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?

Hi Yogi,
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'm not sure what your question is... the fact that a large amount of
biological variation among samples in one treatment group will affect
the P-value in a t-test is EXACTLY how the statistical analysis
accounts for a large amount of biological variation. In simplified
terms, a t-test calculates the differences in the means between two
groups, then adjusts for the amount of biological variation within
each group. The p-value is the probability of getting the calculated
t-value if the two groups had been randomly sampled from the same
distribution. A low probability leads to the conclusion that the two
groups were likely sampled from distributions with different means.
If this doesn't answer your question, perhaps you could elaborate on
exactly how you want to account for biological variation in the
statistical analysis?
Cheers,
Jenny
>
>
>I will be much grateful if you guys could shed some light on this
topic?
>
>
>
>
>Thank you
>
>Yogi
>
>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives:
>http://news.gmane.org/gmane.science.biology.informatics.conductor
Jenny Drnevich, Ph.D.
Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign
330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA
ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at uiuc.edu

Hello
Say for example, I have 3 biological replicates of diseased cells and
for a certain gene, the expression of 1 replicate is too high than the
other two replicates, I would like to know if a t-test accounts for
this variability?
If a gene is differentially expressed in diseased cells compared to
normal cells, I want to make sure that all the replicates of diseased
cells were in fact having a similar gene expression profile...
I hope it makes sense... I'm pretty new to all this
Thanks heaps
Yogi
-----Original Message-----
From: Jenny Drnevich [mailto:drnevich@uiuc.edu]
Sent: Friday, 28 September 2007 1:33 AM
To: Yogi Sundaravadanam; bioconductor
Subject: Re: [BioC] Biological replicates
Hi Yogi,
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'm not sure what your question is... the fact that a large amount of
biological variation among samples in one treatment group will affect
the P-value in a t-test is EXACTLY how the statistical analysis
accounts for a large amount of biological variation. In simplified
terms, a t-test calculates the differences in the means between two
groups, then adjusts for the amount of biological variation within
each group. The p-value is the probability of getting the calculated
t-value if the two groups had been randomly sampled from the same
distribution. A low probability leads to the conclusion that the two
groups were likely sampled from distributions with different means.
If this doesn't answer your question, perhaps you could elaborate on
exactly how you want to account for biological variation in the
statistical analysis?
Cheers,
Jenny
>
>
>I will be much grateful if you guys could shed some light on this
topic?
>
>
>
>
>Thank you
>
>Yogi
>
>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives:
>http://news.gmane.org/gmane.science.biology.informatics.conductor
Jenny Drnevich, Ph.D.
Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign
330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA
ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at uiuc.edu

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]]
>>>
>>>_______________________________________________
>>>Bioconductor mailing list
>>>Bioconductor at stat.math.ethz.ch
>>>https://stat.ethz.ch/mailman/listinfo/bioconductor
>>>Search the archives:
>>>http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>>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
>>Bioconductor at stat.math.ethz.ch
>>https://stat.ethz.ch/mailman/listinfo/bioconductor
>>Search the archives: http://news.gmane.org/gmane.science.biology.inf
>ormatics.conductor
>>

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]]
> >>>
> >>>_______________________________________________
> >>>Bioconductor mailing list
> >>>Bioconductor at stat.math.ethz.ch
> >>>https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>>Search the archives:
> >>>http://news.gmane.org/gmane.science.biology.informatics.conductor
> >>
> >>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
> >>Bioconductor at stat.math.ethz.ch
> >>https://stat.ethz.ch/mailman/listinfo/bioconductor
> >>Search the archives:
http://news.gmane.org/gmane.science.biology.inf
> >ormatics.conductor
> >>
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives:
>http://news.gmane.org/gmane.science.biology.informatics.conductor
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

>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@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]]
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives:
>http://news.gmane.org/gmane.science.biology.informatics.conductor
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

Thanks for the details. The DNA microarray platforms like Buserelin Acetate generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Using the standard curve method and the same set of standard DNA for every qpcr run.