[BioC] Cross-comparison of independent intensities from different experiments (genepix) (sorry I don\'t know how to describe the problem better)
Susanne Gerber [guest]
guest at bioconductor.org
Fri Feb 3 13:53:05 CET 2012
Dear all,
could please anyone help me with the following problem:
Experiments were performed using two color cDNA .gpr files (genepix).
We have an experimental setup with two independent time series (each of it with 4 time-points (in the following T1 - T4).
In the first time series Wildtype(WT) cells were stressed at time point zero with a certain drug and probes were taken at 4 time points afterwards.
These probes were compared with the unstressed WT.
In the second time series mutant-cells (MU) were treated identically and compared with the unstressed MU cell.
Here is the target file
> targets
FileName Cy3 Cy5
1 13754122.gpr WT WT_stress_T1
2 13754112.gpr WT_stress_T1 WT
3 14039687.gpr WT WT_stress_T2
4 13754123.gpr WT WT_stress_T2
5 13754109.gpr WT WT_stress_T3
6 14039055.gpr WT_stress_T3 WT
7 14004643.gpr WT WT_stress_T4
8 14039058.gpr WT_stress_T4 WT
9 14039688.gpr MU MU_stress_T1
10 13754114.gpr MU_stress_T1 MU
11 14039061.gpr MU MU_stress_T2
12 14039059.gpr MU_stress_T2 MU
13 13754124.gpr MU MU_stress_T3
14 13754115.gpr MU_stress_T3 MU
15 14039057.gpr MU MU_stress_T4
16 14039056.gpr MU_stress_T4 MU
I was working a lot with these data and we had some very interesting results, however, I am not able to solve the following problem:
How can a make a comparison between
a) MU and WT
b) MU_stressed and WT
A am not the experimenter and it is also not possible to repeat the experiment and produce a direct comparison.
However, I think - even if it is not the most elegant way - there should be a way to make this comparison with the existing data.
I was already thinking of simple "copy and past" the single channel intensities from the .gpr-files into a new matrix, but I guess this would cause a lot of problems concerning normalization steps.
Perhaps the answer is very easy, - then sorry for bothering you - but I swear I was reading a lot (tutorials) but actually I even don't know what keywords to search (google) for this problem.
What I do right now (after preprocessing) is:
#
#
Average <- avedups(genes, ndups=2, spacing=1)
Average$A[ is.na(Average$A) ] <- 0.0
Average$M[ is.na(Average$M) ] <- 0.0
#
designWT <- modelMatrix(targets,ref="WT")
designWT <- designWT[1:8,1:4]
designWT
designMU <- modelMatrix(targets,ref="MU")
designMU <- designMU[9:16,6:9]
designMU
AverageWT <- Average[,1:8]
AverageMU <- Average[,9:16]
#
fit_WT <- lmFit(AverageWT, designWT)
fit_WT <- eBayes(fit_WT)
topTable(fit_WT)
fit_MU <- lmFit(AverageMU, designMU)
fit_MU <- eBayes(fit_MU)
topTable(fit_MU)
#
.... and further analysis and evaluation procedures
#
Please, what would be the best way to make the comparison
a) MU_(T1-4) with WT as reference
and
b) MU_stressed (T1-4 )with WT as a reference ?
Thanks a lot in advance for the help !
I would be so grateful if someone could give me an answer.
Best regards,
Susanne
-- output of sessionInfo():
> sessionInfo()
R version 2.13.2 (2011-09-30)
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] splines tcltk stats graphics grDevices utils datasets methods base
other attached packages:
[1] MASS_7.3-14 calibrate_1.7 Heatplus_1.22.0 XML_3.4-3 annaffy_1.24.0 KEGG.db_2.5.0
[7] goProfiles_1.14.0 GO.db_2.5.0 annotate_1.30.1 yeast2.db_2.5.0 org.Sc.sgd.db_2.5.0 RSQLite_0.10.0
[13] DBI_0.2-5 AnnotationDbi_1.14.1 statmod_1.4.14 vsn_3.20.0 arrayQuality_1.30.0 convert_1.28.0
[19] affy_1.30.0 marray_1.30.0 limma_3.8.3 maSigPro_1.24.1 DynDoc_1.30.0 widgetTools_1.30.0
[25] Biobase_2.12.2
loaded via a namespace (and not attached):
[1] Mfuzz_2.10.0 RColorBrewer_1.0-5 affyio_1.20.0 grid_2.13.2 gridBase_0.4-4 hexbin_1.26.0
[7] lattice_0.19-33 preprocessCore_1.14.0 tkWidgets_1.30.0 tools_2.13.2 xtable_1.6-0
>
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