Question: BioC normalisations for small array 2 colour data?
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gravatar for Dan Swan
13.0 years ago by
Dan Swan50
Dan Swan50 wrote:
Hi, I have some data from a small specialised microarray - 200 genes, 1 spiked control, 1 negative control. This is 2 colour data, with dye swaps. I was wondering what an appropriate normalisation for this scenario is within Bioconductor given that Lowess is unreliable for <1000 genes? Any pointers would be gratefully recieved. thanks, Dan -- Senior Research Associate, Bioinformatics Support Unit, Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Framlington Place, University of Newcastle upon Tyne, Newcastle, NE2 4HH Tel: +44 (0)191 222 7253 (Leech offices: Rooms M.2046/M.2046A) Tel: +44 (0)191 246 4833 (Devonshire offices: Rooms G.25/G.26) Website: http://bioinf.ncl.ac.uk/support/
microarray • 371 views
ADD COMMENTlink modified 13.0 years ago by martin.schumacher@novartis.com80 • written 13.0 years ago by Dan Swan50
Answer: BioC normalisations for small array 2 colour data?
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gravatar for Sean Davis
13.0 years ago by
Sean Davis21k
United States
Sean Davis21k wrote:
On Thursday 07 September 2006 08:44, Dan Swan wrote: > Hi, > > I have some data from a small specialised microarray - 200 genes, 1 > spiked control, 1 negative control. This is 2 colour data, with dye > swaps. I was wondering what an appropriate normalisation for this > scenario is within Bioconductor given that Lowess is unreliable for > <1000 genes? There is no "correct" answer here. You will need to look at the data and determine what needs to be done. Scatterplots, density plots/histograms, and M vs. A plots can help. If your genes were chosen because they were all thought to be differentially expressed, then any normalization method for two-color arrays will be inappropriate and you should probably think about single-channel normalization. Sean
ADD COMMENTlink written 13.0 years ago by Sean Davis21k
Hi Dan, shameless self-promotion, you could try "vsn" because it only estimates 4 parameters in total in your case, which should be possible with good enough precision from 404 data points. If you do not expect many genes to be differentially expressed, please set the parameter 'lts.quantile' (it controls the degree of robustness or resistance of the estimator) to a higher value than the default, e.g. 0.95. You can use the spike control to see whether the result is plausible. Sean - I agree that normalization methods that are based on assumptions of invariance of 'something' between the different colors or arrays can (but need not) fail if a large part of genes is differentially expressed - but I am not following the argument why 'single-channel' methods would be fundamentally different in this respect. Best wishes Wolfgang Sean Davis wrote: > On Thursday 07 September 2006 08:44, Dan Swan wrote: >> Hi, >> >> I have some data from a small specialised microarray - 200 genes, 1 >> spiked control, 1 negative control. This is 2 colour data, with dye >> swaps. I was wondering what an appropriate normalisation for this >> scenario is within Bioconductor given that Lowess is unreliable for >> <1000 genes? > > There is no "correct" answer here. You will need to look at the data and > determine what needs to be done. Scatterplots, density plots/histograms, and > M vs. A plots can help. > > If your genes were chosen because they were all thought to be differentially > expressed, then any normalization method for two-color arrays will be > inappropriate and you should probably think about single-channel > normalization. > > Sean > > _______________________________________________ > 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 13.0 years ago by Wolfgang Huber13k
On Friday 08 September 2006 00:29, you wrote: bioconductor at stat.math.ethz.ch > Sean - I agree that normalization methods that are based on assumptions > of invariance of 'something' between the different colors or arrays can > (but need not) fail if a large part of genes is differentially expressed > - but I am not following the argument why 'single-channel' methods would > be fundamentally different in this respect. I made an assumption that there might be a common reference which could be used (I am not saying exactly HOW it could be used, but if present, should be invariant). I totally agree that simple single channel normalization is also problematic in the situation where all genes are expected to be differentially expressed. Sean
ADD REPLYlink written 13.0 years ago by Sean Davis21k
Dear Sean, Wolfgang Huber and All Sean Davis wrote: > I totally agree that simple single channel normalization is also problematic > in the situation where all genes are expected to be differentially expressed. > > I have exactly this one: a one color array with 200 genes where all genes are expected to be differentially expressed. I did vsn normalization with default parameters. After this discussion, I am not sure if I did the correct one? What is the suggestion in this situation? Thank you very much -- Marcelo Luiz de Laia Ph.D Candidate S?o Paulo State University (http://www.unesp.br/eng/) School of Agricultural and Veterinary Sciences Department of Technology Via de Acesso Prof.Paulo Donato Castellane s/n 14884-900 Jaboticabal - SP - Brazil Fone: +55-016-3209-2675 Cell: +55-016-97098526
ADD REPLYlink written 13.0 years ago by Marcelo Luiz de Laia770
Answer: BioC normalisations for small array 2 colour data?
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gravatar for martin.schumacher@novartis.com
13.0 years ago by
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ADD COMMENTlink written 13.0 years ago by martin.schumacher@novartis.com80
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