Dear List,

The experiment I have has the following description table:

Treatment SampelingPoint Subjects SampleNames
placebo_20 20 1 placebo_20_01
placebo_22 22 1 placebo_22_01
celltype2_20 20 2 celltype2_20_02
celltype2_22 22 2 celltype2_22_02
celltype2_20 20 3 celltype2_20_03
celltype2_22 22 3 celltype2_22_03
celltype2_20 20 4 celltype2_20_04
celltype2_22 22 4 celltype2_22_04
placebo_20 20 5 placebo_20_05
placebo_22 22 5 placebo_22_05
celltype2_20 20 7 celltype2_20_07
celltype2_22 22 7 celltype2_22_07
celltype2_20 20 8 celltype2_20_08
celltype2_22 22 8 celltype2_22_08
placebo_20 20 9 placebo_20_09
placebo_22 22 9 placebo_22_09
placebo_20 20 11 placebo_20_11
placebo_22 22 11 placebo_22_11
placebo_20 20 12 placebo_20_12
placebo_22 22 12 placebo_22_12
placebo_20 20 13 placebo_20_13
placebo_22 22 13 placebo_22_13
placebo_20 20 14 placebo_20_14
placebo_22 22 14 placebo_22_14
placebo_20 20 15 placebo_20_15
placebo_22 22 15 placebo_22_15
placebo_20 20 16 placebo_20_16
placebo_22 22 16 placebo_22_16
celltype2_20 20 17 celltype2_20_17
celltype2_22 22 17 celltype2_22_17
placebo_20 20 18 placebo_20_18
placebo_22 22 18 placebo_22_18
celltype2_20 20 19 celltype2_20_19
celltype2_22 22 19 celltype2_22_19
celltype2_20 20 20 celltype2_20_20
celltype2_22 22 20 celltype2_22_20
celltype2_20 20 21 celltype2_20_21
celltype2_22 22 21 celltype2_22_21
celltype2_20 20 24 celltype2_20_24
celltype2_22 22 24 celltype2_22_24
celltype2_20 20 25 celltype2_20_25
celltype2_22 22 25 celltype2_22_25
placebo_20 20 26 placebo_20_26
placebo_22 22 26 placebo_22_26
placebo_20 20 27 placebo_20_27
placebo_22 22 27 placebo_22_27
celltype2_20 20 28 celltype2_20_28
celltype2_22 22 28 celltype2_22_28
celltype2_20 20 29 celltype2_20_29
celltype2_22 22 29 celltype2_22_29
placebo_20 20 30 placebo_20_30
placebo_22 22 30 placebo_22_30
placebo_20 20 31 placebo_20_31
placebo_22 22 31 placebo_22_31
placebo_20 20 32 placebo_20_32
placebo_22 22 32 placebo_22_32
celltype2_20 20 33 celltype2_20_33
celltype2_22 22 33 celltype2_22_33
celltype2_20 20 34 celltype2_20_34
celltype2_22 22 34 celltype2_22_34


I want to see the effect of the samplingPoint within a treatment,
because we give the subjects a substance in between day 20 and day 22.
In other words, we are interested in the effect of the substance (comparing
day 20 versus day 22). At both samplingPoints (day 20 and day 22)
1 sample was drawn.  I want to give the analyses extra power
by taking into account that the comparison between day 20 and 22
can be made within the same individual (1 individual is measured twice).

So my comparisons are:
- "placebo_20 – placebo_22"
- "celltype2_20 – celltype2_22"

I am using 8.3 Paired Samples of the limma user guide as my guide for the
limma design setup.

library("limma")

# Give levels. Will be the names (the unique names of the bio.replicates)
of the columns
lev <- c( sort(as.character(unique(description$Treatment))))
> lev
[1] "substance_20"    "substance_22"    "placebo_20" "placebo_22"

> Subjects <- factor(description$Subjects)
> Treatment <- factor(description$Treatment)
> design <- model.matrix(~0+Treatment+Subjects)

# Name comparison
> contrast.fit <- "placebo_20 – placebo_22"

design <- model.matrix(~0+Treatment+Subjects)

# design N =34 subjects but cutt it off for mailing it here.
> design

TreatmentVSL3_20 TreatmentVSL3_22 Treatmentplacebo_20 Treatmentplacebo_22
Subjects2
Subjects3
1 0 0 0 1 0
1 0 0 0 0 1
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 0 0
0 1 0 0 1 0
0 1 0 0 0 1
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0
0 0 0 1 0 0

If I do a fit on my data using the design matrix above I get
the following warning /error
> fit <- lmFit(data, design)
Coefficients not estimable: Subjects34
Warning message:
Partial NA coefficients for 47323 probe(s)

Can anyone tell me what's goes wrong my design?
Or suggest how to do the limma analyses with my
specific experiment.

> sessionInfo()
R version 2.14.2 (2012-02-29)
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)

locale:
[1] C/en_US.UTF-8/C/C/C/C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
 [1] qvalue_1.28.0             ggplot2_0.9.1             limma_3.10.3

 [4] lumiHumanAll.db_1.16.0    org.Hs.eg.db_2.6.4
 lumiHumanIDMapping_1.10.0
 [7] RSQLite_0.11.1            DBI_0.2-5
AnnotationDbi_1.16.19
[10] lumi_2.6.0                nleqslv_1.9.3             methylumi_2.0.13

[13] Biobase_2.14.0

loaded via a namespace (and not attached):
 [1] BiocInstaller_1.2.1   IRanges_1.12.6        KernSmooth_2.23-7
MASS_7.3-18
 [5] Matrix_1.0-5          RColorBrewer_1.0-5    affy_1.32.1
affyio_1.22.0
 [9] annotate_1.32.3       colorspace_1.1-1      dichromat_1.2-4
digest_0.5.2
[13] grid_2.14.2           hdrcde_2.15           labeling_0.1
 lattice_0.20-6
[17] memoise_0.1           mgcv_1.7-16           munsell_0.3
nlme_3.1-103
[21] plyr_1.7.1            preprocessCore_1.16.0 proto_0.3-9.2
reshape2_1.2.1
[25] scales_0.2.1          stringr_0.6           tcltk_2.14.2
 tools_2.14.2
[29] xtable_1.7-0          zlibbioc_1.0.1

Thanks a lot,
Varshna Goelela

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