[BioC] No replicates and differential analysis !!
naomi at stat.psu.edu
Wed Jan 25 16:00:06 CET 2006
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.
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.
>Sean Davis wrote:
> >On 1/25/06 8:34 AM, "Nicolas Servant" <Nicolas.Servant at curie.fr> wrote:
> >>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
> >Simply take array A and divide it by array B. Then rank the genes by those
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Naomi S. Altman 814-865-3791 (voice)
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