[BioC] LogFC query in Limma

[Steve Lianoglou]

... what Jim said.

But also, this 20k differentially expressed (likely probe sets, not
genes) is raising a red flag for me, no? Am I alone here?

That's .. what's the word I'm looking for ... "a lot".

-steve

On Thu, Jan 31, 2013 at 3:56 PM, James W. MacDonald <jmacdon at uw.edu> wrote:
> Hi Roopa,
>
>
> On 1/31/2013 3:45 PM, Roopa Subbaiaih wrote:
>>
>> Hi Steve,
>>
>> This was the script I used-
>> getwd()
>> setwd(dir="/CRSP 406-11/DEMOS/GSE14905-a")
>> ls()
>> #-----------------------------------------------#
>> library(affy)
>> eset = justRMA()
>> f<- factor(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
>>
>> 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2),
>> labels=c("Healthy", "unaffected"))
>> design<- model.matrix(~ 0 + f)
>> design
>> colnames(design)<-c("Healthy","unaffected")
>> design
>> library(limma)
>> fit<- lmFit(eset, design)
>> library(hgu133plus2.db)
>> fit$genes$Symbol<- getSYMBOL(fit$genes$ID,"hgu133plus2.db")
>> contrast.matrix<-makeContrasts(affected-Healthy,levels = design)
>> fit2<- contrasts.fit(fit, contrast.matrix)
>> fit2<- eBayes(fit2)
>> topTable(fit2,coef=1,p=0.05, adjust="fdr")
>> results<- decideTests(fit2, adjust="fdr", p=0.05)
>> summary(results)
>> write.table(results,file="myresults.txt")
>> write.fit().
>>
>> I had identified ~54,000 genes of which ~ 20K were differentially
>> expressed.
>>
>> But when I use these genes for pathway analysis the software asks for fold
>> change values but not p value so it is easier to analyze the data.
>>
>> What I did was - I used the differentially expressed gene table for
>> further
>> analysis. That is I converted logFC values to FC(test/control) assuming
>> that
>>
>> FC= FCmean(test)-FCmean(blank) and LogFC is log2 of FC values.
>>
>> Once I got test/control values I converted them to fold changes using "IF"
>> function in excel sheet to eliminate genes with fold changes between -2 to
>> +2.
>>
>> Once I did this the number of significant genes drastically reduced to ~
>> 2,000.
>>
>> Is this the right method?
>
>
> No. Note that the range of fold changes after 'unlogging' will be 0-INF, and
> the down-regulated genes will be in the range 0-1 whereas the upregulated
> genes will be in the range 1-INF. (e.g. two fold up will be 2, whereas 2
> fold down will be 1/2 or 0.5).
>
> The easiest way to filter is to keep the logFC and filter on -1 and 1. Or
> you can use the lfc argument to decideTests().
>
> Best,
>
> Jim
>
>
>
>>
>> Please advice, thanks, Roopa
>>
>> On Thu, Jan 31, 2013 at 3:23 PM, Steve Lianoglou<
>> mailinglist.honeypot at gmail.com>  wrote:
>>
>>> Hi,
>>>
>>> On Thu, Jan 31, 2013 at 2:54 PM, Roopa Subbaiaih<rss115 at case.edu>  wrote:
>>>>
>>>> Hi All,
>>>>
>>>> Thanks for the reply, I could pull out the the whole information for
>>>> differentially expressed genes. The criteria used was adjust="fdr",
>>>
>>> p=0.05.
>>>>
>>>> I came up with ~ 20,000 genes to be differentially expressed.
>>>
>>> Hmm ... surely 20k cannot be correct?
>>>
>>>> Since I wanted to analyze these genes for deregulated pathways I had to
>>>> come up with fold change values for further analysis.
>>>>
>>>> I assume that for each gene FC= FCmean(test)-FCmean(blank). LogFC is
>>>> log2
>>>> of FC values.
>>>>
>>>> When I convert the FC values (test/blank) to foldchanges using IF
>>>
>>> function
>>>>
>>>> I get lesser number of genes to be deregulated. The criteria was =>2
>>>> foldchanges and =<-2 fold changes.
>>>
>>> I'm missing previous context to this email, so -- not sure what the
>>> "IF function" is, but if you're using limma, the log2fold changes are
>>> reported for you in the logFC column that is returned from
>>> `topTable(...)`
>>>
>>> -steve
>>>
>>> --
>>> Steve Lianoglou
>>> Graduate Student: Computational Systems Biology
>>>   | Memorial Sloan-Kettering Cancer Center
>>>   | Weill Medical College of Cornell University
>>> Contact Info: http://cbio.mskcc.org/~lianos/contact
>>>
>>
>>
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>



-- 
Steve Lianoglou
Graduate Student: Computational Systems Biology
 | Memorial Sloan-Kettering Cancer Center
 | Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact