[BioC] choosing the right model in limma

[Lina Hultin-Rosenberg]

Dear list,

I am analyzing some affymetrix chicken data using limma and have a question
on the best approach regarding random and fixed effects. The target matrix
is as follows:

samplenames					sex		tissue	date
individual
PA009_kyckling_11_H16_060630.CEL	male		heart		1
16
PA009_kyckling_11_H19_060705.CEL	male		heart		3
19
PA009_kyckling_11_H21_060630.CEL	male		heart		1
21
PA009_kyckling_11_H9_060704.CEL	male		heart		2	9
PA009_kyckling_12_B16_060704.CEL	male		brain		2
16
PA009_kyckling_12_B19_060704.CEL	male		brain		2
19
PA009_kyckling_12_B21_060705.CEL	male		brain		3
21
PA009_kyckling_12_B9_060630.CEL	male		brain		1	9
PA009_kyckling_13_G16_060705.CEL	male		gonad		3
16
PA009_kyckling_13_G19_060630.CEL	male		gonad		1
19
PA009_kyckling_13_G21_060704.CEL	male		gonad		2
21
PA009_kyckling_13_G9_060705.CEL	male		gonad		3	9
PA009_kyckling_21_H10_060705.CEL	female	heart		3	10
PA009_kyckling_21_H12_060705.CEL	female	heart		3	12
PA009_kyckling_21_H20_060630.CEL	female	heart		1	20
PA009_kyckling_21_H2_060704.CEL	female	heart		2	2
PA009_kyckling_22_B10_060704.CEL	female	brain		2	10
PA009_kyckling_22_B12_060630.CEL	female	brain		1	12
PA009_kyckling_22_B20_060705.CEL	female	brain		3	20
PA009_kyckling_22_B2_060630.CEL	female	brain		1	2
PA009_kyckling_23_G10_060704.CEL	female	gonad		2	10
PA009_kyckling_23_G12_060630.CEL	female	gonad		1	12
PA009_kyckling_23_G20_060704.CEL	female	gonad		2	20
PA009_kyckling_23_G2_060705.CEL	female	gonad		3	2

The question of interest is what genes that differ between male and female
in the different tissues and as well in general. My concern is if I have to
block for the date/batch and individual effect. In a PCA plot (and other
quality control plots) there isn't sign of any obvious batch or individual
effect. I also used duplicateCorrelation to calculate the correlations for
the batch and individual effects and the results were 0.1 for individual and
-0.03 for batch. Would it be ok to exclude the batch effect from the model
and treat the individual as a random effect or is there a way in limma to
include two random effects?

I also have a more general question regarding lmFit and eBayes. I fitted a
model to the gonad samples only and then compared that to fitting a model to
all samples and extracting the gonad contrast only (see design matrices
below). Obviously the resulting p-values etc differ between the two
approaches but I don't really understand the difference and know which is
the preferred/correct approach.

Only gonad samples:
						m	f
PA009_kyckling_13_G16_060705.CEL	1	0
PA009_kyckling_13_G19_060630.CEL	1	0
PA009_kyckling_13_G21_060704.CEL	1	0
PA009_kyckling_13_G9_060705.CEL	1	0
PA009_kyckling_23_G10_060704.CEL	0	1
PA009_kyckling_23_G12_060630.CEL	0	1
PA009_kyckling_23_G20_060704.CEL	0	1
PA009_kyckling_23_G2_060705.CEL	0	1

All samples:
						mh	mb	mg	fh
fb	fg
PA009_kyckling_11_H16_060630.CEL	1	0	0	0	0
0
PA009_kyckling_11_H19_060705.CEL	1	0	0	0	0
0
PA009_kyckling_11_H21_060630.CEL	1	0	0	0	0
0
PA009_kyckling_11_H9_060704.CEL	1	0	0	0	0	0
PA009_kyckling_12_B16_060704.CEL	0	1	0	0	0
0
PA009_kyckling_12_B19_060704.CEL	0	1	0	0	0
0
PA009_kyckling_12_B21_060705.CEL	0	1	0	0	0
0
PA009_kyckling_12_B9_060630.CEL	0	1	0	0	0	0
PA009_kyckling_13_G16_060705.CEL	0	0	1	0	0
0
PA009_kyckling_13_G19_060630.CEL	0	0	1	0	0
0
PA009_kyckling_13_G21_060704.CEL	0	0	1	0	0
0
PA009_kyckling_13_G9_060705.CEL	0	0	1	0	0	0
PA009_kyckling_21_H10_060705.CEL	0	0	0	1	0
0
PA009_kyckling_21_H12_060705.CEL	0	0	0	1	0
0
PA009_kyckling_21_H20_060630.CEL	0	0	0	1	0
0
PA009_kyckling_21_H2_060704.CEL	0	0	0	1	0	0
PA009_kyckling_22_B10_060704.CEL	0	0	0	0	1
0
PA009_kyckling_22_B12_060630.CEL	0	0	0	0	1
0
PA009_kyckling_22_B20_060705.CEL	0	0	0	0	1
0
PA009_kyckling_22_B2_060630.CEL	0	0	0	0	1	0
PA009_kyckling_23_G10_060704.CEL	0	0	0	0	0
1
PA009_kyckling_23_G12_060630.CEL	0	0	0	0	0
1
PA009_kyckling_23_G20_060704.CEL	0	0	0	0	0
1
PA009_kyckling_23_G2_060705.CEL	0	0	0	0	0	1


Any comments or suggestions would be greatly appreciated. Thank you!

Best regards,
Lina Rosenberg