[BioC] time course differential analysis - design matrix

Agata [guest] guest at bioconductor.org
Fri Mar 28 10:18:18 CET 2014

Dear all,

I am doing differential expression analysis and I have a question concerning time course experiments (Single-Channel Experimental Designs). 

I have one cell line that was treated in 4 different ways. I want to check which genes respond dierently over time for different treatments. I did 4 different comparisons. 

I have treatment A, B, C and D, and I compared groups: A-B, A-C, C-D and B-D. For all my data I created ONE design matrix, and FOUR contrast.diff.matrices. For the fit() function I have used the esetPROC with all my data. This was followed by contrast.fit() and eBayes() functions. At the end I got top differentially expressed genes (from topTableF() function). 

Additionally, I did almost the same thing, but I created FOUR different design matrices and FOUR contrast.diff.matrices for all my comparisons. I extracted the subset of esetPROC only with the data I needed for the comparison, and continued as described above. 

I got different results for those two approaches. The adj.p.values were much smaller for the first approach than for the second one. I assume it is because of the eBayes function. Could you please explain me which approach is the correct/better one and why?

Best wishes,

 -- output of sessionInfo(): 

R version 3.0.2 (2013-09-25)
Platform: x86_64-unknown-linux-gnu (64-bit)

[1] C

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

other attached packages:
 [1] gplots_2.12.1            lattice_0.20-24          sva_3.8.0               
 [4] mgcv_1.7-26              nlme_3.1-111             corpcor_1.6.6           
 [7] vsn_3.30.0               marray_1.40.0            hgug4112a.db_2.10.1     
[10] org.Hs.eg.db_2.10.1      Agi4x44PreProcess_1.22.0 genefilter_1.44.0       
[13] annotate_1.40.0          AgiMicroRna_2.12.0       affycoretools_1.34.0    
[16] KEGG.db_2.10.1           GO.db_2.10.1             RSQLite_0.11.4          
[19] DBI_0.2-7                AnnotationDbi_1.24.0     preprocessCore_1.24.0   
[22] affy_1.40.0              Biobase_2.22.0           BiocGenerics_0.8.0      
[25] biomaRt_2.18.0           limma_3.18.12            WriteXLS_3.4.0          

loaded via a namespace (and not attached):
 [1] AnnotationForge_1.4.4    BSgenome_1.30.0          BiocInstaller_1.12.0    
 [4] Biostrings_2.30.1        Category_2.28.0          DESeq2_1.2.10           
 [7] Formula_1.1-1            GOstats_2.28.0           GSEABase_1.24.0         
[10] GenomicFeatures_1.14.2   GenomicRanges_1.14.4     Hmisc_3.14-0            
[13] IRanges_1.20.6           KernSmooth_2.23-10       MASS_7.3-29             
[16] Matrix_1.1-2             PFAM.db_2.10.1           R.methodsS3_1.6.1       
[19] R.oo_1.17.0              R.utils_1.29.8           R2HTML_2.2.1            
[22] RBGL_1.38.0              RColorBrewer_1.0-5       RCurl_1.95-4.1          
[25] Rcpp_0.11.0              RcppArmadillo_0.4.000.2  ReportingTools_2.2.0    
[28] Rsamtools_1.14.3         VariantAnnotation_1.8.12 XML_3.98-1.1            
[31] XVector_0.2.0            affyio_1.30.0            annaffy_1.34.0          
[34] biovizBase_1.10.7        bit_1.1-11               bitops_1.0-6            
[37] caTools_1.16             cluster_1.14.4           codetools_0.2-8         
[40] colorspace_1.2-4         dichromat_2.0-0          digest_0.6.4            
[43] edgeR_3.4.2              ff_2.2-12                foreach_1.4.1           
[46] gcrma_2.34.0             gdata_2.13.2             ggbio_1.10.11           
[49] ggplot2_0.9.3.1          graph_1.40.1             grid_3.0.2              
[52] gridExtra_0.9.1          gtable_0.1.2             gtools_3.3.0            
[55] hwriter_1.3              iterators_1.0.6          labeling_0.2            
[58] latticeExtra_0.6-26      locfit_1.5-9.1           munsell_0.4.2           
[61] oligoClasses_1.24.0      plyr_1.8                 proto_0.3-10            
[64] reshape2_1.2.2           rtracklayer_1.22.3       scales_0.2.3            
[67] splines_3.0.2            stats4_3.0.2             stringr_0.6.2           
[70] survival_2.37-7          tools_3.0.2              xtable_1.7-1            
[73] zlibbioc_1.8.0          

[1] "Gordon Smyth <smyth at wehi.edu.au>"

Sent via the guest posting facility at bioconductor.org.

More information about the Bioconductor mailing list