Dear Biju,
Using the contrast (A1+B1)/2 - (A2+B2)/2 will find genes which
response in
the same direction, and perhaps by about the same fold change, in both
A
and B.
Doing separate tests for A1-A2 and B1-B2, does not require genes to be
changing in the same direction in A and B.
Best wishes
Gordon
> Date: Thu, 11 Mar 2010 14:49:35 +0100
> From: "Biju Joseph" <bjoseph at="" hygiene.uni-wuerzburg.de="">
> To: <bioconductor at="" stat.math.ethz.ch="">
> Subject: [BioC] Limma-Contrasts-Question
> Message-ID: <000001cac121$b0cbd3b0$12637b10$@uni-wuerzburg.de>
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear all
>
> Sorry if this is a trivial question,
> My experiment design is the following
> 2 strains (A & B) subjected to 4 conditions each (1,2,3,4) compared
using a
> common reference design.
> We are interested in various contrasts between the 8 samples.
> Using limma - I was able to generate topTables for the required
contrasts
> eg:
> A1-B1, A2-B2, A3-B3, A4-B4
> A1-A2, A2-A3, B1-B2, B2-B3 and so on
>
> A comparison for example the topTable A1-A2 and B1-B2, would
represent the
> common response in A and B from condition 1 and condition 2.
>
> Using this manual comparison of the 2 topTables, I saw that around
400 genes
> are commonly differentially regulated in strain A and strain B in
the
> conditions 1 and 2.
>
> Now when I include the following contrast in my model in limma
>
> (A1+B1)/2 - (A2+B2)/2 which in my understanding also generates the
common
> response between condition 1 and 2 in the 2 strains A and B.
>
> The topTable generated using this contrast shows only 10 genes to be
> commonly differentially regulated between condition 1 and 2.
>
> Would be great if someone could explain this discrepancy to me and
about
> which method is safer to compare(comparison of the 2 individual
toptables or
> the toptable generated using make.contrasts).
>
> Best
> Biju
> Institut f?r Hygiene und Mikrobiologie
> Universit?t W?rzburg
> Josef-Schneider-Str. 2, Geb?ude E1
> 97080 W?rzburg
> Email: bjoseph at hygiene.uni-wuerzburg.de
> Tel.: 0931 201 46708
> Fax: 0931 201 46445
______________________________________________________________________
The information in this email is confidential and
intend...{{dropped:4}}
Thanks Gordon for your answer.
In my question, I was referring to the DE expressed genes in the same
direction.
What I am actually unclear about is the following.
Lets say, I generate topTables for
1. A1-A2
2. B1-B2
Now using these 2 individual tables, I could pull out the genes common
in either direction using lets say Access.
Is this method valid and safe?
How comparable should this manually generated common response between
strains A and B in conditions 1 and 2 be comparable to the topTable
generated using (A1+B1)/2 - (A2+B2)/2. Of course I think that these 2
methods should generate more or less the same results at least with
respect to numbers of DE genes.
What FC is better to report for the common response? One from using
the (A1+B1)/2 - (A2+B2)/2 contrast or the FC from the individual
topTables.
Best
Biju
Quoting Gordon K Smyth <smyth at="" wehi.edu.au="">:
> Dear Biju,
>
> Using the contrast (A1+B1)/2 - (A2+B2)/2 will find genes which
> response in the same direction, and perhaps by about the same fold
> change, in both A and B.
>
> Doing separate tests for A1-A2 and B1-B2, does not require genes to
> be changing in the same direction in A and B.
>
> Best wishes
> Gordon
>
>> Date: Thu, 11 Mar 2010 14:49:35 +0100
>> From: "Biju Joseph" <bjoseph at="" hygiene.uni-wuerzburg.de="">
>> To: <bioconductor at="" stat.math.ethz.ch="">
>> Subject: [BioC] Limma-Contrasts-Question
>> Message-ID: <000001cac121$b0cbd3b0$12637b10$@uni-wuerzburg.de>
>> Content-Type: text/plain; charset="iso-8859-1"
>>
>> Dear all
>>
>> Sorry if this is a trivial question,
>> My experiment design is the following
>> 2 strains (A & B) subjected to 4 conditions each (1,2,3,4) compared
using a
>> common reference design.
>> We are interested in various contrasts between the 8 samples.
>> Using limma - I was able to generate topTables for the required
contrasts
>> eg:
>> A1-B1, A2-B2, A3-B3, A4-B4
>> A1-A2, A2-A3, B1-B2, B2-B3 and so on
>>
>> A comparison for example the topTable A1-A2 and B1-B2, would
represent the
>> common response in A and B from condition 1 and condition 2.
>>
>> Using this manual comparison of the 2 topTables, I saw that around
400 genes
>> are commonly differentially regulated in strain A and strain B in
the
>> conditions 1 and 2.
>>
>> Now when I include the following contrast in my model in limma
>>
>> (A1+B1)/2 - (A2+B2)/2 which in my understanding also generates the
common
>> response between condition 1 and 2 in the 2 strains A and B.
>>
>> The topTable generated using this contrast shows only 10 genes to
be
>> commonly differentially regulated between condition 1 and 2.
>>
>> Would be great if someone could explain this discrepancy to me and
about
>> which method is safer to compare(comparison of the 2 individual
toptables or
>> the toptable generated using make.contrasts).
>>
>> Best
>> Biju
>>
>
______________________________________________________________________
> The information in this email is confidential and
inte...{{dropped:10}}
Hi Biju,
One thing to consider is that the question you're asking - "which
genes have the SAME response in A and B?" is not what a statistical
test is designed to measure. Instead, the null hypothesis is that the
responses are the same, and only if there is enough evidence of a
different between the responses will the statistical test become
significant. One possibility would be to do the 3 following contrasts:
1) A1-A2
2) B1-B2
3) (A1-A2) - (B1-B2)
The third one tests whether the response in A is the same as the
response in B. You could do a Venn Diagram on these three contrasts,
and a those genes that are significant in 1) and 2) but not
significant in 3) could be considered genes that have the a
significant response in A and the "same" (i.e., not significantly
different) significant response in B.
Note that genes could be significant for 3), even if they change in
the same direction, if they change by differing amounts (2-fold up
versus 20-fold up). Whether you want to call this the "same" response
depends on your research questions...
HTH,
Jenny
At 02:17 AM 3/15/2010, Biju Joseph wrote:
>Thanks Gordon for your answer.
>
>In my question, I was referring to the DE expressed genes in the same
>direction.
>
>What I am actually unclear about is the following.
>
>Lets say, I generate topTables for
>1. A1-A2
>2. B1-B2
>
>Now using these 2 individual tables, I could pull out the genes
common
>in either direction using lets say Access.
>
>Is this method valid and safe?
>How comparable should this manually generated common response between
>strains A and B in conditions 1 and 2 be comparable to the topTable
>generated using (A1+B1)/2 - (A2+B2)/2. Of course I think that these 2
>methods should generate more or less the same results at least with
>respect to numbers of DE genes.
>
>What FC is better to report for the common response? One from using
>the (A1+B1)/2 - (A2+B2)/2 contrast or the FC from the individual
>topTables.
>
>Best
>Biju
>
>
>Quoting Gordon K Smyth <smyth at="" wehi.edu.au="">:
>
>>Dear Biju,
>>
>>Using the contrast (A1+B1)/2 - (A2+B2)/2 will find genes which
>>response in the same direction, and perhaps by about the same fold
>>change, in both A and B.
>>
>>Doing separate tests for A1-A2 and B1-B2, does not require genes to
>>be changing in the same direction in A and B.
>>
>>Best wishes
>>Gordon
>>
>>>Date: Thu, 11 Mar 2010 14:49:35 +0100
>>>From: "Biju Joseph" <bjoseph at="" hygiene.uni-wuerzburg.de="">
>>>To: <bioconductor at="" stat.math.ethz.ch="">
>>>Subject: [BioC] Limma-Contrasts-Question
>>>Message-ID: <000001cac121$b0cbd3b0$12637b10$@uni-wuerzburg.de>
>>>Content-Type: text/plain; charset="iso-8859-1"
>>>
>>>Dear all
>>>
>>>Sorry if this is a trivial question,
>>>My experiment design is the following
>>>2 strains (A & B) subjected to 4 conditions each (1,2,3,4) compared
using a
>>>common reference design.
>>>We are interested in various contrasts between the 8 samples.
>>>Using limma - I was able to generate topTables for the required
contrasts
>>>eg:
>>>A1-B1, A2-B2, A3-B3, A4-B4
>>>A1-A2, A2-A3, B1-B2, B2-B3 and so on
>>>
>>>A comparison for example the topTable A1-A2 and B1-B2, would
represent the
>>>common response in A and B from condition 1 and condition 2.
>>>
>>>Using this manual comparison of the 2 topTables, I saw that around
400 genes
>>>are commonly differentially regulated in strain A and strain B in
the
>>>conditions 1 and 2.
>>>
>>>Now when I include the following contrast in my model in limma
>>>
>>>(A1+B1)/2 - (A2+B2)/2 which in my understanding also generates the
common
>>>response between condition 1 and 2 in the 2 strains A and B.
>>>
>>>The topTable generated using this contrast shows only 10 genes to
be
>>>commonly differentially regulated between condition 1 and 2.
>>>
>>>Would be great if someone could explain this discrepancy to me and
about
>>>which method is safer to compare(comparison of the 2 individual
toptables or
>>>the toptable generated using make.contrasts).
>>>
>>>Best
>>>Biju
>>____________________________________________________________________
__
>>The information in this email is confidential and
inte...{{dropped:10}}
>
>_______________________________________________
>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 illinois.edu
Thanks Jenny, Your suggestion answers my question.
(i.e)
I make the following Contrasts as you suggest
1. A1-A2
2. B1-B2
3. (A1-A2)-(B1-B2)
And then the genes that are significant in contrasts 1 and 2 but not
in
contrast 3 would be considered as those that have a significant
response in
A and B. This is perfect and answers my question as well.
But what is still not clear to me is what happens when I use the
contrast
(A1+B1)/2 - (A2+B2)/2.
Why do I get a much larger list of genes called as significantly DE
when I
use this contrast compared to the method "significant in contrast 1
and 2
but not in contrast 3".
Best
Biju
Institut f?r Hygiene und Mikrobiologie
Universit?t W?rzburg
Josef-Schneider-Str. 2, Geb?ude E1
97080 W?rzburg
Email: bjoseph at hygiene.uni-wuerzburg.de
Tel.: 0931 201 46708
Fax: 0931 201 46445
-----Urspr?ngliche Nachricht-----
Von: Jenny Drnevich [mailto:drnevich at illinois.edu]
Gesendet: Dienstag, 16. M?rz 2010 15:04
An: Biju Joseph; Gordon K Smyth
Cc: Bioconductor mailing list
Betreff: Re: [BioC] Limma-Contrasts-Question
Hi Biju,
One thing to consider is that the question you're asking - "which
genes have the SAME response in A and B?" is not what a statistical
test is designed to measure. Instead, the null hypothesis is that the
responses are the same, and only if there is enough evidence of a
different between the responses will the statistical test become
significant. One possibility would be to do the 3 following contrasts:
1) A1-A2
2) B1-B2
3) (A1-A2) - (B1-B2)
The third one tests whether the response in A is the same as the
response in B. You could do a Venn Diagram on these three contrasts,
and a those genes that are significant in 1) and 2) but not
significant in 3) could be considered genes that have the a
significant response in A and the "same" (i.e., not significantly
different) significant response in B.
Note that genes could be significant for 3), even if they change in
the same direction, if they change by differing amounts (2-fold up
versus 20-fold up). Whether you want to call this the "same" response
depends on your research questions...
HTH,
Jenny
At 02:17 AM 3/15/2010, Biju Joseph wrote:
>Thanks Gordon for your answer.
>
>In my question, I was referring to the DE expressed genes in the same
>direction.
>
>What I am actually unclear about is the following.
>
>Lets say, I generate topTables for
>1. A1-A2
>2. B1-B2
>
>Now using these 2 individual tables, I could pull out the genes
common
>in either direction using lets say Access.
>
>Is this method valid and safe?
>How comparable should this manually generated common response between
>strains A and B in conditions 1 and 2 be comparable to the topTable
>generated using (A1+B1)/2 - (A2+B2)/2. Of course I think that these 2
>methods should generate more or less the same results at least with
>respect to numbers of DE genes.
>
>What FC is better to report for the common response? One from using
>the (A1+B1)/2 - (A2+B2)/2 contrast or the FC from the individual
>topTables.
>
>Best
>Biju
>
>
>Quoting Gordon K Smyth <smyth at="" wehi.edu.au="">:
>
>>Dear Biju,
>>
>>Using the contrast (A1+B1)/2 - (A2+B2)/2 will find genes which
>>response in the same direction, and perhaps by about the same fold
>>change, in both A and B.
>>
>>Doing separate tests for A1-A2 and B1-B2, does not require genes to
>>be changing in the same direction in A and B.
>>
>>Best wishes
>>Gordon
>>
>>>Date: Thu, 11 Mar 2010 14:49:35 +0100
>>>From: "Biju Joseph" <bjoseph at="" hygiene.uni-wuerzburg.de="">
>>>To: <bioconductor at="" stat.math.ethz.ch="">
>>>Subject: [BioC] Limma-Contrasts-Question
>>>Message-ID: <000001cac121$b0cbd3b0$12637b10$@uni-wuerzburg.de>
>>>Content-Type: text/plain; charset="iso-8859-1"
>>>
>>>Dear all
>>>
>>>Sorry if this is a trivial question,
>>>My experiment design is the following
>>>2 strains (A & B) subjected to 4 conditions each (1,2,3,4) compared
using
a
>>>common reference design.
>>>We are interested in various contrasts between the 8 samples.
>>>Using limma - I was able to generate topTables for the required
contrasts
>>>eg:
>>>A1-B1, A2-B2, A3-B3, A4-B4
>>>A1-A2, A2-A3, B1-B2, B2-B3 and so on
>>>
>>>A comparison for example the topTable A1-A2 and B1-B2, would
represent
the
>>>common response in A and B from condition 1 and condition 2.
>>>
>>>Using this manual comparison of the 2 topTables, I saw that around
400
genes
>>>are commonly differentially regulated in strain A and strain B in
the
>>>conditions 1 and 2.
>>>
>>>Now when I include the following contrast in my model in limma
>>>
>>>(A1+B1)/2 - (A2+B2)/2 which in my understanding also generates the
common
>>>response between condition 1 and 2 in the 2 strains A and B.
>>>
>>>The topTable generated using this contrast shows only 10 genes to
be
>>>commonly differentially regulated between condition 1 and 2.
>>>
>>>Would be great if someone could explain this discrepancy to me and
about
>>>which method is safer to compare(comparison of the 2 individual
toptables
or
>>>the toptable generated using make.contrasts).
>>>
>>>Best
>>>Biju
>>____________________________________________________________________
__
>>The information in this email is confidential and
inte...{{dropped:10}}
>
>_______________________________________________
>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 illinois.edu
Hi Biju,
I suggest you consult a statistics textbook or a
local statistician to get some more information
on 2x2 factorial designs, which is what your
experiment has. In particular, contrast 3 is
known as the interaction term, whereas (A1+B1)/2
- (A2+B2)/2 is the main effect of condition 1 vs.
condition 2 AVERAGED over the levels (A & B) of
the second factor. The reason why you want to
use the interaction term instead of the main
effect term is because the values for A2, B1 and
B2 could be very similar, but if A1 is much
larger, the average of A1 and B1 could be enough
to give you a significant main effect term, even
though the response in not really the same. In a
typical 2x2 ANOVA, if the interaction term is
significant, then you ignore the results for the
main effects because they may be misleading...
Jenny
At 07:37 AM 3/18/2010, Biju Joseph wrote:
>Thanks Jenny, Your suggestion answers my question.
>(i.e)
>
>I make the following Contrasts as you suggest
>1. A1-A2
>2. B1-B2
>3. (A1-A2)-(B1-B2)
>
>And then the genes that are significant in contrasts 1 and 2 but not
in
>contrast 3 would be considered as those that have a significant
response in
>A and B. This is perfect and answers my question as well.
>
>But what is still not clear to me is what happens when I use the
contrast
>
>(A1+B1)/2 - (A2+B2)/2.
>
>Why do I get a much larger list of genes called as significantly DE
when I
>use this contrast compared to the method "significant in contrast 1
and 2
>but not in contrast 3".
>
>Best
>Biju
>
>Institut f?r Hygiene und Mikrobiologie
>Universit?t W?rzburg
>Josef-Schneider-Str. 2, Geb?ude E1
>97080 W?rzburg
>Email: bjoseph at hygiene.uni-wuerzburg.de
>Tel.: 0931 201 46708
>Fax: 0931 201 46445
>
>-----Urspr?ngliche Nachricht-----
>Von: Jenny Drnevich [mailto:drnevich at illinois.edu]
>Gesendet: Dienstag, 16. M?rz 2010 15:04
>An: Biju Joseph; Gordon K Smyth
>Cc: Bioconductor mailing list
>Betreff: Re: [BioC] Limma-Contrasts-Question
>
>Hi Biju,
>
>One thing to consider is that the question you're asking - "which
>genes have the SAME response in A and B?" is not what a statistical
>test is designed to measure. Instead, the null hypothesis is that the
>responses are the same, and only if there is enough evidence of a
>different between the responses will the statistical test become
>significant. One possibility would be to do the 3 following
contrasts:
>
>1) A1-A2
>2) B1-B2
>3) (A1-A2) - (B1-B2)
>
>The third one tests whether the response in A is the same as the
>response in B. You could do a Venn Diagram on these three contrasts,
>and a those genes that are significant in 1) and 2) but not
>significant in 3) could be considered genes that have the a
>significant response in A and the "same" (i.e., not significantly
>different) significant response in B.
>
>Note that genes could be significant for 3), even if they change in
>the same direction, if they change by differing amounts (2-fold up
>versus 20-fold up). Whether you want to call this the "same" response
>depends on your research questions...
>
>HTH,
>Jenny
>
>At 02:17 AM 3/15/2010, Biju Joseph wrote:
> >Thanks Gordon for your answer.
> >
> >In my question, I was referring to the DE expressed genes in the
same
> >direction.
> >
> >What I am actually unclear about is the following.
> >
> >Lets say, I generate topTables for
> >1. A1-A2
> >2. B1-B2
> >
> >Now using these 2 individual tables, I could pull out the genes
common
> >in either direction using lets say Access.
> >
> >Is this method valid and safe?
> >How comparable should this manually generated common response
between
> >strains A and B in conditions 1 and 2 be comparable to the topTable
> >generated using (A1+B1)/2 - (A2+B2)/2. Of course I think that these
2
> >methods should generate more or less the same results at least with
> >respect to numbers of DE genes.
> >
> >What FC is better to report for the common response? One from using
> >the (A1+B1)/2 - (A2+B2)/2 contrast or the FC from the individual
> >topTables.
> >
> >Best
> >Biju
> >
> >
> >Quoting Gordon K Smyth <smyth at="" wehi.edu.au="">:
> >
> >>Dear Biju,
> >>
> >>Using the contrast (A1+B1)/2 - (A2+B2)/2 will find genes which
> >>response in the same direction, and perhaps by about the same fold
> >>change, in both A and B.
> >>
> >>Doing separate tests for A1-A2 and B1-B2, does not require genes
to
> >>be changing in the same direction in A and B.
> >>
> >>Best wishes
> >>Gordon
> >>
> >>>Date: Thu, 11 Mar 2010 14:49:35 +0100
> >>>From: "Biju Joseph" <bjoseph at="" hygiene.uni-wuerzburg.de="">
> >>>To: <bioconductor at="" stat.math.ethz.ch="">
> >>>Subject: [BioC] Limma-Contrasts-Question
> >>>Message-ID: <000001cac121$b0cbd3b0$12637b10$@uni-wuerzburg.de>
> >>>Content-Type: text/plain; charset="iso-8859-1"
> >>>
> >>>Dear all
> >>>
> >>>Sorry if this is a trivial question,
> >>>My experiment design is the following
> >>>2 strains (A & B) subjected to 4 conditions each (1,2,3,4)
compared using
>a
> >>>common reference design.
> >>>We are interested in various contrasts between the 8 samples.
> >>>Using limma - I was able to generate topTables for the required
contrasts
> >>>eg:
> >>>A1-B1, A2-B2, A3-B3, A4-B4
> >>>A1-A2, A2-A3, B1-B2, B2-B3 and so on
> >>>
> >>>A comparison for example the topTable A1-A2 and B1-B2, would
represent
>the
> >>>common response in A and B from condition 1 and condition 2.
> >>>
> >>>Using this manual comparison of the 2 topTables, I saw that
around 400
>genes
> >>>are commonly differentially regulated in strain A and strain B in
the
> >>>conditions 1 and 2.
> >>>
> >>>Now when I include the following contrast in my model in limma
> >>>
> >>>(A1+B1)/2 - (A2+B2)/2 which in my understanding also generates
the common
> >>>response between condition 1 and 2 in the 2 strains A and B.
> >>>
> >>>The topTable generated using this contrast shows only 10 genes to
be
> >>>commonly differentially regulated between condition 1 and 2.
> >>>
> >>>Would be great if someone could explain this discrepancy to me
and about
> >>>which method is safer to compare(comparison of the 2 individual
toptables
>or
> >>>the toptable generated using make.contrasts).
> >>>
> >>>Best
> >>>Biju
> >>__________________________________________________________________
____
> >>The information in this email is confidential and
inte...{{dropped:10}}
> >
> >_______________________________________________
> >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 illinois.edu
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 illinois.edu
