'[BioC] design matrix in limma'

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List:       bioconductor
Subject:    [BioC] design matrix in limma
From:       "KC [guest]" <guest () bioconductor ! org>
Date:       2014-09-15 21:24:01
Message-ID: 20140915212401.484F860F12 () nutria ! fhcrc ! org
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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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