how to analyse a matrix of genes vs samples with limma
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@federico-abascal-2006
Last seen 10.2 years ago
Estimated colleagues, It is probably a simple question, but I am new to limma and I don't find the answer. I have a matrix with columns corresponding to different experiments or samples, and rows corresponding to genes. In a separate vector of factors I have a label indicating to which class each sample belongs to (it could be mutant or WT, for instance). I read that, in order to load data with limma, you have to create a design matrix in which a filename for each experiment (or sample) is given... but I have all the experiments in the same matrix, not in separate files. So, my (simple) question is: is there a way to go from my matrix to limma? Thank you very much, Federico
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Ido M. Tamir ▴ 320
@ido-m-tamir-1268
Last seen 10.2 years ago
On Thursday 13 December 2007 09:38:02 Federico Abascal wrote: > Estimated colleagues, > I have a matrix with columns corresponding to different experiments or > samples, and rows corresponding to genes. In a separate vector of > factors I have a label indicating to which class each sample belongs to > (it could be mutant or WT, for instance). I read that, in order to load > data with limma, you have to create a design matrix in which a filename > for each experiment (or sample) is given... but I have all the > experiments in the same matrix, not in separate files. So, my (simple) > question is: is there a way to go from my matrix to limma? lmFit can deal with a matrix of background corrected normalized M values. An example is given in the examples. The difficult part is constructing the design matrix correctly, and this only needs the column names. # Simulate gene expression data for 100 probes and 6 microarrays # Microarray are in two groups # First two probes are differentially expressed in second group # Std deviations vary between genes with prior df=4 sd <- 0.3*sqrt(4/rchisq(100,df=4)) y <- matrix(rnorm(100*6,sd=sd),100,6) rownames(y) <- paste("Gene",1:100) y[1:2,4:6] <- y[1:2,4:6] + 2 design <- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1)) options(digit=3) fit <- lmFit(y,design) fit <- eBayes(fit) fit as.data.frame(fit[1:10,2]) etc... best wishes, ido
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