[BioC] analysis of reference design with even dye-swap across biological replicates
Aubin-Horth Nadia
Nadia.Aubin-Horth at bio.ulaval.ca
Tue Jun 21 15:42:05 CEST 2011
Jenny
Thanks for the warning. Its a good thing I am asking these questions
on the analysis before doing the actual hybs.
In the light of your comment, may I ask two additional questions:
1-If I stick with the idea of a reference design, but use the fish
from my control group as one of the treatments, and use a separate
pooled sample as a reference, I will now be able to hybridize 6 fish
from each treatment as in the following target file:
1 ref control.1
2 control.2 ref
3 ref control.3
4 control.4 ref
5 ref control.5
6 control.6 ref
7 ref treat1.1
8 treat1.2 ref
9 ref treat1.3
10 treat1.4 ref
11 ref treat1.5
12 treat1.6 ref
13 ref treat2.1
14 treat2.2 ref
15 ref treat2.3
16 treat2.4 ref
17 ref treat2.5
18 treat2.6 ref
19 ref treat3.1
20 treat3.2 ref
21 ref treat3.3
22 treat3.4 ref
23 ref treat3.5
24 treat3.6 ref
As you can see I still have even dye bias for a given treatment (3
fish in cy5 and 3 fish in cy3) but no technical replicate for a given
fish. Could I then analyse
design <- modelMatrix(targets, ref = "ref")
design <- cbind(Dye = 1, design)
fit <- lmFit(MA, design)
contrast.matrix <- makeContrasts(((treat1.1 + treat1.2 + treat1.3 +
treat1.4 + treat1.5 + treat1.6)-(control.1 + control.2 + control.3 +
control.4 + control.5 + control.6))/6, levels=design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
topTable(fit2, adjust = "BH")
and include the dye effect? would my contrast be correct?
2-You suggest that I do a standard loop design. However, the control
will not be directly hybridized with all treatments with 24 hybs ( at
least not with 6 or 8 biological replicates), unless I am not
understanding how you would do the loop design? I have done loop
designs before but I wanted comparisons of all groups to each other,
here I am really interested in the contrast between each treatment and
the control. Using less arrays and a loop design would be great and I
am not attached to reference designs per se, but I want to make sure
that I have the optimal statistical power for the contrast of interest.
Thank you
Nadia
Nadia Aubin-Horth
Assistant professor
Biology Department
Institute of Integrative and Systems Biology
Room 1241, Charles-Eugène-Marchand Building
1030, Ave. de la Médecine
Laval University
Quebec City (QC) G1V 0A6
Canada
Phone: 418.656.3316
Fax: 418.656.7176
web page: http://wikiaubinhorth.ibis.ulaval.ca/Main_Page
On Jun 20, 2011, at 2:46 PM, Jenny Drnevich wrote:
> Hi Nadia,
>
> If the main goal of your experiment is to compare
> each of the treatments to the control, then DO
> NOT pool the control samples! Even though you do
> not care about individual variation, you cannot
> do an accurate statistical test of the difference
> of the means with out the estimate of the
> variance within the controls. Do a standard
> loop-design and make sure the groups are
> dye-balanced (4 replicates in each dye); you do
> not need to do technical dye-swaps to account for
> the dye effect in the model. This will give 4
> groups * 8 replicates / 2 channels = 16 arrays.
>
> That's my 2-cents,
> Jenny
>
> At 01:31 PM 6/20/2011, Aubin-Horth Nadia wrote:
>> Hi everybody,
>>
>> I am planning to analyse a microarray experiment (Agilent, 2 colors)
>> and I would like to make sure I can include dye effect with the hyb
>> design used.
>>
>> I have 4 groups: a control group ("wild type") and 3 treatments. We
>> are interested by the effect of each treatment on gene expression
>> compared to the control. My plan is to maximize the statistical power
>> to find differences between the control and each treatment by using a
>> reference design and having the control in each hyb. Of course, I
>> loose statistical power to find differences between treatments.
>>
>> I have 8 biological replicates (fish) per group available.
>>
>> I am interested to know if I can correctly take dye-bias into account
>> using LIMMA and the following design. I am not interested in
>> individual gene expression level, only mean and variance for each
>> treatment.
>>
>> The 24 hybs are performed using the control group (all 8 individuals
>> pooled) as the reference and the 8 individuals from each of the 3
>> treatments used in only one hyb (no technical replicate). For each
>> treatment, 4 biological replicates would be labelled in cye 3 and 4
>> biological replicates would be labelled in cy5 (assigned at random
>> within treatment). I would thus get an even design in terms of dye
>> labelling for the reference and the treatments,
>> but no dye swap/ technical replicate for a
>> specific fish. The goal is to capture as
>> much biological variance here (8 fish instead of 4 fish with dye
>> swap)
>> for the 24 hybs we can do.
>>
>> The target file would look like this (T1, T2 and T3 are treatments
>> and
>> the following number represents a biological replicate)
>> HYB CY3 Cy5
>> 1 ref T1.1
>> 2 ref T1.2
>> 3 ref T1.3
>> 4 ref T1.4
>> 5 T1.5 ref
>> 6 T1.6 ref
>> 7 T1.7 ref
>> 8 T1.8 ref
>> 9 ref T2.1
>> 10 ref T2.2
>> 11 ref T2.3
>> 12 ref T2.4
>> 13 T2.5 ref
>> 14 T2.6 ref
>> 15 T2.7 ref
>> 16 T2.8 ref
>> 17 ref T3.1
>> 18 ref T3.2
>> 19 ref T3.3
>> 20 ref T3.4
>> 21 T3.5 ref
>> 22 T3.6 ref
>> 23 T3.7 ref
>> 24 T3.8 ref
>>
>> The comparison of interest is the average difference between the
>> control and a given treatment , including dye effects
>>
>> I thought I could then use the example as in section 7.3 of limma
>> user
>> guide on common reference design but including multiple biological
>> replicates and a dye effect (from section 8.2)
>>
>> Here the contrast matrix is made for treatment 1, T1
>>
>> design <- modelMatrix(targets, ref = "ref")
>> design <- cbind(Dye = 1, design)
>> fit <- lmFit(MA, design)
>> cont.matrix <-
>> makeContrasts((T1.1+T1.2+T1.3+T1.4+T1.5+T1.6+T1.7+T1.8)/ 8, levels
>> = design)
>> fit2 <- contrasts.fit(fit, cont.matrix)
>> fit2 <- eBayes(fit2)
>> topTable(fit2, adjust = "BH")
>>
>> Could someone please tell me if
>> 1) the contrast is appropriate?
>> 2) it will be possible to estimate the dye effect as presented in the
>> manual with my own hybridization design?
>>
>> The hybs have not been performed yet but I assume that one can still
>> tell if the design is balanced. I could use a loop design as is
>> normally used in our lab but as I simply want to know what is the
>> effect of each treatment, I though a reference design was
>> appropriate,
>> especially with such a large number of biological replicates.
>>
>> Thank you!
>>
>> Nadia Aubin-Horth
>> Assistant professor
>> Biology Department
>> Institute of Integrative and Systems Biology
>> Room 1241, Charles-Eugène-Marchand Building
>> 1030, Ave. de la Médecine
>> Laval University
>> Quebec City (QC) G1V 0A6
>> Canada
>>
>> Phone: 418.656.3316
>> Fax: 418.656.7176
>>
>> web page: http://wikiaubinhorth.ibis.ulaval.ca/Main_Page
>>
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