Hi everyone,

I am analysing gene expression in animals fed 5 different diets. This diets are made of the same components with the exception of one of them that is included with the following concentration 0, 12.66g, 25.32g, 37.98g, 50.64g (from Diet 1 to Diet 5, respectively).

Samples have been processed using a 44K Agilent two colour microarray. I have run a first analysis using Limma and made pairwise contrasts. I have found that the number of differentially expressed genes increases as I compare diets with larger differences in concentration of the additive (i.e Diet1 vs Diet2 = 256 DE genes, Diet1 vs Diet3 = 1231 DE genes, Diet1 vs Diet3 = 1803 DE genes, etc.). Now, I would like to isolate genes, if there are any, whose expression values are correlated with the amount of additive in the diet. Can anyone suggest an approach for this? Can I do it with Limma?

Cruising through past posts I have come across the following:

genes <- matrix(rnorm(100),nc=10)
var1 <- rnorm(10)
df <- data.frame(var1=rnorm(10))
dm <- model.matrix(~ var1, data=df)
fit1 <- lmFit(genes,dm)
fit2 <- eBayes(fit1)
topTable(fit2,coef=2)

that suggests to simply change the value of the factors to a number.

However, if I try to adapt it to my case:
var1 <- c(0, 12.66, 25.32, 37.98, 50.64)
df <- data.frame(var1)
dm <- model.matrix(~ var1, data=df)
fit1 <- lmFit(fx,dm)
fit2 <- eBayes(fit1)
topTable(fit2,coef=2)

I get the following error:
Error in lm.fit(design, t(M)) : incompatible dimensions

Is the above approach correct? Can I use Limma? If not, is there a particular package that could do the job?  I hope I can get some feedback. Thanks in advance!

Best regards,
Christian De Santis


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