[BioC] multi-level experiments - limma

[Rao,Xiayu]

Hello,

Sorry that I bring up this again, but I do want to know if my logic is correct or not, with regard to subset a big data set according to different research questions and start from normalization to set up different design matrixes and contrasts. I am particularly unsure about the 2nd question of comparing normal and tumor type I (AR+), and how pairing and batch effect should be addressed? Any comments or suggestions would be very appreciated. Thank you very much in advance.

Thanks,
Xiayu



-----Original Message-----
From: bioconductor-bounces at r-project.org [mailto:bioconductor-bounces at r-project.org] On Behalf Of Rao,Xiayu
Sent: Monday, July 28, 2014 4:55 PM
To: bioconductor at r-project.org
Subject: [BioC] multi-level design and contrasts problem - limma

Hello,

I read the limma user guide on the topics of multi-level experiments and found the information very useful. But my design is a little more complicated, and I would like to consult for a solution.
I was asked to solve the following questions regarding the data structure below (targets.txt). I guess I need to set up different design matrixes according to different questions?

1)      Normal vs tumor:    Do I subset the data into paired samples (subject) only and then used the paired design since some samples do not have their normal samples? There is only 1 or 2 patients with Tumor and normal samples in different chips. Can I just do pairing and ignore the batch effect (chip), as I read in the forum that doing both does no good since most pairs are within the same chip.

2)      Normal vs AR positive tumor:    Only tumor samples have AR information. I am thinking to pool type and AR together into 1 column called type_AR with 3 categories: tumorNeg, tumorPos, and normal. I will use design <- model.matrix(~subject+type_AR) and set contrasts normal-tumorPos for (2) and normal-tumorNeg for (3). Or I should follow the multi-level design instructions to include the type_AR and chip in the design (paste the two), and then use duplicateCorrelation() on subject? I will ignore gender.

3)      Normal vs AR negative tumor:     same above.

4)      AR positive vs AR negative tumor:    I am thinking to remove all normal samples and ignore type, subject and gender. The design would be = model.matrix(~chip+AR), right?

5)      Male AR positive vs Female AR positive:    One way is to remove all normal and AR negative samples (only gender and chip left), and compare Female and Male using design <- model.matrix(~chip+gender). The 2nd way is to follow multi-level design instructions to allow more comparisons (including AR negative):

Treat <- factor(paste(targets$gender,targets$AR,sep="."))

design <- model.matrix(~0+Treat)

duplicateCorrelation(eset,design,block=targets$chip)
Please let me know if I am on the right track.  Thank you very much!


Targets.txt:
sample type       subject gender AR          chip
s1            tumor   1              M            neg        1
s2            normal  1              M                            1
s3            tumor   2              M            pos         1
s4            normal  2              M                            1
s5            tumor   3              F              neg        1
s6            normal  3              F                              1
s7            tumor   4              M            pos         1
s8            normal  4              M                            1
s9            tumor   5              M            pos         2
s10         normal  5              M                            2
s11         normal  6              F                              2
s12         tumor   7              M            pos         2
s13         normal  7              M                            2
s14         tumor   8              M            pos         2
s15         normal  8              M                            2
s16         tumor   9              M            neg        3
s17         tumor   10           M            neg        3
s18         tumor   11           F              neg        3
s19         tumor   6              F              pos         3
s20         tumor   12           F              pos         3
s21         tumor   13           F              neg        3
s22         tumor   14           F              pos         3


Thanks,
Xiayu

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