Hello, I'm having trouble constructing a proper design matrix for statistical analyses.
I am working with 2-color Agilent array data for multiple arrays. I've bg-corrected and normalized the expression values.
However, the design of the arrays are not uniform.
The goal of the experiment is to see if there's differential expression between pre-treatment and post-treatment with a drug.
However, the microarrays were formatted as follows:
pre = pre- drug treatment
post = post- drug treatment
file Cy3 Cy5
Array 1 file1.txt pre-sample1 pre-sample2
Array 2 file2.txt pre-sample3 post-sample3
Array 3 file3.txt pre-sample4 pre-sample5
Array 4 file4.txt post-sample6 pre-sample6
Array 5 file5.txt post-sample7 pre-sample7
Array 6 file6.txt pre-sample8 post-sample8
I've been following this guide http://koti.mbnet.fi/tuimala/oppaat/r2.pdf and I'm a bit lost to what the design matrix would look entail.
Is there any way to design a matrix with the pre to pre arrays in there or do I need to repeat the analysis twice, one analysis with just Array 1 & 3, a second analysis with the remaining groups?
Any guidance would be appreciated.
edit: is design <- modelMatrix(targets, ref="pre") an accurate design matrix?
