[BioC] design matrix paired data

[Ninni Nahm [guest]]

Dear all,

I made a design matrix according to the limma user guide. However, I wanted to check with you, whether it is correct or not. I feel like I made a mistake, but I do not know for sure. 


I have 3 patients and 2 conditions: 


df <- data.frame(patient = c("p1","p2","p3", "p1","p2","p3"),
		treatment = c("control", "control", "control", "treatment", "treatment", "treatment")
		) 

df
  patient treatment
1      p1   control
2      p2   control
3      p3   control
4      p1 treatment
5      p2 treatment
6      p3 treatment


design <-  model.matrix(~factor(patient) + factor(treatment, levels = c("control","treatment")), data= df)




design
  (Intercept) factor(patient)p2 factor(patient)p3
1           1                 0                 0
2           1                 1                 0
3           1                 0                 1
4           1                 0                 0
5           1                 1                 0
6           1                 0                 1
  factor(treatment, levels = c("control", "treatment"))treatment
1                                                              0
2                                                              0
3                                                              0
4                                                              1
5                                                              1
6                                                              1
attr(,"assign")
[1] 0 1 1 2
attr(,"contrasts")
attr(,"contrasts")$`factor(patient)`
[1] "contr.treatment"

attr(,"contrasts")$`factor(treatment, levels = c("control", "treatment"))`
[1] "contr.treatment"

colnames(design) <- c("intercept", "patient1", "patient2", "treatment")


fit <- lmFit(eset, design)
fit <- eBayes(fitNoAdj)
tt  <- topTable(fitNoAdj, coef= "treatment", adjust="fdr")


Is this correct? 

Thank you in advance!

Ninni



 -- output of sessionInfo(): 

> sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] annotate_1.42.0                       pd.hugene.2.0.st_3.8.1               
 [3] oligo_1.28.0                          Biostrings_2.32.0                    
 [5] XVector_0.4.0                         IRanges_1.22.6                       
 [7] oligoClasses_1.26.0                   RColorBrewer_1.0-5                   
 [9] xtable_1.7-3                          hugene20sttranscriptcluster.db_2.14.0
[11] org.Hs.eg.db_2.14.0                   RSQLite_0.11.4                       
[13] DBI_0.2-7                             AnnotationDbi_1.26.0                 
[15] GenomeInfoDb_1.0.2                    Biobase_2.24.0                       
[17] BiocGenerics_0.10.0                   limma_3.20.1                         

loaded via a namespace (and not attached):
 [1] affxparser_1.36.0     affyio_1.32.0         BiocInstaller_1.14.2 
 [4] bit_1.1-12            codetools_0.2-8       ff_2.2-13            
 [7] foreach_1.4.2         GenomicRanges_1.16.2  iterators_1.0.7      
[10] preprocessCore_1.26.0 splines_3.1.0         stats4_3.1.0         
[13] XML_3.98-1.1          zlibbioc_1.10.0  

--
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