Entering edit mode
Hello,
I am analysing microarray data collected with Affymetrix MouseGene 2.0
ST chips. I have a few questions about properly using limma. I have
four groups with three replicates. The groups are Control, Treatment
#1 & #2, Treatment #1, and Treatment #2. I may not have the proper
design matrix. I am not familiar with their use in linear regression.
Currently, my design matrix is set up like this:
# Design matrix for Limma
design <- model.matrix(~ 0+factor(c(1,1,1,2,2,2,3,3,3,4,4,4)))
colnames(design) <- c("Control", "Group1", "Group2", "Group3")
> design
Control Group1 Group2 Group3
1 1 0 0 0
2 1 0 0 0
3 1 0 0 0
4 0 1 0 0
5 0 1 0 0
6 0 1 0 0
7 0 0 1 0
8 0 0 1 0
9 0 0 1 0
10 0 0 0 1
11 0 0 0 1
12 0 0 0 1
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`factor(c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4))`
[1] "contr.treatment"
This was modified directly from the limma users guide page 36. Should
the control group be all 1's? and should Group1 (treatment 1 & 2) be
1's from row 7:12? I would like to find genes different from control
and I would like to find genes differentially expressed between the
combination of treatments versus each treatment alone.
My Contrasts matrix is set up like this:
# Limma contrast matrix more than 5, no Venn diagrams.
contrast.matrix <- makeContrasts(Group1-Control, Group2-Control,
Group3-Control, Group3-Group2, Group3-Group1, Group2-Group1,
levels=design)
> contrast.matrix
Contrasts
Levels Group1 - Control Group2 - Control Group3 - Control Group3 -
Group2
Control -1 -1 -1
0
Group1 1 0 0
0
Group2 0 1 0
-1
Group3 0 0 1
1
Contrasts
Levels Group3 - Group1 Group2 - Group1
Control 0 0
Group1 -1 -1
Group2 0 1
Group3 1 0
Is there a better way to relate the fact that Group 2 is a combination
of treatment 1 and treatment 2?
Thanks!
Matt
Matthew E. Thornton
Research Lab Specialist
Saban Research Institute
USC/Children?s Hospital Los Angeles
513X, Mail Stop 35
4661 W. Sunset Blvd.
Los Angeles, CA 90027-6020
matthew.thornton at med.usc.edu