LogFC query in Limma
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@roopa-subbaiaih-5490
Last seen 6.9 years ago
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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. 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. My question is am I doing it right? Why does the number drastically reduce? Is there a way to do it? Thanks, Roopa On Thu, Jan 31, 2013 at 5:36 AM, Maciej JoÅczyk <mjonczyk@biol.uw.edu.pl>wrote: > Hi Roopa, > > > results <- decideTests(fit2, adjust="fdr", p=0.05) >> summary(results) >> write.table(results,file="**myresults.txt") >> >> The results table shows ~10,000 genes to be upregulated and ~12,000 genes >> to be down regulated. >> >> My question is how can I get fold change values associated with these >> genes >> in the results file? >> > > You can create object with needed columns binded: > > e.g. > > x=cbind(fit$coefficients,fit$**p.value,p.adjust(fit$p.value,"** > BH"),separate) > > Where "fit" is the result of lmFit and then eBayes commands - it contains > "coefficients" column with (mean) log2 fold change for each gene. > > *In fact it haven't been clear for me at first - but I compared it to the > output of topTable (logFC column) and it is equal.* > > Back to the table - proposed example will give you: > log2 fold change, > raw p-value, > corrected p-value (here Benjamini-Hochberg, equall to "fdr"), > and finally change direction for significant genes (where separate is > result of decideTests) > > Before "write.table" you can change column names with colnames(x)=c(...) > > > HTH > > -- > Maciej Jonczyk, > Department of Plant Molecular Ecophysiology > Faculty of Biology, University of Warsaw > 02-096 Warsaw, Miecznikowa 1 > Poland > > > -- > This email was Anti Virus checked by Astaro Security Gateway. > http://www.astaro.com > [[alternative HTML version deleted]] Pathways convert Pathways convert • 2.5k views ADD COMMENT 0 Entering edit mode @steve-lianoglou-2771 Last seen 2 hours ago Denali 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 ADD COMMENT 0 Entering edit mode 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? Please advice, thanks, Roopa On Thu, Jan 31, 2013 at 3:23 PM, Steve Lianoglou < mailinglist.honeypot@gmail.com> wrote: > Hi, > > On Thu, Jan 31, 2013 at 2:54 PM, Roopa Subbaiaih <rss115@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 > -- --------------------------------------- Roopa Shree Subbaiaih Post Doctoral Fellow Department of Dermatology School of Medicine Case Western Reserve University Cleveland, OH-44106 Tel:+1 216 368 0211 [[alternative HTML version deleted]] ADD REPLY 0 Entering edit mode 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 ADD REPLY 0 Entering edit mode ... 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 ADD REPLY 0 Entering edit mode What you said was true. They are probe sets. When I upload the information into IPA 54k probe sets gives ~18k genes identified. I was concerned with fold changes for further analysis. Thanks, Roopa On Thu, Jan 31, 2013 at 4:02 PM, Steve Lianoglou < mailinglist.honeypot@gmail.com> wrote: > ... 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@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@gmail.com> wrote: > >> > >>> Hi, > >>> > >>> On Thu, Jan 31, 2013 at 2:54 PM, Roopa Subbaiaih<rss115@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 > -- --------------------------------------- Roopa Shree Subbaiaih Post Doctoral Fellow Department of Dermatology School of Medicine Case Western Reserve University Cleveland, OH-44106 Tel:+1 216 368 0211 [[alternative HTML version deleted]] ADD REPLY 0 Entering edit mode If you just use the expression values from the original authors, I get just under 9K probesets for this comparison at an FDR of 0.05 and no fold change criterion. It drops to just under 900 with a 2-fold difference added in. So yeah, seems like a lot to me as well. Best, Jim On 1/31/2013 4:02 PM, Steve Lianoglou wrote: > ... 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 >> > > -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099