M vs A plot
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@richard-friedman-513
Last seen 9.7 years ago
Dear Bioconductors, I have normalized a series of arrays using print-tip normalization. Where as the systematic error in the unnormalized data was pronounced, The systematic error on the normalized array was reduced greatly. The M vs. A curve was flat for most of the 48 print-tips. However for a few printips, for A>12 M deviates from close to zero. in one case, M rises as high as M=1/2. at A=15. This only involves a small fraction of the spots (It is hard to estimate what proportion). Does this sound serious? If so, what should I do about it? Is anyone willing to look at the JPEg file (I did not attach it because I don't know if I am allowed to do so). Thanks and best wishes, Rich ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist Herbert Irving Comprehensive Cancer Center Oncoinformatics Core Lecturer Department of Biomedical Informatics Box 95, Room 130BB or P&S 1-420C Columbia University Medical Center 630 W. 168th St. New York, NY 10032 (212)305-6901 (5-6901) (voice) friedman@cancercenter.columbia.edu http://cancercenter.columbia.edu/~friedman/ "Spring, Summer, and Winter. Then Fall came along, and that's the end of our song, and the pigeons never hibernate at all". -Rose Friedman, age 7 (These are the correct lyrics and supersede the version previously at the end of my sig)
Cancer Cancer • 936 views
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@michael-watson-iah-c-378
Last seen 9.7 years ago
Richard The nature of any normalisation means that we will always have outliers - those spots that deviate from all the rest. There could be two reasons - that spot represents a differentially expressed gene or the spot is unreliable and comes from a "bad" spot. I'd take the common sense approach to these outliers: i) Check any replicate spots - if all replicate spots are outliers then you have evidence that it's a differentially expressed gene. However, if the replicates disagree, this is evidence that the outlier comes from an unreliable / bad measurement ii) Go take a look at the spot on the original image. Does it look "good"? You are likely always to find outliers after normalisation. This is, after all, what we are looking for, isn't it? The key is to be able to say, when you see an outlier, if that spot is of reliable quality or not. Thanks Mick -----Original Message----- From: Richard Friedman [mailto:friedman@cancercenter.columbia.edu] Sent: 29 January 2004 22:26 To: 'Bioconductor Mail List' Cc: IRA A TABAS Subject: [BioC] M vs A plot Dear Bioconductors, I have normalized a series of arrays using print-tip normalization. Where as the systematic error in the unnormalized data was pronounced, The systematic error on the normalized array was reduced greatly. The M vs. A curve was flat for most of the 48 print-tips. However for a few printips, for A>12 M deviates from close to zero. in one case, M rises as high as M=1/2. at A=15. This only involves a small fraction of the spots (It is hard to estimate what proportion). Does this sound serious? If so, what should I do about it? Is anyone willing to look at the JPEg file (I did not attach it because I don't know if I am allowed to do so). Thanks and best wishes, Rich ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist Herbert Irving Comprehensive Cancer Center Oncoinformatics Core Lecturer Department of Biomedical Informatics Box 95, Room 130BB or P&S 1-420C Columbia University Medical Center 630 W. 168th St. New York, NY 10032 (212)305-6901 (5-6901) (voice) friedman@cancercenter.columbia.edu http://cancercenter.columbia.edu/~friedman/ "Spring, Summer, and Winter. Then Fall came along, and that's the end of our song, and the pigeons never hibernate at all". -Rose Friedman, age 7 (These are the correct lyrics and supersede the version previously at the end of my sig) _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
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Mick, Thanks for the help. What concerns me however is not a single point being an outlier, but the whole loess fit to all the points leading the lowess curve for a few printips to deviate significantly from being a straight line practically colinear with the x-axis (abcissa). The two test cases on which I learned to use marray - the apoE data that comes with spot, and the swirl data that comes with marray, all had significantly expressed genes - however they also had flat normalized lowess curves. Significant curvature in the lowess curve leads me to be concerned that the spots associated with that region of the curve are improperly normalized. Can anyone out there give me: 1. Guidelines as to how flat the lowess curve should be for the data to be considered normalized. 2. Advice as to what to do if the printtip normalization option in marray did not remove intensity dependence. If anyone is willing to look at the M vs A curve, I would be grateful. Thanks and best wishes, Rich On Fri, 30 Jan 2004, michael watson (IAH-C) wrote: > Richard > > The nature of any normalisation means that we will always have outliers - those spots that deviate from all the rest. There could be two reasons - that spot represents a differentially expressed gene or the spot is unreliable and comes from a "bad" spot. > > I'd take the common sense approach to these outliers: > > i) Check any replicate spots - if all replicate spots are outliers then you have evidence that it's a differentially expressed gene. However, if the replicates disagree, this is evidence that the outlier comes from an unreliable / bad measurement > > ii) Go take a look at the spot on the original image. Does it look "good"? > > You are likely always to find outliers after normalisation. This is, after all, what we are looking for, isn't it? The key is to be able to say, when you see an outlier, if that spot is of reliable quality or not. > > Thanks > Mick > > -----Original Message----- > From: Richard Friedman [mailto:friedman@cancercenter.columbia.edu] > Sent: 29 January 2004 22:26 > To: 'Bioconductor Mail List' > Cc: IRA A TABAS > Subject: [BioC] M vs A plot > > > Dear Bioconductors, > > I have normalized a series of arrays using print-tip normalization. > Where as the systematic error in the unnormalized data was pronounced, > The systematic error on the normalized array was reduced greatly. > The M vs. A curve was flat for most of the 48 print-tips. However for a > few > printips, for A>12 M deviates from close to zero. in one case, M rises > as high > as M=1/2. at A=15. This only involves a small fraction of the spots (It > is hard to > estimate what proportion). > > Does this sound serious? > > If so, what should I do about it? > > Is anyone willing to look at the JPEg file (I did not attach it > because I don't > know if I am allowed to do so). > > Thanks and best wishes, > Rich > ------------------------------------------------------------ > Richard A. Friedman, PhD > Associate Research Scientist > Herbert Irving Comprehensive Cancer Center > Oncoinformatics Core > Lecturer > Department of Biomedical Informatics > Box 95, Room 130BB or P&S 1-420C > Columbia University Medical Center > 630 W. 168th St. > New York, NY 10032 > (212)305-6901 (5-6901) (voice) > friedman@cancercenter.columbia.edu > http://cancercenter.columbia.edu/~friedman/ > > "Spring, Summer, and Winter. > Then Fall came along, > and that's the end of our song, > and the pigeons never hibernate at all". > -Rose Friedman, age 7 > (These are the correct lyrics and supersede > the version previously at the end of my sig) > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist Herbert Irving Comprehensive Cancer Center Oncoinformatics Core Lecturer Department of Biomedical Informatics Box 95, Room 130BB or P&S 1-420C Columbia University Medical Center 630 W. 168th St. New York, NY 10032 (212)305-6901 (5-6901) (voice) friedman@cancercenter.columbia.edu http://cancercenter.columbia.edu/~friedman/ "Spring, Summer, and Winter. Then Fall came along, and that's the end of our song, and the pigeons never hibernate at all". -Rose Friedman, age 7 (These are the correct lyrics and supersede the version previously at the end of my sig)
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Hi Richard loess normalization has a parameter "span", which determines the so-called bandwidth of the smoothing window. Apparently the default is too small for you (leading to a too flexible regression curve), so you have to make it larger. There is a lot of literature on choosing bandwidths (most of it seems to involve some kind of cross-validation), but I am actually not aware on recommendations for microarrays other than "by eye". Best wishes Wolfgang -- ------------------------------------- Wolfgang Huber Division of Molecular Genome Analysis German Cancer Research Center Heidelberg, Germany Phone: +49 6221 424709 Fax: +49 6221 42524709 Http: www.dkfz.de/abt0840/whuber ------------------------------------- Richard Friedman wrote: > Mick, > > Thanks for the help. What concerns me however is not a single > point being an outlier, but the whole loess fit to all the points leading > the lowess curve for a few printips to deviate significantly from being > a straight line practically colinear with the x-axis (abcissa). The two > test cases on which I learned to use marray - the apoE data that comes > with spot, and the swirl data that comes with marray, all had > significantly expressed genes - however they also had flat normalized > lowess curves. Significant curvature in the lowess curve leads me > to be concerned that the spots associated with that region of > the curve are improperly normalized. > > Can anyone out there give me: > > 1. Guidelines as to how flat the lowess curve should be for the > data to be considered normalized. > > 2. Advice as to what to do if the printtip normalization option > in marray did not remove intensity dependence. > > If anyone is willing to look at the M vs A curve, I would be grateful. > > Thanks and best wishes, > Rich > > >
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