Dear Bioconductors,
I have some proteomics data for several tissues:

Heart x 3 replicates
Lung x 3 replicates

Each data set has a gene symbol and the number of peptides for that gene (a
rough measure of protein expression).

I want to make a data structure like:

            heart1   heart2  heart3  lung1   lung2    lung3
Gene1  2            4           3        7         9           20
Gene2    50        45          33      0         1            0
Gene3  ...... etc
Gene4

Each number in the data frame corresponds to number of peptides for that
gene.

My questions are:

Is a Principle Component Analysis useful for this data set?
What would a PCA  tell me?
What function would I use make a nice graphical representation of the data?

Or should I used a concordance function, something like?

con<-function(y1,y2){
  d<-(mean(y1) - mean(y2))
  v1<-var(y1)
  v2<-var(y2)
  cov<-cov(y1,y2)
  con<-(2*cov)/(v1+v2+d^2)
  return(con)};

This will tell me if two samples have concordance but I don't know how to
involve all samples. Basically, I want to summarise the data.

Any suggestions will be appreciated.

J.

	[[alternative HTML version deleted]]

