[BioC] RNA degradation problem

[Matthew Hannah]

________________________________

From: fhong at salk.edu [mailto:fhong at salk.edu]
Sent: Thu 19/01/2006 21:27
To: Matthew Hannah
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] RNA degradation problem



Hi Matthew,

Thank you very much for your help.

> >It's amazing how many
>> lab plant biologists see pooled samples from a bulk of plants grown at
>> the same time as biological replicates when they are clearly not.
>I would think that all plants under experiment shoudl be grown at the same
>time without different conditions/treatments. Biological replicates should
>be tissue samples from differnt groupd of plants, say sample from 50
>plants as replicate1 and sample from another 50 as replicate 2.
>Do you think that biological replicates should be grown at different time?


Absolutely! Biological replication must be either single plants grown in the same experiment (but noone wants to risk single plants for arrays) or large pools of plants from INDEPENDENT experiments (or the pools must be smaller than sample size - doesn't really happen for arrays) otherwise what biological variability are you sampling? Say you have 150 plants growing in the greenhouse and you harvest 3 random pools of 50 as your 3 'biological replicates' then you will have eliminated all variability from them and the arrays will be as good as technical replicates and any statistical testing is invalid.

>> I find hist, RNA deg, AffyPLM and a simple RMA norm followed by
>> plot(as.data.frame(exprs(eset.rma))) can answer in most cases for why it
>> didn't work, or won't work - in the rare case when someone asks for QC
> >before rather than after they realise the data is strange ;-)
>This actually pull out another question: when % of differential genes is
>large, which normalization better works better?
I've posted on this alot about 1.5 years ago, you should find it in the archives - but simply noone knows or has tested it


>http://cactus.salk.edu/temp/QC_t.doc
>Take a look at the last plot, which clearly indicate homogeneous within
>replicates and heterogeneous among samples.
>(1) Will stem top and stem base differ so much? Or it is the preparation
>process bring in extra correlaton within replicates.
>(2) when % of differential genes is large, which normalization better
>works better?
Looking at these scatterplots, I can honestly say I've never seen so much DE. I would be suprised if samples such as different stem positions were so different. Something must be wrong with the samples or sampling in my opinion. The scatterplots are slightly more user friendly if you use pch="."

HTH,

Matt






--------------------
Fangxin Hong  Ph.D.
Plant Biology Laboratory
The Salk Institute
10010 N. Torrey Pines Rd.
La Jolla, CA 92037
E-mail: fhong at salk.edu
(Phone): 858-453-4100 ext 1105