https://support.bioconductor.org/p/45792/

The problem is not to do with the file format. The problem is almost certainly that you are trying to read data files from different GEO series that contain different numbers of rows (probes), and read.maimages() does not allow you to do that.

As the help page for read.maimages says, "All image analysis files being read are assumed to contain data for the same genelist in the same order."

Does it make sense to combine the different GEO series? Did they all use exactly the same Agilent array? If it does make sense, but the data files contain data for different sets of probe, then it is up to you read the series into R separately, then to make decisions about which probes can be matched up across series and which cannot.

Best wishes Gordon

-------------- original message ----------------- [BioC] File format for single channel analysis of Agilent microarray data with Limma? Parisa Razaz Parisa.Razaz at icr.ac.uk Sun May 27 20:09:13 CEST 2012

Hi Guido,

Thank you for getting back to me. I am also using data downloaded from GEO and have now incorporated your suggestion of "agilent.median" when using the read.maimages function. However the problem now appears to be with loading files from different series (when using the read.maimages function). Particular combinations of series work and others don't, with those that don't giving the same error message as before. I thought that this may be a size limit issue, but the combined number of samples for some of the series that don't work together is smaller at times than those that do. Do you have any idea why this might be and how I would get around it?

Thanks,

Parisa

From: Hooiveld, Guido [Guido.Hooiveld@wur.nl] Sent: 23 May 2012 16:52 To: bioconductor at r-project.org; Parisa Razaz Subject: RE: [BioC] File format for single channel analysis of Agilent microarray data with Limma?

Hi Parisa,

I also once struggled with reading in some Agilent singe channel arrays (that I downloaded from GEO; GSE27784), but for me these line of codes worked (in particularly note that the 2nd line is different than the one that is given on the website you linked to; specifically the statement source="agilent.median"):

HTH, Guido

targets <- readTargets("targets_GSE27784.txt", row.names="Name") e.raw <- read.maimages(targets$FileName, source="agilent.median", green.only=TRUE) Read GSM686624_251486829200_S01_GE1_105_Jan09_1_1.txt Read GSM686625_251486829201_S01_GE1_105_Jan09_1_2.txt Read GSM686626_251486829328_S01_GE1_105_Jan09_1_3.txt Read GSM686627_251486829200_S01_GE1_105_Jan09_1_2.txt Read GSM686628_251486829200_S01_GE1_105_Jan09_1_4.txt Read GSM686629_251486829201_S01_GE1_105_Jan09_1_4.txt Read GSM686630_251486829328_S01_GE1_105_Jan09_1_4.txt Read GSM686631_251486829328_S01_GE1_105_Jan09_1_1.txt Read GSM686632_251486829328_S01_GE1_105_Jan09_1_2.txt Read GSM686633_251486829200_S01_GE1_105_Jan09_1_3.txt Read GSM686634_251486829201_S01_GE1_105_Jan09_1_3.txt Read GSM686635_251486829201_S01_GE1_105_Jan09_1_1.txt

Background correction using normexp + offset

e.raw2 <- backgroundCorrect(e.raw, method="normexp", offset=50) Array 1 corrected Array 2 corrected Array 3 corrected Array 4 corrected Array 5 corrected Array 6 corrected Array 7 corrected Array 8 corrected Array 9 corrected Array 10 corrected Array 11 corrected Array 12 corrected

Perform quantile normalization

expr.data <- normalizeBetweenArrays(e.raw2, method="quantile")

Use the avereps function to average replicate spots.

E.avg <- avereps(expr.data, ID=expr.data$genes$ProbeName)

Alternatively, perform background correction using the negative

control probes + quantile normalization table(e.raw$genes$ControlType)

-1     0     1

153 43379 1486

bg.corr <- neqc(e.raw, status=e.raw$genes$ControlType, negctrl=-1, regular=0)

E.avg <- avereps(bg.corr, ID=bg.corr$genes$ProbeName)

Guido Hooiveld, PhD Nutrition, Metabolism & Genomics Group Division of Human Nutrition Wageningen University Biotechnion, Bomenweg 2 NL-6703 HD Wageningen the Netherlands tel: (+)31 317 485788 fax: (+)31 317 483342 email: guido.hooiveld at wur.nl internet: http://nutrigene.4t.com http://scholar.google.com/citations?user=qFHaMnoAAAAJ http://www.researcherid.com/rid/F-4912-2010

-----Original Message----- From: bioconductor-bounces@r-project.org [mailto:bioconductor-bounces at r-project.org] On Behalf Of Parisa [guest] Sent: Wednesday, May 23, 2012 15:51 To: bioconductor at r-project.org; parisa.razaz at icr.ac.uk Subject: [BioC] File format for single channel analysis of Agilent microarray data with Limma?

Hi,

I am following the protocol outlined here for analysis of single channel Agilent microarray data:

http://matticklab.com/index.php?title=Single_channel_analysis_of_Agile nt_microarray_data_with_Limma

I keep getting the following error message when using Limma's read.maimages function to load my data into an RGList object:

Error in RG[[a]][, i] <- obj[, columns[[a]]] : number of items to replace is not a multiple of replacement length

I think this may be due to my Agilent raw data txt files being in the wrong format. I am having difficulty finding an example Agilent feature extraction raw data txt file online to compare it to. A link to a screen shot of one of the files I am using is below. I would appreciate if someone could let me know if it is in the correct format, and if not then what format it should be in to prevent the above error message from coming up.

Thank you,

Parisa

http://www4.picturepush.com/photo/a/8322602/img/8322602.png

-- output of sessionInfo():

sessionInfo()R version 2.13.1 (2011-07-08) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)

locale: [1] en_GB.UTF-8/en_GB.UTF-8/C/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] limma_3.8.3

The information in this email is confidential and intend...{{dropped:4}} Microarray probe Microarray probe • 3.8k views ADD COMMENT • link updated 10.7 years ago by Parisa Razaz ▴ 40 • written 10.7 years ago by Gordon Smyth 47k 0 Parisa Razaz ▴ 40 @parisa-razaz-5306 Last seen 8.5 years ago Dear Gordon,

Great, thank you. I have gone through my files and pulled out the ones where the gene list differs - and now it works.

I need a large data set of colon and breast tumour data, which is why I am combining the different GEO series. The series are all on the Agilent 4x44K whole human genome platform, so hopefully combining them should be OK?

Thanks,

Parisa

On 28 May 2012, at 10:51, Gordon K Smyth wrote:

Dear Parisa,

The problem is not to do with the file format. The problem is almost certainly that you are trying to read data files from different GEO series that contain different numbers of rows (probes), and read.maimages() does not allow you to do that.

As the help page for read.maimages says, "All image analysis files being read are assumed to contain data for the same genelist in the same order."

Does it make sense to combine the different GEO series? Did they all use exactly the same Agilent array? If it does make sense, but the data files contain data for different sets of probe, then it is up to you read the series into R separately, then to make decisions about which probes can be matched up across series and which cannot.

Best wishes Gordon

-------------- original message ----------------- [BioC] File format for single channel analysis of Agilent microarray data with Limma? Parisa Razaz Parisa.Razaz at icr.ac.uk Sun May 27 20:09:13 CEST 2012

Hi Guido,

Thank you for getting back to me. I am also using data downloaded from GEO and have now incorporated your suggestion of "agilent.median" when using the read.maimages function. However the problem now appears to be with loading files from different series (when using the read.maimages function). Particular combinations of series work and others don't, with those that don't giving the same error message as before. I thought that this may be a size limit issue, but the combined number of samples for some of the series that don't work together is smaller at times than those that do. Do you have any idea why this might be and how I would get around it?

Thanks,

Parisa

---

From: Hooiveld, Guido [Guido.Hooiveld at wur.nl] Sent: 23 May 2012 16:52 To: bioconductor at r-project.org; Parisa Razaz Subject: RE: [BioC] File format for single channel analysis of Agilent microarray data with Limma?

Hi Parisa,

I also once struggled with reading in some Agilent singe channel arrays (that I downloaded from GEO; GSE27784), but for me these line of codes worked (in particularly note that the 2nd line is different than the one that is given on the website you linked to; specifically the statement source="agilent.median"):

HTH, Guido

targets <- readTargets("targets_GSE27784.txt", row.names="Name") e.raw <- read.maimages(targets$FileName, source="agilent.median", green.only=TRUE) Read GSM686624_251486829200_S01_GE1_105_Jan09_1_1.txt Read GSM686625_251486829201_S01_GE1_105_Jan09_1_2.txt Read GSM686626_251486829328_S01_GE1_105_Jan09_1_3.txt Read GSM686627_251486829200_S01_GE1_105_Jan09_1_2.txt Read GSM686628_251486829200_S01_GE1_105_Jan09_1_4.txt Read GSM686629_251486829201_S01_GE1_105_Jan09_1_4.txt Read GSM686630_251486829328_S01_GE1_105_Jan09_1_4.txt Read GSM686631_251486829328_S01_GE1_105_Jan09_1_1.txt Read GSM686632_251486829328_S01_GE1_105_Jan09_1_2.txt Read GSM686633_251486829200_S01_GE1_105_Jan09_1_3.txt Read GSM686634_251486829201_S01_GE1_105_Jan09_1_3.txt Read GSM686635_251486829201_S01_GE1_105_Jan09_1_1.txt

Background correction using normexp + offset

e.raw2 <- backgroundCorrect(e.raw, method="normexp", offset=50) Array 1 corrected Array 2 corrected Array 3 corrected Array 4 corrected Array 5 corrected Array 6 corrected Array 7 corrected Array 8 corrected Array 9 corrected Array 10 corrected Array 11 corrected Array 12 corrected

Perform quantile normalization

expr.data <- normalizeBetweenArrays(e.raw2, method="quantile")

Use the avereps function to average replicate spots.

E.avg <- avereps(expr.data, ID=expr.data$genes$ProbeName)

Alternatively, perform background correction using the negative

control probes + quantile normalization table(e.raw$genes$ControlType)

-1 0 1 153 43379 1486

bg.corr <- neqc(e.raw, status=e.raw$genes$ControlType, negctrl=-1, regular=0)

E.avg <- avereps(bg.corr, ID=bg.corr$genes$ProbeName)

---

Guido Hooiveld, PhD Nutrition, Metabolism & Genomics Group Division of Human Nutrition Wageningen University Biotechnion, Bomenweg 2 NL-6703 HD Wageningen the Netherlands tel: (+)31 317 485788 fax: (+)31 317 483342 email: guido.hooiveld at wur.nl internet: http://nutrigene.4t.com http://scholar.google.com/citations?user=qFHaMnoAAAAJ http://www.researcherid.com/rid/F-4912-2010

-----Original Message----- From: bioconductor-bounces at r-project.org [mailto:bioconductor- bounces at r-project.org] On Behalf Of Parisa [guest] Sent: Wednesday, May 23, 2012 15:51 To: bioconductor at r-project.org; parisa.razaz at icr.ac.uk Subject: [BioC] File format for single channel analysis of Agilent microarray data with Limma?

Hi,

I am following the protocol outlined here for analysis of single channel Agilent microarray data:

http://matticklab.com/index.php?title=Single_channel_analysis_of_Agi lent_microarray_data_with_Limma

I keep getting the following error message when using Limma's read.maimages function to load my data into an RGList object:

Error in RG[[a]][, i] <- obj[, columns[[a]]] : number of items to replace is not a multiple of replacement length

I think this may be due to my Agilent raw data txt files being in the wrong format. I am having difficulty finding an example Agilent feature extraction raw data txt file online to compare it to. A link to a screen shot of one of the files I am using is below. I would appreciate if someone could let me know if it is in the correct format, and if not then what format it should be in to prevent the above error message from coming up.

Thank you,

Parisa

http://www4.picturepush.com/photo/a/8322602/img/8322602.png

-- output of sessionInfo():

sessionInfo()R version 2.13.1 (2011-07-08) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)

locale: [1] en_GB.UTF-8/en_GB.UTF-8/C/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] limma_3.8.3

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