Hi.

I am trying to reproduce this kind of figure (Chemokines differentially regulated in the five differents cells type on top) :

I would like to do the same, first with just M1 and M2 conditions, and eventually more. I would also like to be able to choose the genes in this heatmap (if you have advises on how to do this, I'll take it !)

So for now I have :

1. the AffyBatch form the CEL files
2. the annotated expression set normalized with rma().
3. The MarrayLM from lmfit(data.rma,design)
4. The MarrayLM from eBayes(data.fit) with pair-wise comparisons (contrasts)

From which one should I build the matrix for heatmap() ? How ?

I want to focus on gene coding for plasma membrane proteins, is there a way to do this ?

Thanks for your help !

So you want to work with the normalised expression matrix. I normally start with a data frame actually, they are easier to sort and subset etc. You can convert between them using as.data.frame() and as.matrix(), but it may be easier just to write the matrix then read it in again, that is what I do after neqc() normalisation.

I always have an annotation file (data frame) to use which will be in the same order as the columns in the matrix/ data frame. The annotation file can be used to add annotation in the heatmap such as the M1 and M2 conditions you describe. I prefer to use aheatmap function from NMF, it is easier to use than heatmap.2, and I think the output looks better. People normally select a subset of genes to use, such as the most variable or differentially expressed from limma.

You can easily subset the genes which should be the row names in your data frame using, I would do subsetteddf <- subset(newdf, row.names %in% listofgenes) to get a subsetted data frame. Then you can convert it back to a matrix for plotting.

The chemokines family of proteins has broad, diverse functional repertoires. However, their structural variation is narrow. Chemokines are small (8-10 kDa), secreted single polypeptide chains 70-100 residues long. Across the family, the proteins have 20-95% amino acid sequence identity (including conserved cysteine residues) and new members are continuing to be identified at a rapid pace;