[BioC] multiple comparisons followed by multiple tests

[Matthew Hannah]

Rich,

So you have
WT Mut - 1 chip
WT Control - 3 chips
KO Mut - 1 chip
KO Control - 2 chips

Odd design, I'd have used 1 more chip and done 2 biological reps of all 
treatments. But that's irrelevant as its not your experiment and its after 
the event anyway.

The only statistical comparison that could be made is WT - KO, but then
the treatment effects may get in the way and I guess the primary interest
are differences in response to the treatment. Also remember the comment
about if these are tech reps then stats is dodgy ground anyway.

I would find out whether the reps are really tech reps ie:same RNA hybridised
to 3 chips, biological reps within or between experiments. If its the first 
then he probably wasted $3000, the 2nd and 3rd may allow you to estimate the
variability between samples and what kind of fold changes you may get by chance.
You will also need to find out if there was any pooling done and consider this
(see Rafael's post on pooling paper above).

To cut to the point, the only option is to filter on fold change in some way.
The bigger the changes the better and confirm with RT-PCR, I guess. Oh, and 
tell them to ask *before* next time ;-)

HTH,
Matt


-----Original Message-----
From: Richard Friedman [mailto:friedman at cancercenter.columbia.edu]
Sent: Mittwoch, 21. Juli 2004 16:39
To: Matthew Hannah
Cc: 'Bioconductor Mail List'
Subject: Re: [BioC] multiple comparisons followed by multiple tests 


Dear Matt and other Bioconductor Users:
On Jul 21, 2004, at 9:56 AM, Matthew Hannah wrote:

> I would look at the Limma help pages as this allows Lm fitting and the
> specification of multiple comparisons and also P value correction by 
> fdr
> (although I think this is only after ebayes mod of t-stats?).
>
> As for the replication if you have less than 3 reps per treatment then
> you are obviously wasting your time. Also if they are just technical 
> reps
> rather than true biological reps then any statistical analysis will be
> misleading due to the underestimate of biological variability. You also
> don't mention what type of data (affy or cDNA) or the general design
> which may allow people to offer more detailed advice.
> You also don't mention the starting point data - for example if it's 
> affy
> data are the signal values from MAS5 or have you looked into using RMA 
> or
> GCRMA as a low-level normalisation?

Thank you for your help. I phrased my questions generally, but will now
be more specific, if that affects the answers.
The data is Affy data. I normalized it with RMA.
I agree with you that between 1-3 technical replicates is not optimal.
I didn't design the experiments. I was just given them to analyze
  after they were performed. The experimentalist with whom I am working 
is
prepared to perform a limited number of PCR confirmatory experiments.
I will encourage him to do more experiments, but he wants to see what 
can be
learned from the present dataset first.
The experiment is to detect the effect of a knockout on the ability of
cells to respond to different mutagens. So I am planning on comparing:


1. wild-type exposed to mutagen (1 technical replicate)  vs. wildtype 
no treatment (3 technical replicates).
2. knockout no treatment (2 technical replicates) vs. wildtype 
no-treatmen (3 technical replicates).
3. knockout exposed to mutagen (1 technical replicate) - wildtype 
exposed to mutagen (1 technical replicate).
4. (knockout exposed to mutagen  (1 technical replicate) - wildtype 
exposed to mutagen)(1 technical replicate). -
      (knockout no treatment  (2 technical replicates) - wildtype no 
treatment (3 technical replicates)).

  My question is; Given the small number of replicates, should I ignore 
statistical analysis altogether and merely
proceed with fold changes.

Thanks and best wishes,
Rich

>
> HTH,
> Matt
>
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------------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist
Herbert Irving Comprehensive Cancer Center
Oncoinformatics Core
Lecturer
Department of Biomedical Informatics
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http://cancercenter.columbia.edu/~friedman/


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In Memoriam, Tim O'Connor