[BioC] Biological replication (was RNA degradation problem)

[Henk van den Toorn]

Maybe this is a problem that is not especially suited for the bioconductor
mailing list, but it is an interesting problem that is probably useful for
many of the participants. So, here's my 2 cents.

>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?

I think the last question is very important. I guess you don't need to try
to INCREASE the biological variability at all cost for a single experiment.
If you would be interested in combining experiments of the same lab in a
single analysis, it's probably wise to follow Naomi's advice to take
different replicates. A problem that might arise, is that the "biological"
variation is influenced by many circumstances inside a greenhouse or growth
chamber. In our lab practice, it's clear that influences like the weather
and the season have a profound influence on the biology of the plant, even
though our plants are kept in climate controlled growth chambers. If you
would use different batches of plants, you are actually confounding these
factors to the batches of plants. By using different samples of plants,
although unfortunately pooled in the same circumstances, you might actually
block the circumstances for later analysis, if you're willing to go that
far.

I'm very interested to see what other people have to say about this!

Henk van den Toorn, MSc
bioinformatician, Molecular Genetics group, Utrecht University
 

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Naomi Altman
Sent: 20 January 2006 01:42
To: Matthew Hannah; fhong at salk.edu
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] Biological replication (was RNA degradation problem)

The question of what is appropriate biological replication is a tough one.
The objective is to obtain results that are valid in the population of
interest, which usually is not plants grown in a single batch in the green
house.  But how much variability should we induce?  Each batch of plants
grown separately but in the same building (different growth chambers), grown
in different labs?  different universities?

In my very first Affy experiment, the investigator did the
following:  2 batches of plants grown separately, 2 samples of plants from 1
of the batches, 2 microarrays from one of the samples, for 4 arrays in all.
The correlation among the results was 2 arrays from same sample > 2 samples
from same batch > 2 batches.  This should be no surprise, even though we did
not have enough replication to do any formal testing.

I think at minimum that you want to achieve results that would be replicable
within your own lab.  That would suggest batches of plants grown separately
from separate batches of seed.

The best plan is a randomized complete block design, with every condition
sampled in every block.  If the conditions are tissues, this is readily
achieved.

Personally, I look at the density plots of the probes on the arrays.  If
they have the same "shape" (which is usually a unimodal distribution with
long tail to the right on the log2 scale) then I cross my fingers (that is
supposed to bring good luck) and use RMA.  Most of the experiments I have
been involved with using arabidopsis arrays have involved tissue
differences, and the amount of differential expression has been huge on the
probeset scale (over 60% of genes), but these probe densities have been
pretty similar.

--Naomi

At 05:02 PM 1/19/2006, Matthew  Hannah wrote:
>
>
>________________________________
>
>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
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor

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

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