Hi Joe,
For the heatmap, what I meant was to subtract the mean value of each
gene from the intensity values of that gene.
Alternatively, you can set scale=ârowâ in the heatmap
function.
Regards,
Yunshun
-----------------------------
Yunshun Chen,
Research Officer,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Email: yuchen@wehi.edu.au
From: houjue@cmpt.ac.cn [mailto:houjue@cmpt.ac.cn]
Sent: Wednesday, 19 February 2014 1:41 PM
To: yuchen@wehi.EDU.AU
Cc: bioconductor@r-project.org; smyth
Subject: Re: RE: [BioC] limma for time course dataset 2
Hi Yunshun,
Thanks so much. I get your points eventually. Previously, I sorted
DEG at Day14 from regression model as setting absolute log2FC more
than log2(1.5) from topTable function. Now, I guess it's not correct,
it should be done by one-way pair-wise comparisons.
For heatmap, you mean get mean value of each group (with every gene),
then substract baseline mean intensity ? That's right?
Best,
Joe
_____
houjue@cmpt.ac.cn
From: Yunshun Chen <mailto:yuchen@wehi.edu.au>
Date: 2014-02-19 10:19
To: houjue@cmpt.ac.cn
CC: bioconductor@r-project.org; Gordon K Smyth
<mailto:smyth@wehi.edu.au>
Subject: RE: [BioC] limma for time course dataset 2
Hi Joe,
DEG (DE genes I suppose) have to be defined under particular
comparisons.
It is very common that a gene is DE between group 1 and group 2, but
not DE between group 1 and group 3.
Also, the one-way approach and the regression approach are testing
different hypotheses.
The one-way approach can produce results for pair-wise comparisons or
comparisons with any contrasts.
The regression approach tends to detect the general differences due to
the time effect.
I wonder how you compare Day 0 with Day 14 under the regression model.
When you refer to âall DEGâ, how do you define your âall DEGâ?
Do you mean the genes that are differentially expressed in at least
one of the pair-wise comparisons?
If so, you can do all the pair-wise comparisons under the one-way
approach and then take the union of them.
For the heatmap, you might need to subtract the average expression
value of each gene for every gene.
Hope that helps.
Regards,
Yunshun
From: houjue@cmpt.ac.cn [mailto:houjue@cmpt.ac.cn]
Sent: Wednesday, 19 February 2014 12:12 PM
To: yuchen@wehi.EDU.AU
Cc: bioconductor@r-project.org; smyth
Subject: Re: RE: [BioC] limma for time course dataset 2
Hi Yunshun,
Thanks so much for your reply. Actually, I could understand your
points, and both one-way layout and fit regression spline trend have
been used in my data. What make me confused and consider is fit
regression will miss some of DEG that actual present significant
difference in one-way approach. Like, Gene A showed as DEG when I
compare baseline(Day0) with Day 14, but not in regression approach. So
I want to make a whole heatmap inculed all DEG, how to achieve this
goal in limma? one more thing, usually, in heatmap, as you suggestion,
intensity or fold-change of each group would be used in heatmap? I
feel, intensity looks not pretty clear and obvious.
Best,
Joe
_____
houjue@cmpt.ac.cn
From: Yunshun Chen <mailto:yuchen@wehi.edu.au>
Date: 2014-02-19 08:46
To: houjue@cmpt.ac.cn
CC: bioconductor@r-project.org; Gordon K Smyth
<mailto:smyth@wehi.edu.au>
Subject: RE: [BioC] limma for time course dataset 2
Hi Joseph,
The way to analyse a time-course experiment depends on what scientific
question you want to answer.
If, say, you are interested in the difference between time 1 and time
2 for
all 21 subjects, then you can compare those two groups using the
standard
one-way layout analysis.
If you are interested in whether the expression levels of the genes
change
along the time, then you can analyse the data by fitting a trend using
a
regression spline or a polynomial.
There is no information on how you fit your data and what you are
trying to
find.
Did you use the one-way layout approach or fit a regression spline
trend?
That determines the meaning of the coefficients in your output.
In other words, what 'coef' to use for the 'topTable' depends on what
model
you fit to your data and what question you want to answer.
There is a time course experiment case study in the limma user's guide
(section 9.6).
It might be helpful to answer your question.
Regards,
Yunshun
-----------------------------
Yunshun Chen,
Research Officer,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Email: yuchen@wehi.edu.au
------------------------------
Message: 14
Date: Tue, 18 Feb 2014 15:15:21 +0800
From: Joseph J Hou <houjue00722@sina.com>
To: smyth <smyth@wehi.edu.au>
Cc: bioconductor <bioconductor@stat.math.ethz.ch>
Subject: [BioC] limma for time course dataset 2
Message-ID: <2014021815152088222923@sina.com>
Content-Type: text/plain
Hi Gordon,
If I have multiplex time points, when set coeff to "NULL" in topTable
function, it return results (named as R1) from F-statistics, however,
if I
set coeff to each time point, one by one, then I get DEG in each time
point
(named as R2, combined all DEG symbol in one list). Then, R1 and R2
have
some overlap, but R2 usually has more genes that excluded in R1. ?So,
in
this case, which approach back correct DEG gene list? ?
Best,
Joe
Joseph J Hou
----------------------------------------------
Jue Hou Ph.D.
Research Assistant
Center of Medical Physics and Technology
Hefei Institutes of Physical Science
Chinese Academy of Sciences
No.350 Shushanhu Road,Shushan District,Heifei,P.R. China
Tel. +86551-65595385
Email: joseph.houjue@gmail.com; houjue@cmpt.ac.cn
?7"<~HK#:?Joseph J
Hou7"KMJ1<d#:?2014-02-17?10:30ju<~hk#:?smyth3-km#:?bioconductorvwlb#:? limma="" for="" time="" course="" dataset="" hi?gordon,="" my="" project="" is="" time="" course="" dataset,="" 9="" time="" points="" and="" 21="" subjects.="" when="" i="" use="" "toptable"="" function="" to="" call="" differential="" genes,="" how="" to="" set="" argument="" "coeff"="" ?="" if="" i="" set="" it="" as="" each="" time="" point="" (column="" name="" specifying)="" one="" by="" one,="" it'll="" reture="" results="" by="" t-test.="" ??if="" i="" set="" to="" as="" "null",="" toptable="" will="" use="" f-ststistic="" to="" rank="" genes="" with="" all="" coefficient,="" and="" usually="" the="" difference="" gene="" by="" f-="" test="" will="" smaller="" than="" t-test="" by="" each="" time="" points.="" i'm="" worry="" about="" f-ststistic="" will="" miss="" some="" of="" meaning="" informations.="" so,="" as="" you="" suggestion,="" how="" to="" set="" the="" coeff="" parameter="" to="" time-course="" dataset,="" to="" find="" allover="" deg="" and="" deg="" in="" individual="" time="" point??p.s.="" usually,="" in="" heatmap="" figure,="" log-fold="" change="" or="" normalized="" intensity="" of="" each="" group="" used="" in="" visualization??="" best,joe="" joseph="" j="" hou="" ----------------------------------------------="" jue="" hou="" ph.d.="" research="" assistant="" center="" of="" medical="" physics="" and="" technology="" hefei="" institutes="" of="" physical="" science="" chinese="" academy="" of="" sciences="" no.350="" shushanhu="" road,shushan="" district,heifei,p.r.="" china="" tel.="" +86551-65595385="" email:="" joseph.houjue@gmail.com;="" houjue@cmpt.ac.cn="" [[alternative="" html="" version="" deleted]]="" ------------------------------="" _______________________________________________="" bioconductor="" mailing="" list="" bioconductor@r-project.org="" https:="" stat.ethz.ch="" mailman="" listinfo="" bioconductor="" end="" of="" bioconductor="" digest,="" vol="" 132,="" issue="" 19="" *********************************************="" ______________________________________________________________________="" the="" information="" in="" this="" email="" is="" confidential="" and="" intend...{{dropped:26}}="" <="" div="">