Dear Steve and Sean.

First, thanks for replying to my query.

Normalisation:

Yes, I will normalise to number of peptides per gene model (due to
isoforms). If I wanted to be ultra careful, I would see if any peptides were
specific to any one isoform but that is complicated and probably inaccurate.
I also will normalise on amount of protein loaded onto the mass/spec as
there are slight differences.

Thanks for the PCA information.

“What am I trying to show?”

Well, I would like to graphically show:

1)      How well the replicates of each tissue concord/agree/correlate. So
how good were the replicates, how close did they agree? Are the majority of
the same genes being expressed at similar levels between the replicates

2)      Overall, globally/graphically, how the heart data differed from the
lung data. What genes are not being expressed between the tissues, what are
and at what differences in the level of expression.

Summing the number of peptides sounds good (easy :-)).

“There are likely biases (that depend on the experimental design and assays
used) in the data.  Try to determine what those are using simple plots of
the data.“

Once I have normalised the peptide counts, the major difference is the
tissues. Please can you give me an example of a plot function to use? Mmmm,
maybe you elude to that below.

“What are the distributions of the data when cut various ways (per gene, per
sample)?”

Thanks, I will try that.

“Do the data need transformation (log, for example)?”

You mean like microarray data, for a plot to have a normal distribution
curve around zero? How will I know that?

Thanks again, it is very helpful.
John.

On Wed, Mar 3, 2010 at 5:17 PM, Sean Davis <seandavi@gmail.com> wrote:

> On Wed, Mar 3, 2010 at 11:41 AM, Johnny H <ukfriend22@googlemail.com>
> wrote:
> > 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.
>
> Start simple.  There are likely biases (that depend on the
> experimental design and assays used) in the data.  Try to determine
> what those are using simple plots of the data.  What are the
> distributions of the data when cut various ways (per gene, per
> sample)?  What do scatter plots of one sample versus another look
> like?  Do the data need transformation (log, for example)?  Do the
> data need normalization (likely)?
>
> In short, some data exploration might be necessary before you can move
> on to ask more biologically relevant questions.  You may already have
> the information that you need to determine the best way forward, but
> that isn't clear from your post.
>
> Hope that helps,
> Sean
>

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