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 'all DEG', how do you define your 'all DEG'?

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

 

 

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