[BioC] Biological replicates

[Naomi Altman]

Technical replication is usually not effective in 
determining biologically meaningful effects, but 
is certainly useful for determining whether an 
outlying sample is actually biologically 
different, or just part of the usual variability 
in the system (which is a mix of biological 
variation and technical variation).  However, it 
is also useful to remember that the technical 
variation in the system can be due to the sample 
preparation as well as the hybridization.  So a 
"bad" array might produce an almost identical 
technical replicate.  All in all, if possible it 
is best to take another biological sample.

With small sample sizes, you cannot help seeing 
what appear to be unusual effects.  To give you 
an idea, suppose that you have 4 biological 
replicates from the same treatment and you divide 
them arbitrarily into 2 groups of 2.  There is a 
1/3 probability that the 2 largest end up in one 
group and the 2 smallest in the other.  On the 
other hand, there is also 1/3 probability that 
the largest and smallest are in one group and the 
2 middle ones in the other, which gives the false 
impression that the variability is higher in one group than the other.

--Naomi




At 06:51 PM 9/27/2007, Ana Conesa wrote:
>There will be always a difference in expression between biological
>replicates. If this is big then you need bigger differences between
>conditions to find a signigicant differential expressed gene. It´s
>not that this will skew the data a bit, it´s that it will be harder
>to find significant changes. Big differences between replicates could
>have a technical origin or simply reflect biological variation. If
>you do not have technical replicates aswell you cannot tell the
>difference.
>A
> >
> >
> >---- Mensaje Original ----
> >De: yogi.sundaravadanam at agrf.org.au
> >Para: bioconductor at stat.math.ethz.ch, naomi at stat.psu.edu
> >Asunto: Re: [BioC] Biological replicates
> >Fecha: Fri, 28 Sep 2007 08:16:09 +1000
> >
> >>>This is exactly what the t-test is all about.  If you want to state
> >
> >>that a gene differentially expresses between 2 conditions, don't you
> >
> >>mean that the difference in expression is higher than the difference
> >
> >>between biological replicates of the same condition?
> >>
> >>I was just wondering what I should do if the difference of
> >expression exists between the replicates itself... won't that skew
> >the data a bit?
> >>
> >>
> >> -----Original Message-----
> >>From: Naomi Altman [mailto:naomi at stat.psu.edu]
> >>Sent: Friday, 28 September 2007 1:01 AM
> >>To: Yogi Sundaravadanam
> >>Subject: Re: [BioC] Biological replicates
> >>
> >>This is exactly what the t-test is all about.  If you want to state
> >>that a gene differentially expresses between 2 conditions, don't you
> >
> >>mean that the difference in expression is higher than the difference
> >
> >>between biological replicates of the same condition?
> >>
> >>--Naomi
> >>
> >>At 01:13 AM 9/27/2007, you wrote:
> >>>Hi all
> >>>
> >>>
> >>>
> >>>I am working with biological replicates and I am a bit worried
> >about the
> >>>biological variation between samples.
> >>>
> >>>For example, the abundance of a certain gene in sample 1 could be
> >>>hundreds of time higher or lower than in sample B. If this is the
> >case,
> >>>
> >>>this will significantly affect the P-value in the t-test.
> >>>
> >>>
> >>>
> >>>As such, my question is whether there is a way we can account for
> >this
> >>>fact in the statistical analysis?
> >>>
> >>>
> >>>
> >>>I will be much grateful if you guys could shed some light on this
> >topic?
> >>>
> >>>
> >>>
> >>>
> >>>Thank you
> >>>
> >>>Yogi
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>         [[alternative HTML version deleted]]
> >>>
> >>>_______________________________________________
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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
> >>
> >>_______________________________________________
> >>Bioconductor mailing list
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> >>Search the archives: http://news.gmane.org/gmane.science.biology.inf
> >ormatics.conductor
> >>
>
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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