[BioC] design matrix in limma

[KC [guest]]

Hello BioC forum,

I am making a design matrix for gene expression data analysis with  the adjustment of Sex variable.  The R code I am using is below. I want to make sure if I am doing correctly.  Thanks for your review and feedback.

> library(limma)
> Sample<-factor(pd$Sample_Group, levels=c("Control","Case"))   ### treatment 
> Sex<-factor(pd$Sex, levels=c("M","F"))                        ### Sex
> design<-model.matrix(~Sample+Sex)
> design
   (Intercept) SampleCase SexF
1            1          0    0
2            1          0    0
3            1          0    0
4            1          0    1
5            1          0    1
6            1          0    1
7            1          1    1
8            1          0    0
9            1          0    1
10           1          1    0
11           1          1    1
12           1          0    1
13           1          0    1
14           1          1    0
15           1          0    0
16           1          1    1
17           1          0    0
18           1          0    0
19           1          1    0
20           1          0    0
21           1          0    1
22           1          1    1
23           1          0    1
24           1          0    0
25           1          1    0
26           1          0    1
27           1          0    1
28           1          0    1
29           1          0    0
30           1          0    0
31           1          1    1
32           1          0    1
33           1          0    1
34           1          1    0
35           1          0    0
attr(,"assign")
[1] 0 1 2
attr(,"contrasts")
attr(,"contrasts")$Sample
[1] "contr.treatment"

attr(,"contrasts")$Sex
[1] "contr.treatment"

> colnames(design)
[1] "(Intercept)" "SampleCase"  "SexF"       
> Lmfit<-lmFit(autosome.Mvalues.noSNPs, design)  ## using M values
> fit<-eBayes(Lmfit)
> top.50<-topTable(fit,coef=2,adjust.method="fdr",number=50,sort.by="B")
> top.50

 -- output of sessionInfo(): 

> sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

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

other attached packages:
 [1] FlowSorted.Blood.450k_1.2.0                        quadprog_1.5-5                                    
 [3] DMRcate_1.0.1                                      DMRcatedata_1.0.0                                 
 [5] limma_3.20.8                                       ks_1.9.2                                          
 [7] rgl_0.93.1098                                      mvtnorm_1.0-0                                     
 [9] misc3d_0.8-4                                       KernSmooth_2.23-12                                
[11] minfiData_0.6.0                                    IlluminaHumanMethylation450kanno.ilmn12.hg19_0.2.1
[13] IlluminaHumanMethylation450kmanifest_0.4.0         minfi_1.10.2                                      
[15] bumphunter_1.4.2                                   locfit_1.5-9.1                                    
[17] iterators_1.0.7                                    foreach_1.4.2                                     
[19] Biostrings_2.32.1                                  XVector_0.4.0                                     
[21] GenomicRanges_1.16.4                               GenomeInfoDb_1.0.2                                
[23] IRanges_1.22.10                                    lattice_0.20-29                                   
[25] Biobase_2.24.0                                     BiocGenerics_0.10.0                               

loaded via a namespace (and not attached):
 [1] annotate_1.42.1       AnnotationDbi_1.26.0  base64_1.1            beanplot_1.1          codetools_0.2-8      
 [6] DBI_0.2-7             digest_0.6.4          doRNG_1.6             genefilter_1.46.1     grid_3.1.0           
[11] illuminaio_0.6.0      MASS_7.3-33           matrixStats_0.10.0    mclust_4.3            multtest_2.20.0      
[16] nlme_3.1-117          nor1mix_1.1-4         pkgmaker_0.22         plyr_1.8.1            preprocessCore_1.26.1
[21] R.methodsS3_1.6.1     RColorBrewer_1.0-5    Rcpp_0.11.2           registry_0.2          reshape_0.8.5        
[26] rngtools_1.2.4        RSQLite_0.11.4        siggenes_1.38.0       splines_3.1.0         stats4_3.1.0         
[31] stringr_0.6.2         survival_2.37-7       tools_3.1.0           XML_3.98-1.1          xtable_1.7-3         
[36] zlibbioc_1.10.0  

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