**660**wrote:

Dear Bioconductor Community,

in a previous thread( https://support.bioconductor.org/p/64372/#64413) i have asked about the implementation of a paired analysis, and Mr. James W. MacDonald kindly provided me an suggestion of a paired limma analysis, which i used on the code below :

library(limma) conditions <- data.trusted.eset$condition condition <- factor(conditions, levels(condition)[c(2,1)]) pairs <- factor(rep(1:13, each = 2)) design <- model.matrix(~condition+pairs) fit <- lmFit(data.trusted.eset, design) fit2 <- eBayes(fit)

In the next step i wanted to implement a multifactorial analysis in limma to evaluate the interaction between two factors, condition(control & cancer tissue) & meta_factor(metastatic & non-metastatic cancer), in order to evaluate possible DE genes between metastatic cancer and non-metastatic cancer samples:

f <- paste(data.trusted.eset$condition, data.trusted.eset$Meta_factor, sep=".") f <- factor(f) pairs <- factor(rep(1:13, each = 2)) design <- model.matrix(~0 +f +pairs) fit <- lmFit(data.trusted.eset, design) head(names$coefficients) contrast.matrix <- makeContrasts(M.CvsC="fCancer.1-fCancer.0", M.CvsControl="fCancer.1-fNormal.0", levels=design) fitb <- contrasts.fit(fit, contrast.matrix) fit2 <- eBayes(fitb)

My first question is if it is wrong for the statistical model that i didnt use an intercept term(because with the intercept term i couldnt use the coefficient "fCancer.0" for the contrast.matrix) ? And secondly, is it also important as with the first analysis with just one factor, to include the term pairs to get meaningful results ? My last question is that with topTable, i get only 4 genes with adj.p,Val < 0.05. Is that necessarily wrong or it might illustrate possible small differences between metastatic and non-metastatic tumors ?

Thank you for your patience(& please excuse me for any "begginer" questions, as i have only using R & Bioconductor for a few months).

**38k**• written 4.4 years ago by svlachavas •

**660**

any help or suggestions ?

660What is

`data.trusted.eset$condition`

? What is`data.trusted.eset$Meta_factor`

? We'd be able to give more help if you define it here, rather than us having to dig through an old thread to figure out what you're talking about.24kPlease excuse me, because i forgot to mention it in the start when i was refering to the variables. Data.trusted.eset is my ExpressionSet, and the two other variables are in the phenoData object as above

pData(data.trusted.eset) :

condition Meta_factor

7_Tesch.CEL Normal 0

8_Tesch.CEL Cancer 0

0687_04_Blauth.CEL Normal 0

0687_04_Blauth_1.CEL Cancer 0

0863_03_Schmidt.CEL Normal 0

0863_03_Schmidt_1.CEL Cancer 0

0948_04_Leiber.CEL Normal 0

0948_04_Leiber_1.CEL Cancer 0

1043_04_Nagel.CEL Normal 0

1043_04_Nagel_1.CEL Cancer 0

1103_03_Braun.CEL Normal 1

1103_03_Braun_1.CEL Cancer 1

1234_06_Floersch.CEL Normal 0

1234_06_Floersch_1.CEL Cancer 0

1235_06_Hey.CEL Normal 0

1235_06_Hey_1.CEL Cancer 0

1236_06_Liebich.CEL Normal 1

1236_06_Liebich_1.CEL Cancer 1

1410_03_Urbaniak.CEL Normal 1

1410_03_Urbaniak_1.CEL Cancer 1

1430_04_Patschke.CEL Normal 0

1430_04_Patschke_1.CEL Cancer 0

1518_03_Dege.CEL Normal 0

1518_03_Dege_1.CEL Cancer 0

1554_03_Gemmer.CEL Normal 1

1554_03_Gemmer_1.CEL Cancer 1

660