446_S01_GE1_1010_Sep10_1_4.txt C
>>> 8 4
>>> 19 DC_18_8 US10463851_252665214445_S01_GE1_1010_Sep10_1_1.txt T
>>> 8 18
>>> 20 SC_18_8 US10463851_252665214448_S01_GE1_1010_Sep10_1_1.txt C
>>> 8 18
>>>
>>>
>>> then I create my design matrix :
>>>
>>>> Donor
>>> [1] 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8
>>> Levels: 4 5 6 7 8
>>>> Treat=factor(Targets$Treatment,levels=c("C","T"))
>>>> Treat
>>> [1] T C T C T C T C T C T C T C T C T C T C
>>> Levels: C T
>>>> Time=Treat=factor(Targets$Time)
>>>> Time
>>> [1] 4 4 18 18 4 4 18 18 4 4 18 18 4 4 18 18 4 4 18 18
>>> Levels: 4 18
>>>
>>>> design=model.matrix(~Donor+Treat+Time)
>>>> design
>>> (Intercept) Donor5 Donor6 Donor7 Donor8 Treat18 Time18
>>> 1 1 0 0 0 0 0 0
>>> 2 1 0 0 0 0 0 0
>>> 3 1 0 0 0 0 1 1
>>> 4 1 0 0 0 0 1 1
>>> 5 1 1 0 0 0 0 0
>>> 6 1 1 0 0 0 0 0
>>> 7 1 1 0 0 0 1 1
>>> 8 1 1 0 0 0 1 1
>>> 9 1 0 1 0 0 0 0
>>> 10 1 0 1 0 0 0 0
>>> 11 1 0 1 0 0 1 1
>>> 12 1 0 1 0 0 1 1
>>> 13 1 0 0 1 0 0 0
>>> 14 1 0 0 1 0 0 0
>>> 15 1 0 0 1 0 1 1
>>> 16 1 0 0 1 0 1 1
>>> 17 1 0 0 0 1 0 0
>>> 18 1 0 0 0 1 0 0
>>> 19 1 0 0 0 1 1 1
>>> 20 1 0 0 0 1 1 1
>>> attr(,"assign")
>>> [1] 0 1 1 1 1 2 3
>>> attr(,"contrasts")
>>> attr(,"contrasts")$Donor
>>> [1] "contr.treatment"
>>>
>>> attr(,"contrasts")$Treat
>>> [1] "contr.treatment"
>>>
>>> attr(,"contrasts")$Time
>>> [1] "contr.treatment"
>>>
>>>
>>> In this design matrix I think something is wrong, because of the column
>>> Treat18 is the same as Time18.
>>> I don't understand why.
>>> So, the following code failed, and the differential expressed genes are
>>> odds.
>>>
>>> Somebody can help me !!! Thanks all.
>>>
>>>
>>>> fit=lmFit(test_norm,design)
>>> Coefficients not estimable: Time18
>>> Message d'avis :
>>> Partial NA coefficients for 34183 probe(s)
>>>> fit2=eBayes(fit)
>>> Message d'avis :
>>> In ebayes(fit = fit, proportion = proportion, stdev.coef.lim =
>>> stdev.coef.lim, :
>>> Estimation of var.prior failed - set to default value
>>>
>>>
>>>> table = topTable(fit2,1, number=5000,
>>> p.value=0.05,adjust.method="BH",sort.by="logFC",lfc=2)
>>>> head(table)
>>> ID logFC AveExpr t P.Value
>>> adj.P.Val
>>> B
>>> 6509 A_33_P3396434 18.44159 18.41239 245.14490 1.308161e-31
>>> 2.353520e-28
>>> 53.41519
>>> 22398 A_33_P3223592 18.25824 18.24591 242.75647 1.545005e-31
>>> 2.514901e-28
>>> 53.36821
>>> 10771 A_33_P3244165 18.21029 18.02229 90.76191 2.796577e-24
>>> 2.467615e-23
>>> 44.59915
>>> 6149 A_33_P3346552 18.14780 18.12098 207.18556 2.282464e-30
>>> 1.147374e-27
>>> 52.50960
>>> 23554 A_33_P3210160 18.08158 18.21026 239.64192 1.924175e-31
>>> 2.560908e-28
>>> 53.30521
>>> 20924 A_33_P3286278 18.04425 18.07312 179.72121 2.558128e-29
>>> 5.025546e-27
>>> 51.56876
>>>
>>>
>>> Best,
>>>
>>> Ingrid
>>>
>>>
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