Normalization quality
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alex lam RI ▴ 310
@alex-lam-ri-1491
Last seen 7.7 years ago
Dear BioCers, Hi! I don't have much of experience in handling affy chips and I hope someone can help me here. I loaded 276 affy CEL files using justGCRMA - my computer couldn't cope going the readAffy route. Quantile normalization was done. I have noticed that for some probesets there are some strange results. For example: > summary(exprs(eset.norm.quantile)["203329_at",]) Min. 1st Qu. Median Mean 3rd Qu. Max. 2.486 2.513 2.523 2.545 2.534 5.201 This gene seems to be very lowly expressed for everyone except for 1 chip. I did a boxplot of that chip against a few others after normalization and the overall distributions are similar. (1) As I don't have an AffyBatch object, is there a way to make image plots? And what other methods are there to catch these odd ones? (2) I was provided summarised expression values from an external group and they used MAS5 for pre-processing. For the same gene, the expression summary is: > summary(mas.expr[,"X203329_at"]) # I received a csv file, hence it's a data frame Min. 1st Qu. Median Mean 3rd Qu. Max. 2.926 5.337 5.950 5.880 6.403 9.183 The variation seems to be much greater, and it looks much more interesting. Can I expect such difference? Is it just because of gcrma works in the natural log scale or is there something wrong with my normalization? Many thanks for your help. Best wishes, Alex ------------------------------------ Alex Lam PhD student Department of Genetics and Genomics Roslin Institute (Edinburgh) Roslin Midlothian EH25 9PS Phone +44 131 5274471 Web http://www.roslin.ac.uk
Genetics Normalization affy Genetics Normalization affy • 572 views
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@sean-davis-490
Last seen 1 day ago
United States
On Thursday 05 October 2006 06:22, alex lam (RI) wrote: > Dear BioCers, > > Hi! I don't have much of experience in handling affy chips and I hope > someone can help me here. > > I loaded 276 affy CEL files using justGCRMA - my computer couldn't cope > going the readAffy route. > Quantile normalization was done. > > I have noticed that for some probesets there are some strange results. > > For example: > > summary(exprs(eset.norm.quantile)["203329_at",]) > > Min. 1st Qu. Median Mean 3rd Qu. Max. > 2.486 2.513 2.523 2.545 2.534 5.201 > > This gene seems to be very lowly expressed for everyone except for 1 > chip. I did a boxplot of that chip against a few others after > normalization and the overall distributions are similar. I would look at the quality metrics for the arrays as a separate issue from normalization. If you have 276 arrays, there will likely be an "outlier" for at least one array FOR MANY GENES. > (1) As I don't have an AffyBatch object, is there a way to make image > plots? And what other methods are there to catch these odd ones? > > (2) I was provided summarised expression values from an external group > and they used MAS5 for pre-processing. For the same gene, the expression > > summary is: > > summary(mas.expr[,"X203329_at"]) # I received a csv file, hence it's a > > data frame > Min. 1st Qu. Median Mean 3rd Qu. Max. > 2.926 5.337 5.950 5.880 6.403 9.183 > The variation seems to be much greater, and it looks much more > interesting. I think that for most purposes, most people would agree that MAS5 is inferior to RMA or GCRMA, but such a statement is a dangerous one to make without knowing the details of the experiment. Sean
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Hi Sean, Thanks. I wasn't aware of the quality metrics. I googled it and will have a go with simpleaffy to see how I get on. How would you decide if GCRMA is better than MAS5 or vice-versa if you have more info? The arrays came from GSE1485 in GEO. Cheers, Alex -----Original Message----- From: Sean Davis [mailto:sdavis2@mail.nih.gov] Sent: 05 October 2006 11:47 To: bioconductor at stat.math.ethz.ch Cc: alex lam (RI) Subject: Re: [BioC] Normalization quality On Thursday 05 October 2006 06:22, alex lam (RI) wrote: > Dear BioCers, > > Hi! I don't have much of experience in handling affy chips and I hope > someone can help me here. > > I loaded 276 affy CEL files using justGCRMA - my computer couldn't > cope going the readAffy route. > Quantile normalization was done. > > I have noticed that for some probesets there are some strange results. > > For example: > > summary(exprs(eset.norm.quantile)["203329_at",]) > > Min. 1st Qu. Median Mean 3rd Qu. Max. > 2.486 2.513 2.523 2.545 2.534 5.201 > > This gene seems to be very lowly expressed for everyone except for 1 > chip. I did a boxplot of that chip against a few others after > normalization and the overall distributions are similar. I would look at the quality metrics for the arrays as a separate issue from normalization. If you have 276 arrays, there will likely be an "outlier" for at least one array FOR MANY GENES. > (1) As I don't have an AffyBatch object, is there a way to make image > plots? And what other methods are there to catch these odd ones? > > (2) I was provided summarised expression values from an external group > and they used MAS5 for pre-processing. For the same gene, the > expression > > summary is: > > summary(mas.expr[,"X203329_at"]) # I received a csv file, hence it's > > a > > data frame > Min. 1st Qu. Median Mean 3rd Qu. Max. > 2.926 5.337 5.950 5.880 6.403 9.183 > The variation seems to be much greater, and it looks much more > interesting. I think that for most purposes, most people would agree that MAS5 is inferior to RMA or GCRMA, but such a statement is a dangerous one to make without knowing the details of the experiment. Sean
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