Question: Question on unbalanced paired design

0

Sandhya Pemmasani Kiran •

**40**wrote:
Dear List:
We are analyzing Agilent microarray data for a study where samples are
related. After Quantile normalization on 'gProcessedSignal', averaging
replicate spots and log transformation, we are trying to use LIMMA for
differential expression analysis.
Design is as follows-
4 Treatment groups - A, B, C and D
3 Doses per Treatment group, but 4 doses for Treatment A (Total 13
Treatment-Dose combinations)
There are 8 patient samples in each Treatment-Dose combination (Total
104 samples)
We are interested in comparing Dose effects within Treatments and
overlaps across Treatment-Dose combinations. No Treatment comparisons
like A vs. B
Patient samples are related within a Treatment group. But they differ
from treatment to treatment. So, this is a nested design, but samples
are related/paired. These samples are coming from 32 patients.
Out of 104 samples, 12 samples failed in Extraction/Hybridization QC
and we are currently analyzing 92 samples. We missed few of the paired
samples in each Treatment-Dose group.
Here are the few lines of targets file (attached is full targets
file)-
SampleName Trt Dose SibShip
A-01-001 A 1 1
A-03-001 A 3 1
A-04-001 A 4 1
A-01-012 A 1 6
A-02-012 A 2 6
A-04-012 A 4 6
A-01-031 A 1 14
A-02-031 A 2 14
A-03-031 A 3 14
A-04-031 A 4 14
A-01-040 A 1 17
A-02-040 A 2 17
A-03-040 A 3 17
A-04-040 A 4 17
. . . .
. . . .
. . . .
B-01-013 B 1 7
B-02-013 B 2 7
B-03-013 B 3 7
B-01-016 B 1 10
B-02-016 B 2 10
B-03-016 B 3 10
B-01-024 B 1 12
B-02-024 B 2 12
B-03-024 B 3 12
. . . .
. . . .
R-code-
-------------
targets_design = readTargets("targets_design.txt")
> TD <- factor(paste(targets_design$Trt, targets_design$Dose,
sep="_"))
> Sibship <- factor(targets_design$SibShip)
> design <- model.matrix(~0+TD+Sibship)
> fit <- lmFit(ldt, design)
Coefficients not estimable: Sibship27 Sibship31 Sibship32
Warning message:
Partial NA coefficients for 34127 probe(s)
> cont.matrix <- makeContrasts(
+ TDA_2 - TDA_1,
+ TDA_3 - TDA_2,
+ TDA_4 - TDA_3,
+ TDB_2 - TDB_1,
+ TDB_3 - TDB_2,
+ levels = design)
> fit1 <- contrasts.fit(fit, cont.matrix)
> fit2 <- eBayes(fit1)
> fit2$coefficients[1:5,]
Contrasts
TDA_2 - TDA_1 TDA_3 - TDA_2 TDA_4 - TDA_3 TDB_2 - TDB_1
TDB_3 - TDB_2
A_23_P146146 -0.2176523 0.14287127 -0.05801898 0.3476315
-0..25312193
A_23_P42935 0.1718808 0.18653560 -0.20015286 -0.2664990
-0..04537665
A_23_P117082 0.1545347 0.32006311 -0.16050816 1.0063268
-1..01438229
A_23_P2683 -0.2549002 -0.16453369 0.27796574 0.2916715
-0..79682996
A_24_P358131 -0.4647673 0.09824839 0.22298962 -0.4026419
0..53349466
When I run the above code taking patient samples for which we have
observations on all treatments, it seems to be correct- because logFC
values are matching with my calculations. So, my design matrix is
correct ???
But, when I include, all the samples (92), logFC values are not
matching, because of unbalanced data and LIMMA doesn't ignore non-
paired samples, as discussed in
https://stat.ethz.ch/pipermail/bioconductor/2011-August/040875.html
Should I go ahead with analysis (thinking that design matrix is
correct) or is it better to do individual paired t-tests, ignoring
data from non-paired samples at each comparison level?
Can you suggest an easy way to explain to non-statisticians that why
values are not matching.
Thanks,
Sandhya
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modified 4.9 years ago
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Ryan C. Thompson ♦

**6.3k**• written 4.9 years ago by Sandhya Pemmasani Kiran •**40**