Question: Normalize data across platforms
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gravatar for Steve Taylor
10.1 years ago by
Steve Taylor280
Steve Taylor280 wrote:
Hi, I have two sets of affy data CEL files. One set is from Hugene 1.0 ST arrays and the other from U133plus2 Arrays. I need to compare one set with another. First I plan to use RMA to normalise the data set for each platform. I then plan to get a common reference id across the arrays, probably using ENSEMBL gene ID. With the subset that have probes in common, what would be the best way to normalise across the arrays? Would quantile normalization using aroma.light be suitable? Thanks for any advice, Steve ------------------------------------------------------------------ Medical Sciences Division Weatherall Institute of Molecular Medicine/Sir William Dunn School Oxford University
normalization affy • 368 views
ADD COMMENTlink modified 10.1 years ago by michael watson IAH-C3.4k • written 10.1 years ago by Steve Taylor280
Answer: Normalize data across platforms
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gravatar for michael watson IAH-C
10.1 years ago by
michael watson IAH-C3.4k wrote:
Hi Steve Quantile normalisation is a very conservative normalisation, and there are fears in some quarters that it may lead to over-normalisation. However, as you're comparing between datasets, this may be what you're after. Alternatively, have you considered using "housekeeping" or control genes? These should be constant across arrays, experiments etc (if you believe in them) and so could provide a good normalisation factor. Mick -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch on behalf of Steve Taylor Sent: Tue 31/03/2009 12:44 PM To: Bioconductor Subject: [BioC] Normalize data across platforms Hi, I have two sets of affy data CEL files. One set is from Hugene 1.0 ST arrays and the other from U133plus2 Arrays. I need to compare one set with another. First I plan to use RMA to normalise the data set for each platform. I then plan to get a common reference id across the arrays, probably using ENSEMBL gene ID. With the subset that have probes in common, what would be the best way to normalise across the arrays? Would quantile normalization using aroma.light be suitable? Thanks for any advice, Steve ------------------------------------------------------------------ Medical Sciences Division Weatherall Institute of Molecular Medicine/Sir William Dunn School Oxford University _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD COMMENTlink written 10.1 years ago by michael watson IAH-C3.4k
Hi, Thanks for your reply. > > Quantile normalisation is a very conservative normalisation, and there are fears in some quarters that it may lead to over- normalisation. However, as you're comparing between datasets, this may be what you're after. > I agree. I am just looking for large differences so I am hoping it might be ok. > Alternatively, have you considered using "housekeeping" or control genes? These should be constant across arrays, experiments etc (if you believe in them) and so could provide a good normalisation factor. > Does anyone know if affy has control probes that are consistent across platforms? Kind regards and thanks, Steve ------------------------------------------------------------------ Medical Sciences Division Weatherall Institute of Molecular Medicine/Sir William Dunn School Oxford University
ADD REPLYlink written 10.1 years ago by Steve Taylor280
Maybe this will help you regarding control probes across platforms --> https://aberdeen.ac.uk/ims/facilities/microarray/documents/chipcontrol s.doc Regards Priscila -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor- bounces@stat.math.ethz.ch] On Behalf Of Steve Taylor Sent: Tuesday, March 31, 2009 7:46 AM To: michael watson (IAH-C) Cc: Bioconductor Subject: Re: [BioC] Normalize data across platforms Hi, Thanks for your reply. > > Quantile normalisation is a very conservative normalisation, and there are fears in some quarters that it may lead to over- normalisation. However, as you're comparing between datasets, this may be what you're after. > I agree. I am just looking for large differences so I am hoping it might be ok. > Alternatively, have you considered using "housekeeping" or control genes? These should be constant across arrays, experiments etc (if you believe in them) and so could provide a good normalisation factor. > Does anyone know if affy has control probes that are consistent across platforms? Kind regards and thanks, Steve ------------------------------------------------------------------ Medical Sciences Division Weatherall Institute of Molecular Medicine/Sir William Dunn School Oxford University _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD REPLYlink written 10.1 years ago by Priscila Darakjian40
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