How to import normalized file (using xps) to limma for differential expressed genes?
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Biologist ▴ 90
@biologist-9801
Last seen 18 months ago

Hi Christian,

For HuGene20stv1 array I have used xps package:

library(xps)

input <- choose.dir("D:/GeneST_Analyse/ProjectName")
setwd(input)
print(dir(input))

#Subdirectories

scmdir <- paste(input,"schemes",sep="/")
libdir <- paste(input,"library",sep="/")
anndir <- paste(input,"Annotation",sep="/")
celdir <- paste(input,"CELfiles",sep="/")
rootdir <- paste(input,"rootdata",sep="/")

scheme.exon <- import.exon.scheme("Scheme",filedir=scmdir,layoutfile=paste(libdir,"HuGene-2_0-st.clf",sep="/"),schemefile=paste(libdir,"HuGene-2_0-st.pgf",sep="/"),probeset=paste(anndir,"HuGene-2_0-st-v1.na35.hg19.probeset.csv",sep="/"),transcript=paste(anndir,"HuGene-2_0-st-v1.na35.hg19.transcript.csv",sep="/"),verbose=TRUE)

 

celfiles <- dir(path = "D:/GeneST_Analyse/ProjectName/CELfiles", pattern = "*.CEL$", all.files = FALSE, full.names = FALSE, recursive = FALSE, ignore.case = FALSE)
data.exon <- import.data(scheme.exon,"tmp_data_exon",filedir=rootdir,celfiles=celfiles,celdir=celdir,verbose=FALSE)

unlist(treeNames(data.exon))

Normalization:

data.rma <- rma(data.exon, "tmp_exonRMA", filedir=rootdir, verbose=FALSE, exonlevel="affx+core", option="transcript", background="antigenomic")

Now, how to use limma with this data to get differential expressed genes?

Thank you

 

 

xps heatmap limma • 1.6k views
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@james-w-macdonald-5106
Last seen 11 hours ago
United States

This site is primarily intended to help people with specific questions about how to use Bioconductor software rather than a tutorial site where people can ask open-ended questions and get free tutorials on how to do a data analysis. If you want to learn how to analyze data using limma, you should read the limma User's Guide.

 

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cstrato ★ 3.9k
@cstrato-908
Last seen 3.0 years ago
Austria

Dear Venkatesh,

The following should give you the expression values with the UnitNames:
> expr.rma <- validData(data.rma)

Furthermore, you could use function 'export', see '?export'
This will give you the annotation data you want, e.g. gene name, gene symbol, etc, e.g.:

This will export the data and create a data.frame:
> expr.rma <- export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fSymbol:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = TRUE, verbose = TRUE)

or to export the data only as text file:
> export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fSymbol:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = FALSE, verbose = TRUE)


Since the results of  function rma() are linear values you need to convert them fist to log2 values:
> tmp <- as.matrix(log2(expr.rma[,'columns to convert to log2]))

 

For using limma, here is an example from my examples in xps/examples/script4bestmatch.R.
This is w/o guarantee! You need to adjust it to your own needs.

> library(limma)

## create design matrix
> tissue <- c("Breast","Breast","Breast","Prostate","Prostate","Prostate") 
> design <- model.matrix(~factor(tissue)) 
> colnames(design) <- c("Breast","BreastvsProstate") 
> design

> fit <- lmFit(tmp, design) 
> fit <- eBayes(fit) 
> xpsu.lm <- topTable(fit, coef=2, n=length(rownames(tmp)), adjust="BH")
> xpsu.lm <- xpsu.lm[order(xpsu.lm[,"ID"]),c("ID","logFC","P.Value","adj.P.Val")]
> colnames(xpsu.lm) <- c("xpsu.ID","xpsu.logFC","xpsu.P.Value","xpsu.adj.P.Val")


Maybe, you need ot create a contrst matrix, e.g.:
> cont.matrix <- makeContrasts(........,
                             levels = design
                             )
> fit2 <- contrasts.fit(fit,cont.matrix)
> fit3 <- eBayes(fit2)


For further questions regarding limma please read the  user guide and ask the experts.

Best regards,
Christian 

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Thanks for the reply Christian. Yes I have used in the same way before but its is giving all the genes which are present insted of differential expressed genes. Please check the following :

expr.rma <- export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fSymbol:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = TRUE, verbose = TRUE)

tmp <- as.matrix(log2(expr.rma[,4:9]))


Group <- c("GrpA","GrpA","GrpA","GrpB","GrpB","GrpB") 
design <- model.matrix(~factor(Group)) 
colnames(design) <- c("GrpA","GrpAvsGrpB")
design

fit <- lmFit(tmp, design)
cont.matrix <- makeContrasts(GrpAvsGrpB, levels = design) 
fit2 <- contrasts.fit(fit,cont.matrix)
fit3 <- eBayes(fit2) 
tab <- topTable(fit3, coef=1, n=Inf, adjust="fdr", sort.by="none")
write.table(tab, file="DEG.xls",row.names=F, sep="\t")

expr.rma has 44796 genes and DEG.xls also have 44796 genes. 

.......And Apart from Limma I used PreFilter and UniFilter which is mentioned in xps.pdf

prefltr <- PreFilter(mad=c(0.5), lothreshold=c(7.0,0.02,"mean"), hithreshold=c(10.5,80.0,"percent"))

rma.pfr <- prefilter(data.rma, "tmpdt_exonPrefilter", filedir=rootdir , filter=prefltr, minfilters=2, verbose=FALSE)

tmp <- validData(rma.pfr)
head(tmp)

dim(tmp[tmp[,"FLAG"]==1,])

The data show that 5295 genes of the 44796 genes on the GeneChip are selected for further analysis.

unifltr <- UniFilter(foldchange=c(1.3,"both"), unifilter=c(0.1,"pval"))

rma.ufr <- unifilter(data.rma, "tmpdt_exonUnifilter", filedir=rootdir , unifltr, group=c("GrpA","GrpA","GrpA","GrpB","GrpB","GrpB"), xps.fltr=rma.pfr, verbose=FALSE)

tmp <- validData(rma.ufr)
tmp
dim(tmp)

The data show that only 767 genes of the pre{selected 5295 genes are considered to be differentially expressed.

But I need a heatmap So I also tried using limma. But Dont know where I went wrong. Please help me out.

Thank you

 

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cstrato ★ 3.9k
@cstrato-908
Last seen 3.0 years ago
Austria

Please read my first reply again:

Furthermore, you could use function 'export', see '?export'
This will give you the annotation data you want, e.g. gene name, gene symbol, etc, e.g.:

This will export the data and create a data.frame:
> expr.rma <- export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fSymbol:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = TRUE, verbose = TRUE)

or to export the data only as text file:
> export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fSymbol:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = FALSE, verbose = TRUE)


This will give you the annotation for the unitnumbers.

Regards,
Christian

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Hi Christian,

I have tried that before and this is what I GOT

> expr.rma <- export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fSymbol:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = TRUE, verbose = TRUE)
> head(expr.rma)
  UNIT_ID UnitName GeneSymbol X_8__HuGene_2_0_st_.mdp_LEVEL
1       0 17127159       <NA>                     353.56800
2       1 17127161       <NA>                      11.08420
3       2 17127163       <NA>                     221.64700
4       3 17127165       <NA>                       5.02182
5       4 17127167       <NA>                     393.91500
6       5 17127169       <NA>                       7.38325
  E__HuGene_2_0_st_.mdp_LEVEL GG7__HuGene_2_0_st_.mdp_LEVEL
1                   335.29500                     221.71700
2                     7.01459                       7.33511
3                   226.77400                     136.27400
4                     3.41172                       4.12834
5                   433.67100                     228.32800
6                     6.05120                       4.32289
  J__HuGene_2_0_st_.mdp_LEVEL O__HuGene_2_0_st_.mdp_LEVEL
1                   815.65400                   684.85000
2                    11.21210                    12.62680
3                   431.32000                   392.53300
4                     6.90306                     4.91183
5                  1083.58000                   695.28500
6                     6.09091                     4.60186
  T__HuGene_2_0_st_.mdp_LEVEL
1                   803.73600
2                    10.36830
3                   704.74300
4                     5.65681
5                   830.41600
6                     5.88536

 

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I would suggest that you open the original Affymetrix file 'HuGene-2_0-st-v1.na35.hg19.transcript.csv' in 
order to get a feeling for the annotation data. You will see that e.g. UnitName=17127159 has no gene symbol, 
since it is an 'control->affx' probeset. 
Please note that only 'UnitName', i.e. the transcript_cluster_id, is unique.

Did you open the outfile = "MyName.txt" to see how it looks like?

I would suggest that you use 'varlist = "*"' in order to see what data are exported. Furthermore, please
read the help '?export.expr' carefully.

(FYI, when typing 'help.start() in R then your browser will open and 'xps' will be listed in 'Packages'.
Then you can read the help for all 'xps' functions in your browser.)

Regards,
Christian

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As you said I have given in the following way.

export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fTranscriptID:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = FALSE, verbose = TRUE)

I want to check which probeset belongs to which transcript so I gave fTranscriptID.

UNIT_ID    UnitName    TranscriptID    X_8      E            GG7          J            O            T

3742    16657437    16657436    2.10763    2.10763    1.32498    4.14518    9.77053    13.9962
3743    16657438    16657436    2.54837    1.92386    3.63425    2.26163    3.24937    2.79736
3744    16657439    16657436    11.3137    7.27181    10.0098    10.0176    15.2267    6.35405
3745    16657441    16657440    4.19212    6.30431    2.10763    1.06896    1.43262    1.74003
3746    16657442    16657440    2.57503    1.89032    1.87688    4.1086    3.58509    2.44511
3747    16657443    16657440    41.9018    22.0025    23.6494    10.6459    29.4596    13.7648
3748    16657444    16657440    10.2051    12.3036    13.1857    6.5307    18.2471    7.20289
3749    16657446    16657445    1.6846    1.40303    1.44591    2.18139    2.3705    5.62349

It gave the Transcript ID but for fUnitName why it is not probesetID? 

 

 

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In this case fUnitName is the probesetID as you can easily verify by looking at the Affymetrix probeset 
annotation file 'HuGene-2_0-st-v1.na35.hg19.probeset.csv'

Regards,
Christian

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cstrato ★ 3.9k
@cstrato-908
Last seen 3.0 years ago
Austria

Please read the help '?topTable' carefully. It says:
'By default number probes are listed. Alternatively, by specifying p.value and number=Inf, 
all genes with adjusted p-values below a specified value can be listed.'

Thus, it is up to you which p-value you want to use as cutoff.

Best regards,
Christian 

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Ok. Thank you 

And Apart from Limma I used PreFilter and UniFilter which is mentioned in xps.pdf

prefltr <- PreFilter(mad=c(0.5), lothreshold=c(7.0,0.02,"mean"), hithreshold=c(10.5,80.0,"percent"))

rma.pfr <- prefilter(data.rma, "tmpdt_exonPrefilter", filedir=rootdir , filter=prefltr, minfilters=2, verbose=FALSE)

tmp <- validData(rma.pfr)
head(tmp)

dim(tmp[tmp[,"FLAG"]==1,])

The data show that 5295 genes of the 44796 genes on the GeneChip are selected for further analysis.

unifltr <- UniFilter(foldchange=c(1.3,"both"), unifilter=c(0.1,"pval"))

rma.ufr <- unifilter(data.rma, "tmpdt_exonUnifilter", filedir=rootdir , unifltr, group=c("GrpA","GrpA","GrpA","GrpB","GrpB","GrpB"), xps.fltr=rma.pfr, verbose=FALSE)

tmp <- validData(rma.ufr)
tmp
dim(tmp)

The data show that only 767 genes of the pre{selected 5295 genes are considered to be differentially expressed. Is generating heatmap with this possible?

But I need a heatmap So I also tried using limma. But Dont know where I went wrong. Please help me out.

Thank you

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cstrato ★ 3.9k
@cstrato-908
Last seen 3.0 years ago
Austria

For 767 genes it is possible to create a heatmap and/or to do cluster analysis.

Basic R has functions 'hclust()', 'heatmap()', 'image()' for these purposes.
Furthermore, I am sure that Bioconductor has packages for these  purposes, too.

Finally, limma has its own function 'heatDiagram()'.

Please note that these questions have nothing to do with 'xps'.

Best regards,
Christian 

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Hi christian,

To get a heatmap I have used in the following way.

Group <- c("GrpA","GrpA","GrpA","GrpB","GrpB","GrpB") 
design <- model.matrix(~factor(Group)) 
colnames(design) <- c("GrpA","GrpAvsGrpB") 
design

fit <- lmFit(tmp, design)
cont.matrix <- makeContrasts(GrpAvsGrpB, levels = design) 
fit2 <- contrasts.fit(fit,cont.matrix)
fit3 <- eBayes(fit2) 
tab <- topTable(fit3, coef=1, n=Inf, adjust="fdr", sort.by="none")
idx = which(tab$adj.P.Val < 0.05)

library(gplots)
heatmap.2(tmp[idx,],trace='none',scale='row')

But this gives the following error.

Error:

Error in heatmap.2(tmp[idx, ], trace = "none", scale = "row") :

'x' must have at least 2 rows and 2 columns

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Actually, Dear ghk, just two comments above for your code before the actual heatmap function (perhaps you wrote it in hurry, but is a bit confused):

in the start you use a design matrix with an intercept: 

design <- model.matrix(~factor(Group)) 

and then you arbitary use makeContrasts, which does not make sense, as you have already defined your contrast GrpAvsGrpB before lmFit.  Thus, you should have continued directly to eBayes step.

Moreover, your definition above in the columns of design matrix (GrpAvsGrpB) is not correct:

> Group <- c("GrpA","GrpA","GrpA","GrpB","GrpB","GrpB") 
> design <- model.matrix(~factor(Group)) 
> design
  (Intercept) factor(Group)GrpB
1           1                 0
2           1                 0
3           1                 0
4           1                 1
5           1                 1
6           1                 1

So, your second coefficient is the difference of GrpB vs the GrpA, not the inverse.

Best,

Efstathios

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Hi Efstathios,

Thanks for the reply. Please check the following once.

design <- model.matrix(~ -1+factor(c(1,1,2,2,3,3)))

colnames(design) <- c("group1", "group2", "group3")
fit <- lmFit(celfiles.rma, design)

contrast.matrix <- makeContrasts(group2-group1, group3-group2, group3-group1, levels=design)

fit2 <- contrasts.fit(fit, contrast.matrix)

fit2 <- eBayes(fit2)

tab <- topTable(fit2, coef=1, adjust="fdr", sort.by="B", number=Inf)

head(tab)
             logFC   AveExpr          t      P.Value adj.P.Val         B
17100820  3.813465 10.084444  13.990013 1.477686e-05 0.3544622 -4.142070
17100824  3.813465 10.084444  13.990013 1.477686e-05 0.3544622 -4.142070
16744689 -3.037688  1.577746 -13.252989 1.983301e-05 0.3544622 -4.143913
16842465 -6.954801  4.096664 -11.968499 3.444300e-05 0.4616826 -4.147931
16843156  3.222599  2.013231  11.375102 4.530001e-05 0.4857702 -4.150236
16706416 -2.014341  2.072348  -8.395743 2.270946e-04 1.0000000 -4.169346

idx = which(tab$P.Val < 0.9)

heatmap.2(tab[idx,],trace='none',scale='row')


Error in heatmap.2(tab[idx, ], trace = "none", scale = "row") : 
  `x' must be a numeric matrix 

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Dear gnk,

i'm no certain what tab is ? if im correct, it must be your eSet; if so, this explains the Error above--try:

heatmap.2(exprs(tab)[idx,],trace='none',scale='row')

This should now work

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Yes I tried but it gave another error now.

heatmap.2(exprs(tab)[idx,],trace='none',scale='row')

Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘exprs’ for signature ‘"data.frame"’

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So, tab isn't your eSet ? you should use your expressionSet container object with the above function to work-anyway, heatmap.2 expects a numeric matrix to work, that's why it complains :)

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Could you please check this once and tell me where I went wrong.

setwd("D:/Chaduvulu/ViroScan3D/GeneST_Analyse/CELFiles")
library(oligo)
celfiles = list.files(path = ".", pattern = ".CEL$", all.files = FALSE,
                      full.names = FALSE, recursive = FALSE, ignore.case = FALSE)

celfiles
[1] "#8_(HuGene-2_0-st).CEL"  "E_(HuGene-2_0-st).CEL"   "GG7_(HuGene-2_0-st).CEL"
[4] "J_(HuGene-2_0-st).CEL"   "O_(HuGene-2_0-st).CEL"   "T_(HuGene-2_0-st).CEL"

Data <- read.celfiles(celfiles)
celfiles.rma <- rma(Data, target="core")

library(limma)

design <- model.matrix(~ -1+factor(c(1,1,2,2,3,3)))

colnames(design) <- c("group1", "group2", "group3")
fit <- lmFit(celfiles.rma, design)

contrast.matrix <- makeContrasts(group2-group1, group3-group2, group3-group1, levels=design)

fit2 <- contrasts.fit(fit, contrast.matrix)

fit2 <- eBayes(fit2)

tab <- topTable(fit2, coef=1, adjust="fdr", sort.by="none", number=Inf)
write.table(tab, file="DEG2.xls",row.names=F, sep="\t")

idx = which(tab$P.Val < 0.9)
heatmap.2(exprs(tab)[idx,],trace='none',scale='row')

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You should use:

heatmap.2(exprs(celfiles.rma)[idx,],trace='none',scale='row') # topTable output has nothing relevant here
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Hi christian,

Using "xps" package

data.rma <- rma(data.exon, "tmp_exonRMA", filedir=rootdir, verbose=FALSE, exonlevel="affx+core", option="transcript", background="antigenomic")

expr.rma <- export.expr(data.rma, treename = "*", treetype = "mdp", varlist = "fUnitName:fSymbol:fLevel", outfile = "MyName.txt", sep = "\t", as.dataframe = TRUE, verbose = TRUE)

tmp <- as.matrix(log2(expr.rma[,4:9]))

library(limma)

design <- model.matrix(~ 0+factor(c(1,1,1,2,2,2)))

colnames(design) <- c("group1", "group2")
fit <- lmFit(tmp, design)

contrast.matrix <- makeContrasts(group2-group1, group1-group2, levels=design)

fit2 <- contrasts.fit(fit, contrast.matrix)

fit2 <- eBayes(fit2)

tab <- topTable(fit2, coef=2, adjust="fdr", sort.by="none", number=Inf)
write.table(tab, file="DEG.xls",row.names=F, sep="\t")

results <- tab[which(tab$logFC >= 1.5 & tab$P.Value <= 0.05),]
write.table(results, file="DEGp.xls", row.names=T, sep="\t")

idx = which(tab$P.Value < 0.05 & tab$logFC > 1.5)

heatmap(tmp[idx],trace='none',scale='row')
Error in heatmap(tmp[idx], trace = "none", scale = "row") : 
  'x' must be a numeric matrix

Could you please help me in this?

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Dear ghk,

you missed a comma above--you should use again:

heatmap(tmp[idx,],trace='none',scale='row') # (i hope you do not feed the troll :))

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Oh ya I didnt see that. Very very sorry. its my fault. Thank you very much @svlachavas

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And a small qtn. How can I add Annotation to this; I gat the Unitnumbers instead of gene names.

head(tab)
               logFC   AveExpr           t    P.Value adj.P.Val         B
16650001 -0.67713091 0.7013795 -2.11246203 0.07463104 0.8414740 -3.923065
16650003  0.01775323 0.8173575  0.05752413 0.95581904 0.9922585 -5.247542
16650005  0.20727356 1.2926154  0.28654513 0.78318782 0.9636612 -5.214740
16650007  0.13198594 0.8028477  0.37395467 0.72008320 0.9502862 -5.191016
16650009 -0.31117759 0.7187522 -0.92432176 0.38765340 0.8815813 -4.917247
16650011 -0.18142907 0.7450577 -0.46483921 0.65689248 0.9382330 -5.160082

 

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When I run the above code I get the logFoldchange values but I need the linear FoldChange values. So how to do that? Any idea? I want to keep P.Value <= 0.05 and FoldChange >= 1.5 Looking forward to your response.

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@svlachavas Could you please hep me to get heatmap of differential expressed exons. I'm working on Human Gene 2.0 ST array. I have used "xps" package for the analysis.

scheme.exon <- import.exon.scheme("Scheme",filedir=scmdir,layoutfile=paste(libdir,"HuGene-2_0-st.clf",sep="/"),schemefile=paste(libdir,"HuGene-2_0-st.pgf",sep="/"),probeset=paste(anndir,"HuGene-2_0-st-v1.na35.hg19.probeset.csv",sep="/"),transcript=paste(anndir,"HuGene-2_0-st-v1.na35.hg19.transcript.csv",sep="/"),verbose=TRUE)

##Reading CEL Info
#Import CEL-files by giving the path where all .CEL files are present

celfiles <- dir(path = "D:/GeneST_Analyse/ProjectName/CELfiles", pattern = "*.CEL$", all.files = FALSE, full.names = FALSE, recursive = FALSE, ignore.case = FALSE)
data.exon <- import.data(scheme.exon,"tmp_data_exon",filedir=rootdir,celfiles=celfiles,celdir=celdir,verbose=FALSE)

unlist(treeNames(data.exon))

[1] "X_8__HuGene_2_0_st_.cel" "E__HuGene_2_0_st_.cel"  
[3] "GG7__HuGene_2_0_st_.cel" "J__HuGene_2_0_st_.cel"  
[5] "O__HuGene_2_0_st_.cel"   "T__HuGene_2_0_st_.cel"

data.rma <- rma(data.exon, "tmp_exonRMA", filedir=rootdir, verbose=FALSE, exonlevel="affx+core", option="transcript", background="antigenomic")

>data.rma

UNIT_ID   UN       X       E       GG7     J     O
0      17127159 353.568 335.295 221.717 815.654 684.85
1      17127161 11.0842 7.01459 7.33511 11.2121 12.6268
2      17127163 221.647 226.774 136.274 431.32  392.533
3      17127165 5.02182 3.41172 4.12834 6.90306 4.91183

unifltr <- UniFilter(foldchange=c(1.5,"both"), unifilter=c(0.05,"pval"))

rma.ufr <- unifilter(data.rma, "tmp_exonUnifilter", filedir=rootdir , filter=unifltr, group=c("GrpA","GrpA","GrpA","GrpB","GrpB","GrpB"), xps.fltr=rma.pfr, verbose=FALSE)

tmp2 <- validData(rma.ufr)
tmp2 has differential expressed genes.

UN          ID    St      M1    M2       SE    DOF  PV        PA            FC
17127159    0   -5.9    297.3   765.7   0.22    4   0.003   0.00389231  2.57536
17127163    2   -3.87   189.914 492.307 0.3548  4   0.0179  0.01795     2.59226
17127167    4   -3.8908 339.136 855.276 0.3429  4   0.0176  0.017       2.52192
17127171    6   -3.922  390.44  986.365 0.340   4   0.0172179   0.01721 2.52627
17127175    8   -4.715  536.072 1210.65 0.2492  4   0.00920158  0.00920 2.258

How can I get a heatmap for this?

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