[BioC] technical replicates

[Naomi Altman]

Dear Bill,
Perhaps I have not understood your problem.

You could normalize your data as suggested.  After that, if you want to get 
some type of significance value, you do a two-sample test such as
a t-test or Wilcoxon test.  To date I am using a gene by gene analysis, by 
applying the function to each row of the expression matrix.

(see affy command "expr  and R commands "apply", t.test and Wilcox.test)

There are probably more efficient ways to do this.  This can also be done 
in limma, using the design (1 1 1 -1 -1 -1) assuming the first 3 arrays 
come from one condition and the other 3 from the other.

To detect outliers, I usually do MvA plots or array x array expression 
plots (on the log scale)

i.e. array x array plots would be the 3 plots arising from all pairs of 
arrays from the same treatment, with expression from array i on the x-axis 
and expression from array j on the y-axis.  Outliers are points far from 
the diagonal.

MvA plots are the 3 plots arising from all pairs of arrays from the same 
treatment.  The x-axis is the expression value averaged over the arrays, 
and the y-axis is the difference in expression values between the two 
arrays.  This generally looks like a sideways raindrop, with the wide end 
near the origin.  Again, points away from the point cloud are outliers.

--Naomi


At 09:15 PM 1/12/2004, William Kenworthy wrote:
>Unfortunately, they came to us too late and were forced by circumstances
>to complete the experiment as designed.  Next time ...
>
>I am thinking a straight average of the replicate values for each gene
>may the best solution (not per probe).  However, a significance value
>may show up a suspect gene (if a difference exists across the
>replicates, something is wrong in a yes/no fashion, rather than looking
>at degrees of significance)
>
>My preference is to use RMA from the affy package as a first pass, then
>expand using some of the other algorithms but concrete examples (and
>documentation) on how to specify replicates is lacking.  (i.e., the data
>examples say there are replicates, but how are they specified/handled -
>or not?)
>
>Billk
>
>
>On Sun, 2004-01-11 at 23:03, Naomi Altman wrote:
> > I usually use mixed model ANOVA to determine differential expression.  By
> > putting in a random effects term for sample (you have 2 technical reps per
> > sample) you end up with the correct analysis.
> >
> > However, in your case you have no sample replication.  As a result, you
> > cannot do a statistically valid ANOVA.   People do use various
> > approximations - the Affy-type Wilcoxon tests which treat the probes as
> > replicates (makes me very very nervous); using the technical replicates as
> > if they were biological replicates (makes me very nervous - there is no
> > guarantee that there is much relationship between the technical and
> > biological variation).  Of course, the best thing to do is to convince the
> > investigators that biological replication is far more important than
> > technical replication.  In most Affy studies, technical replication will
> > not be cost-effective due to the relatively small technical variance and
> > the large cost of individual arrays.
> >
> > At 01:18 AM 1/9/2004, William Kenworthy wrote:
> > >Hi, I have just been passed a set of affy data that consists of 3
> > >states, two technical replicates of each state (6 chips overall)
> > >
> > >1. whats the best way (normalisations, algorithms) to leverage technical
> > >replicates?
> > >
> > >2. how do you tell algorithms such as rma which are the replicates?  (I
> > >presume the phenoData in the AffyBatch specifies this, but the examples
> > >are in a binary format so you cant open the raw data file and see how
> > >they are put together!)
> > >
> > >BillK
> > >
> > >_______________________________________________
> > >Bioconductor mailing list
> > >Bioconductor at stat.math.ethz.ch
> > >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
> >
> > Naomi S. Altman                                814-865-3791 (voice)
> > Associate Professor
> > Bioinformatics Consulting Center
> > Dept. of Statistics                              814-863-7114 (fax)
> > Penn State University                         814-865-1348 (Statistics)
> > University Park, PA 16802-2111

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111