[BioC] No replicates and differential analysis !!

[Naomi Altman]

Without replication, there is nothing staatistical that is really 
"robust" because you do not know how variable the data are.

In the old industrial design literature, in experiments without 
replication, a normal probability plot (qqnorm) or half-normal plot 
were used to identify effects which were too large compared to random 
normal (which presumably fit most of the effects).  You could do 
something similar (I would suggest using the quantiles of the t3 or 
t4 distribution rather than a normal) but the method requires 2 
assumptions that are very unlikely in the current situation:  the 
responses must be independent (but responses on the same array are 
dependent) and the responses must be identically distributed as a 
K*t4 distribution, where K is a constant related to the gene-wise 
standard deviation - i.e. the SD for all genes must be equal.

There is also the volcano plot, which I have never used, but is based 
on similar ideas.

A more robust idea is to use a binary search using PCR and the 
observed fold differences.  Although given the expense, it would be 
simpler to run a replicate for each condition.

   --Naomi

At 09:19 AM 1/25/2006, Nicolas Servant wrote:
>Thanks for your answer,
>But in this case, i have to choose a fold change threshold ! And it is
>supported that the FC tends to be greater at low expression levels.
>For instance a FC greater than 2 for expression values near 50 is
>readily seen, but it is low probability to observe FC greater than 2 for
>expression values near 1000
>So i would like to use a more robust approach.
>
>Regards,
>Nicolas S.
>
>Sean Davis wrote:
>
> >
> >On 1/25/06 8:34 AM, "Nicolas Servant" <Nicolas.Servant at curie.fr> wrote:
> >
> >
> >
> >>Hello,
> >>
> >>Does anybody know a R package or function to compare expression level
> >>(affy data) of two groups with no replicates in each group ? In fact,
> >>just compare one array to an other.
> >>The purpose is to find differentially expressed genes.
> >>We cannot used statistical test (not enougth replicates), but we can
> >>used graphical approach based on scatter plot, and outliers detection
> >>approach.
> >>
> >>
> >
> >Simply take array A and divide it by array B.  Then rank the genes by those
> >ratios.
> >
> >Sean
> >
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> >
> >
> >
> >
>
>
>--
>Nicolas Servant
>Equipe Bioinformatique
>Institut Curie
>26, rue d'Ulm - 75248 Paris Cedex 05 - FRANCE
>
>Email: Nicolas.Servant at curie.fr
>Tel: 01 44 32 42 75
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111