[BioC] Limma : Single Channel experiment design matrix

[Koran [guest]]

Dear All,

I have a question regarding the way to analyse single channel experiment (several groups).

In a first approach, I followed the limma user's guide for several groups (chapter 9.3), and used a contrast
matrix to make the comparison between two groups among all groups.

I also followed another approach : I take a sub expression set with only the two groups of samples I need to compare, and then follow the two groups approach (chapter 9.2)

If fold change remains the same, the p.value of moderated t-test is different :

for the "chapter 9.3" I get this (topTable):
              logFC   AveExpr         t      P.Value    adj.P.Val        B
NM_013409  4.804450  9.351186  63.46856 5.198462e-32 2.225306e-27 60.42083
NM_170685  3.327586  7.476924  43.29198 2.292074e-27 4.102931e-23 51.64301
NM_021995  3.598441  8.731876  42.94068 2.875416e-27 4.102931e-23 51.44328
NM_000014  2.686684 11.968353  38.61755 5.481149e-26 4.817512e-22 48.80565
NM_001747  2.727227  8.834094  38.33543 6.716748e-26 4.817512e-22 48.62109

for the "chapter 9.2", I get this topTable :
              logFC   AveExpr         t      P.Value    adj.P.Val        B
NM_013409  4.804450 10.238329  70.14768 7.077519e-15 2.709195e-10 23.07593
NM_015464  3.868533  9.850459  66.20398 1.265772e-14 2.709195e-10 22.72371
NM_000119 -3.322662 11.608264 -61.31983 2.733108e-14 3.899871e-10 22.22951
BC025320   2.908061  7.112412  56.61705 6.089619e-14 6.516958e-10 21.68233
NM_000014  2.686684 11.682645  53.85715 1.005598e-13 8.609327e-10 21.32326
NM_170685  3.327586  7.826983  51.22412 1.662803e-13 1.086579e-09 20.95091


Of course, logFC remains the same, Avg Expression are obviously differents, but the p.value are differents.
So I was wondering why ? and wich is the best approach to choose since one give results with more statistical power ?

Thank you for your kind answers.

Koran












 -- output of sessionInfo(): 

R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

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

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

other attached packages:
 [1] RColorBrewer_1.0-5         R.basic_0.53.0             R.utils_1.29.8             R.oo_1.18.0                R.methodsS3_1.6.1         
 [6] plotrix_3.5-3              multicore_0.1-7            pvclust_1.2-2              arrayQualityMetrics_3.18.0 impute_1.36.0             
[11] marray_1.40.0              limma_3.18.13              fortunes_1.5-2             snowfall_1.84-6            snow_0.3-13               

loaded via a namespace (and not attached):
 [1] affy_1.40.0           affyio_1.30.0         affyPLM_1.38.0        annotate_1.40.1       AnnotationDbi_1.24.0  beadarray_2.12.0     
 [7] BeadDataPackR_1.14.0  Biobase_2.22.0        BiocGenerics_0.8.0    BiocInstaller_1.12.0  Biostrings_2.30.1     Cairo_1.5-5          
[13] cluster_1.14.4        colorspace_1.2-4      DBI_0.2-7             Formula_1.1-1         gcrma_2.34.0          genefilter_1.44.0    
[19] grid_3.0.2            Hmisc_3.14-2          hwriter_1.3           IRanges_1.20.6        KernSmooth_2.23-10    lattice_0.20-27      
[25] latticeExtra_0.6-26   parallel_3.0.2        plyr_1.8.1            preprocessCore_1.24.0 Rcpp_0.11.0           reshape2_1.2.2       
[31] RSQLite_0.11.4        setRNG_2011.11-2      splines_3.0.2         stats4_3.0.2          stringr_0.6.2         survival_2.37-7      
[37] SVGAnnotation_0.93-1  tools_3.0.2           vsn_3.30.0            XML_3.95-0.2          xtable_1.7-1          XVector_0.2.0        
[43] zlibbioc_1.8.0       


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