Discussion on gene-1-0-st-array-analysis
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Diya Vaka
Aug 27, 2009, 3:46:13 PM8/27/09
to aroma.affymetrix
Hello All,
I want to know about the up and down regulated genes.So how am i
supposed to proceed after this step
Diya
I want to know about the up and down regulated genes.So how am i
supposed to proceed after this step
Diya
Mathieu Parent
Aug 28, 2009, 8:28:48 AM8/28/09
to aroma-af...@googlegroups.com
Hi,
They way it has been proposed to me, is to extract the matrix from the normalised and summarised data, log it and pass into the LIMMA package for differential expression analysis.
What is your experimental design ?
Math
McGill University
Diya v
Aug 28, 2009, 10:13:32 AM8/28/09
to aroma-af...@googlegroups.com, parent....@gmail.com
| Hi I have 2 control and 2 treatment groups of MoGene-1_0-st.I have the data normalized and and a data matrix after fit(plm) is performed. I want to do statistical analysis for differentially expressed ganes. Can I take the datamatrix generated from aroma.affymetrix and do the analysis with limma Is there any online tutorial for this? Thanks, Diya --- On Fri, 28/8/09, Mathieu Parent <parent....@gmail.com> wrote: |
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Mark Robinson
Aug 30, 2009, 11:11:09 PM8/30/09
to aroma-af...@googlegroups.com, parent....@gmail.com
Hi Diya.
Just to add my 2 cents to this, although Mathieu has pretty much
covered it.
There are no online tutorials (that I know of) for exactly what you
want to do, but your proposed analysis is very standard. lmFit() of
limma can work directly on your matrix of logged data. Consult the
limma documentation (e.g. the limma user's guide) on how to do this.
Cheers,
Mark
Mark Robinson, PhD (Melb)
Epigenetics Laboratory, Garvan
Bioinformatics Division, WEHI
e: m.rob...@garvan.org.au
e: mrob...@wehi.edu.au
p: +61 (0)3 9345 2628
f: +61 (0)3 9347 0852
------------------------------
Just to add my 2 cents to this, although Mathieu has pretty much
covered it.
There are no online tutorials (that I know of) for exactly what you
want to do, but your proposed analysis is very standard. lmFit() of
limma can work directly on your matrix of logged data. Consult the
limma documentation (e.g. the limma user's guide) on how to do this.
Cheers,
Mark
> Love Cricket? Check out live scores, photos, video highlights and
> more. Click here
> >
>
------------------------------
> more. Click here
> >
>
Mark Robinson, PhD (Melb)
Epigenetics Laboratory, Garvan
Bioinformatics Division, WEHI
e: m.rob...@garvan.org.au
e: mrob...@wehi.edu.au
p: +61 (0)3 9345 2628
f: +61 (0)3 9347 0852
------------------------------
Mathieu Parent
Aug 31, 2009, 9:26:31 AM8/31/09
to Diya v, aroma-af...@googlegroups.com
Salut !
Here is the little function I wrote to run limma and save time. I'm a bad scripter, but this saves me a lot of time. Limma is very well documented. Here is a book chapter from Gordon Smith that covers it all:
www.statsci.org/smyth/pubs/limma-biocbook-reprint.pdf
Cheers !
(load the function)
The call for you would be...
data <- matrix_from_limma_not_logged
design <- cbind(c(1,1,0,0), c(0,0,1,1))
colnames(design) <- c("Gr1", "Gr2")
rownames(design) <- c("array1", "array2", "array3", "array4")
contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1), levels=design)
Pval <- 0.05
FDR <- "fdr"
LFC <- log2(2)
output_from_limma <- fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
rm(design, contrast, Pval, LFC, FDR)
##### Execute LIMMA to output matrix of significant genes
### ARGUMENTS
## fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
# Data matrix - Output from Aroma, NOT LOGGED. rows=genes, cols=arrays
# Design matrix for n array in 2 group (ie design <- cbind(c(1,1,0,0), c(0,0,1,1) )
# Contrast matrix (ie contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1), levels=design) )
# Pval (numeric, between 0 and 1) (ie Pval <- 0.05)
# LFC (numeric, logged base 2 value of fold change) (ie LFC<- log2(2))
# FDR correction (Caracter, '"none"', '"BH"', '"fdr"', '"BY"' or '"holm" ) (ie FDR <- "fdr")
fun.LIMMA <- function(data, design, contrast, Pval, LFC, FDR) {
library(limma)
# Fit
fit.lm <- lmFit(log(data, 2), design)
fit.co <- contrasts.fit(fit.lm, contrast)
fit.eb <- eBayes(fit.co)
# Results
results <- decideTests(fit.eb, adjust.method=FDR, p.val=Pval, lfc=LFC)
summary_table<-summary(
Here is the little function I wrote to run limma and save time. I'm a bad scripter, but this saves me a lot of time. Limma is very well documented. Here is a book chapter from Gordon Smith that covers it all:
www.statsci.org/smyth/pubs/limma-biocbook-reprint.pdf
Cheers !
(load the function)
The call for you would be...
data <- matrix_from_limma_not_logged
design <- cbind(c(1,1,0,0), c(0,0,1,1))
colnames(design) <- c("Gr1", "Gr2")
rownames(design) <- c("array1", "array2", "array3", "array4")
contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1), levels=design)
Pval <- 0.05
FDR <- "fdr"
LFC <- log2(2)
output_from_limma <- fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
rm(design, contrast, Pval, LFC, FDR)
##### Execute LIMMA to output matrix of significant genes
### ARGUMENTS
## fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
# Data matrix - Output from Aroma, NOT LOGGED. rows=genes, cols=arrays
# Design matrix for n array in 2 group (ie design <- cbind(c(1,1,0,0), c(0,0,1,1) )
# Contrast matrix (ie contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1), levels=design) )
# Pval (numeric, between 0 and 1) (ie Pval <- 0.05)
# LFC (numeric, logged base 2 value of fold change) (ie LFC<- log2(2))
# FDR correction (Caracter, '"none"', '"BH"', '"fdr"', '"BY"' or '"holm" ) (ie FDR <- "fdr")
fun.LIMMA <- function(data, design, contrast, Pval, LFC, FDR) {
library(limma)
# Fit
fit.lm <- lmFit(log(data, 2), design)
fit.co <- contrasts.fit(fit.lm, contrast)
fit.eb <- eBayes(fit.co)
# Results
results <- decideTests(fit.eb, adjust.method=FDR, p.val=Pval, lfc=LFC)
summary_table<-summary(
results)
# Filtering
signif_probes <- which(results != 0)
limma_output <- fit.eb[signif_probes,]
limma_output$signif_probes <- signif_probes
# Output
cat("\n", "\n"); cat("*** Input:", "\n") ; cat("\n", "Pval = ", Pval)
cat("\n", "LFC = ", LFC)
cat("\n", "FDR correction method = ", FDR, "\n", "\n")
cat("*** Number of significant probes :", length(signif_probes), "\n", "\n")
cat("*** Summary of the results", "\n", "\n"); print(summary(results)); cat("\n")
cat("*** Object returned: limma_output", "\n", "\n")
limma_output #returns the table as an object
# Filtering
signif_probes <- which(results != 0)
limma_output <- fit.eb[signif_probes,]
limma_output$signif_probes <- signif_probes
# Output
cat("\n", "\n"); cat("*** Input:", "\n") ; cat("\n", "Pval = ", Pval)
cat("\n", "LFC = ", LFC)
cat("\n", "FDR correction method = ", FDR, "\n", "\n")
cat("*** Number of significant probes :", length(signif_probes), "\n", "\n")
cat("*** Summary of the results", "\n", "\n"); print(summary(results)); cat("\n")
cat("*** Object returned: limma_output", "\n", "\n")
limma_output #returns the table as an object
Diya Vaka
Sep 3, 2009, 3:22:44 PM9/3/09
to aroma.affymetrix
Hi,
I have a question.I have 2contral samples(v7,v8) and 2 treatment
samples(g1,g4).I have the data generated from aroma.affymetrix and I
have applied log2 on it and load the data for doing ststistical
analysis by limma.But I dont understand whether to give coef=2or 1 in
the toptable command.When I give one it gives very little fold change
and when I give 1 its gives 14 fold change.
dat.m<-read.csv("datam.csv",skip=0)
genes<-rownames(dat.m)
groups<-c(2,2,1,1)
groups<-as.factor(groups)
design<-model.matrix(~groups)
design
fit<-lmFit(dat.m, design)
fit<-eBayes(fit)
toptable(fit, coef=2)
Thanks in advance,
Diya
On Aug 31, 8:26 am, Mathieu Parent <parent.math...@gmail.com> wrote:
> Salut !
>
> Here is the little function I wrote to run limma and save time. I'm a bad
> scripter, but this saves me a lot of time. Limma is very well documented.
> Here is a book chapter from Gordon Smith that covers it all:www.statsci.org/smyth/pubs/*limma*-biocbook-reprint.pdf
>
> *output_from_limma <- fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
I have a question.I have 2contral samples(v7,v8) and 2 treatment
samples(g1,g4).I have the data generated from aroma.affymetrix and I
have applied log2 on it and load the data for doing ststistical
analysis by limma.But I dont understand whether to give coef=2or 1 in
the toptable command.When I give one it gives very little fold change
and when I give 1 its gives 14 fold change.
dat.m<-read.csv("datam.csv",skip=0)
genes<-rownames(dat.m)
groups<-c(2,2,1,1)
groups<-as.factor(groups)
design<-model.matrix(~groups)
design
fit<-lmFit(dat.m, design)
fit<-eBayes(fit)
toptable(fit, coef=2)
Thanks in advance,
Diya
On Aug 31, 8:26 am, Mathieu Parent <parent.math...@gmail.com> wrote:
> Salut !
>
> Here is the little function I wrote to run limma and save time. I'm a bad
> scripter, but this saves me a lot of time. Limma is very well documented.
>
> Cheers !
>
> (load the function)
> The call for you would be...
>
> data <- matrix_from_limma_not_logged
> design <- cbind(c(1,1,0,0), c(0,0,1,1))
> colnames(design) <- c("Gr1", "Gr2")
> rownames(design) <- c("array1", "array2", "array3", "array4")
> contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1), levels=design)
> Pval <- 0.05
> FDR <- "fdr"
> LFC <- log2(2)*
> Cheers !
>
> (load the function)
> The call for you would be...
>
> data <- matrix_from_limma_not_logged
> design <- cbind(c(1,1,0,0), c(0,0,1,1))
> colnames(design) <- c("Gr1", "Gr2")
> rownames(design) <- c("array1", "array2", "array3", "array4")
> contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1), levels=design)
> Pval <- 0.05
> FDR <- "fdr"
>
> *output_from_limma <- fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
> rm(design, contrast, Pval, LFC, FDR)
>
> *##### Execute LIMMA to output matrix of significant genes
>
> ### ARGUMENTS
> ## fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
> # Data matrix - Output from Aroma, NOT LOGGED. rows=genes, cols=arrays
> # Design matrix for n array in 2 group (ie design <- cbind(c(1,1,0,0),
> c(0,0,1,1) )
> # Contrast matrix (ie contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1),
> levels=design) )
> # Pval (numeric, between 0 and 1) (ie Pval <- 0.05)
> # LFC (numeric, logged base 2 value of fold change) (ie LFC<- log2(2))
> # FDR correction (Caracter, '"none"', '"BH"', '"fdr"', '"BY"' or '"holm" )
> (ie FDR <- "fdr")
> *
> ## fun.LIMMA(data, design, contrast, Pval, LFC, FDR)
> # Data matrix - Output from Aroma, NOT LOGGED. rows=genes, cols=arrays
> # Design matrix for n array in 2 group (ie design <- cbind(c(1,1,0,0),
> c(0,0,1,1) )
> # Contrast matrix (ie contrast <- makeContrasts(Gr1VsGr2 = (Gr2-Gr1),
> levels=design) )
> # Pval (numeric, between 0 and 1) (ie Pval <- 0.05)
> # LFC (numeric, logged base 2 value of fold change) (ie LFC<- log2(2))
> # FDR correction (Caracter, '"none"', '"BH"', '"fdr"', '"BY"' or '"holm" )
> (ie FDR <- "fdr")
> On Fri, Aug 28, 2009 at 8:13 AM, Diya v <diya_2...@yahoo.co.in> wrote:
> > Hi
>
> > I have 2 control and 2 treatment groups of MoGene-1_0-st.I have the data
> > normalized and and a data matrix after fit(plm) is performed.
>
> > I want to do statistical analysis for differentially expressed ganes.
>
> > Can I take the datamatrix generated from aroma.affymetrix and do the
> > analysis with limma
>
> > Is there any online tutorial for this?
>
> > Thanks,
> > Diya
>
> > --- On *Fri, 28/8/09, Mathieu Parent <parent.math...@gmail.com>* wrote:
> > Hi
>
> > I have 2 control and 2 treatment groups of MoGene-1_0-st.I have the data
> > normalized and and a data matrix after fit(plm) is performed.
>
> > I want to do statistical analysis for differentially expressed ganes.
>
> > Can I take the datamatrix generated from aroma.affymetrix and do the
> > analysis with limma
>
> > Is there any online tutorial for this?
>
> > Thanks,
> > Diya
>
>
> > From: Mathieu Parent <parent.math...@gmail.com>
> > Subject: [aroma.affymetrix] Re: Discussion on gene-1-0-st-array-analysis
> > To: aroma-af...@googlegroups.com
> > Date: Friday, 28 August, 2009, 5:58 PM
>
> > Hi,
> > They way it has been proposed to me, is to extract the matrix from the
> > normalised and summarised data, log it and pass into the LIMMA package for
> > differential expression analysis.
>
> > What is your experimental design ?
>
> > Math
> > McGill University
>
> > On Thu, Aug 27, 2009 at 3:46 PM, Diya Vaka <biotechd...@gmail.com<http://mc/compose?to=biotechd...@gmail.com>
> > From: Mathieu Parent <parent.math...@gmail.com>
> > Subject: [aroma.affymetrix] Re: Discussion on gene-1-0-st-array-analysis
> > To: aroma-af...@googlegroups.com
> > Date: Friday, 28 August, 2009, 5:58 PM
>
> > Hi,
> > They way it has been proposed to me, is to extract the matrix from the
> > normalised and summarised data, log it and pass into the LIMMA package for
> > differential expression analysis.
>
> > What is your experimental design ?
>
> > Math
> > McGill University
>
> > > wrote:
>
> >> Hello All,
>
> >> I want to know about the up and down regulated genes.So how am i
> >> supposed to proceed after this step
>
> >> Diya
>
> > ------------------------------
>
> >> Hello All,
>
> >> I want to know about the up and down regulated genes.So how am i
> >> supposed to proceed after this step
>
> >> Diya
>
> > Love Cricket? Check out live scores, photos, video highlights and more. Click
> > here <http://in.rd.yahoo.com/tagline_cricket_2/*http://cricket.yahoo.com>.
Henrik Bengtsson
Sep 6, 2009, 9:03:11 PM9/6/09
to aroma-af...@googlegroups.com
Hi Diya,
at this stage of your analysis you're leaving the world of
aroma.affymetrix. Please use the Bioconductor mailing list for
questions about limma etc. You have a much better chance to get a
quick and clear answer there.
/Henrik
at this stage of your analysis you're leaving the world of
aroma.affymetrix. Please use the Bioconductor mailing list for
questions about limma etc. You have a much better chance to get a
quick and clear answer there.
/Henrik
Diya v
Sep 8, 2009, 4:35:57 PM9/8/09
to aroma-af...@googlegroups.com
| Thankyou --- On Mon, 7/9/09, Henrik Bengtsson <henrik.b...@gmail.com> wrote: |
|
Germán González
Dec 4, 2009, 8:33:10 AM12/4/09
to aroma.affymetrix
"There has been some recent work that suggests you can use the Gene
arrays to do splicing analysis."
Can you give me sites or any references about that?
thanks, german
arrays to do splicing analysis."
Can you give me sites or any references about that?
thanks, german
Mark Robinson
Dec 4, 2009, 3:42:54 PM12/4/09
to aroma-af...@googlegroups.com
Hi Germán.
Try this link:
http://www.biomedcentral.com/1471-2105/10/156
Cheers,
Mark
> --
> When reporting problems on aroma.affymetrix, make sure 1) to run the
> latest version of the package, 2) to report the output of
> sessionInfo() and traceback(), and 3) to post a complete code example.
>
>
> You received this message because you are subscribed to the Google
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Try this link:
http://www.biomedcentral.com/1471-2105/10/156
Cheers,
Mark
> When reporting problems on aroma.affymetrix, make sure 1) to run the
> latest version of the package, 2) to report the output of
> sessionInfo() and traceback(), and 3) to post a complete code example.
>
>
> You received this message because you are subscribed to the Google
> Groups "aroma.affymetrix" group.
> To post to this group, send email to aroma-af...@googlegroups.com
> To unsubscribe from this group, send email to aroma-affymetr...@googlegroups.com
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