extract lmFit results?
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Jianping Jin ▴ 890
@jianping-jin-1212
Last seen 10.2 years ago
Dear list, Could anyone point me out how to extract the results from lmFit? The on-line document said "the probe-wise fitted model results are stored in a compact form". What I wanted to get is ALL data values after linear model normalization instead of going through all limma processes. Thanks in advance! Jianping xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx x Jianping Jin Ph.D. x x Bioinformatics scientist x x Center for bioinformatics x x 3133 Bioinformatics Building x x CB# 7104 x x University of North Carolina x x Chapel Hill, NC 27599 x x Tel: (919)843-6105 x x Fax: (919)843-3103 x x E-mail: jjin at email.unc.edu x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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@james-w-macdonald-5106
Last seen 2 minutes ago
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Jianping Jin wrote: > Dear list, > > Could anyone point me out how to extract the results from lmFit? The > on-line document said "the probe-wise fitted model results are stored in a > compact form". What I wanted to get is ALL data values after linear model > normalization instead of going through all limma processes. The results are output in a named list. As with all named lists, you can extract the names using names(listname). If, for example, your lmFit() output is called 'fit', you can get information in several ways: names(fit) ## gives names fit ## outputs first 10 or so values in each list component fit$coefficients ##extract coefficients fit$p.value ## p-values etc. See ?list, ?"[[" for more information. HTH, Jim > > Thanks in advance! > > Jianping > > xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx > x Jianping Jin Ph.D. x > x Bioinformatics scientist x > x Center for bioinformatics x > x 3133 Bioinformatics Building x > x CB# 7104 x > x University of North Carolina x > x Chapel Hill, NC 27599 x > x Tel: (919)843-6105 x > x Fax: (919)843-3103 x > x E-mail: jjin at email.unc.edu x > xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623
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Thanks James! Please forgive my ignorance if I am wrong. I knew "fit$coefficients" stuff. That just give me "coefficients". Is that "b" in y = a + bx? It must be more complicated than that I guess. In my case I just used default "design" in fitting as below: fitR <- lmFit(RG$R, ndups=2, spacing=722, correlation=0.5205939) What if I am interested in getting all "y" values for each gene in each chip after lmFit fitting? Do I need to calculate them out following some formula? Or I can extract them from somewhere within the limma package? Thanks again! Jianping --On Friday, February 17, 2006 1:27 PM -0500 "James W. MacDonald" <jmacdon at="" med.umich.edu=""> wrote: > Jianping Jin wrote: >> Dear list, >> >> Could anyone point me out how to extract the results from lmFit? The >> on-line document said "the probe-wise fitted model results are stored in >> a compact form". What I wanted to get is ALL data values after linear >> model normalization instead of going through all limma processes. > > The results are output in a named list. As with all named lists, you can > extract the names using names(listname). If, for example, your lmFit() > output is called 'fit', you can get information in several ways: > > names(fit) ## gives names > fit ## outputs first 10 or so values in each list component > fit$coefficients ##extract coefficients > fit$p.value ## p-values > etc. > > See ?list, ?"[[" for more information. > > HTH, > > Jim > > >> >> Thanks in advance! >> >> Jianping >> >> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >> x Jianping Jin Ph.D. x >> x Bioinformatics scientist x >> x Center for bioinformatics x >> x 3133 Bioinformatics Building x >> x CB# 7104 x >> x University of North Carolina x >> x Chapel Hill, NC 27599 x >> x Tel: (919)843-6105 x >> x Fax: (919)843-3103 x >> x E-mail: jjin at email.unc.edu x >> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor > > > -- > James W. MacDonald, M.S. > Biostatistician > Affymetrix and cDNA Microarray Core > University of Michigan Cancer Center > 1500 E. Medical Center Drive > 7410 CCGC > Ann Arbor MI 48109 > 734-647-5623 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx x Jianping Jin Ph.D. x x Bioinformatics scientist x x Center for bioinformatics x x 3133 Bioinformatics Building x x CB# 7104 x x University of North Carolina x x Chapel Hill, NC 27599 x x Tel: (919)843-6105 x x Fax: (919)843-3103 x x E-mail: jjin at email.unc.edu x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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Hi Jianping, Jianping Jin wrote: > Thanks James! > > Please forgive my ignorance if I am wrong. I knew "fit$coefficients" stuff. > That just give me "coefficients". Is that "b" in y = a + bx? It must be > more complicated than that I guess. In my case I just used default "design" > in fitting as below: Well, that formula is conventionally used to describe a linear regression where there is some concept of slope. We are fitting an ANOVA model, and since there is no ordering of the factor levels, there is no concept of slope. A more conventional formula for ANOVA (without the subscripts) would be something like y = a + b + error The coefficients would correspond to 'a' and 'b', which depending on the design matrix can mean different things. > > fitR <- lmFit(RG$R, ndups=2, spacing=722, correlation=0.5205939) Hmmm. I haven't done any cDNA work in years, but I don't think that looks right. If I am not mistaken, what you are doing is fitting a one-sample t-test on just the cy5 (or is cy3 red? I can never remember) values which is usually not what you want. The convention is to fit a one-sample t-test on the log ratios, which then tests to see if the average log ratio is different from zero (which corresponds to testing if the average fold change is different from one). The model here would be y = a + error > > What if I am interested in getting all "y" values for each gene in each > chip after lmFit fitting? Do I need to calculate them out following some > formula? Or I can extract them from somewhere within the limma package? If by 'y' values you mean the fitted values, you would get those from the fit$coefficients, which in your case should just be a vector, one value per gene. HTH, Jim > > Thanks again! > > Jianping > > --On Friday, February 17, 2006 1:27 PM -0500 "James W. MacDonald" > <jmacdon at="" med.umich.edu=""> wrote: > > >>Jianping Jin wrote: >> >>>Dear list, >>> >>>Could anyone point me out how to extract the results from lmFit? The >>>on-line document said "the probe-wise fitted model results are stored in >>>a compact form". What I wanted to get is ALL data values after linear >>>model normalization instead of going through all limma processes. >> >>The results are output in a named list. As with all named lists, you can >>extract the names using names(listname). If, for example, your lmFit() >>output is called 'fit', you can get information in several ways: >> >>names(fit) ## gives names >>fit ## outputs first 10 or so values in each list component >>fit$coefficients ##extract coefficients >>fit$p.value ## p-values >>etc. >> >>See ?list, ?"[[" for more information. >> >>HTH, >> >>Jim >> >> >> >>>Thanks in advance! >>> >>>Jianping >>> >>>xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >>>x Jianping Jin Ph.D. x >>>x Bioinformatics scientist x >>>x Center for bioinformatics x >>>x 3133 Bioinformatics Building x >>>x CB# 7104 x >>>x University of North Carolina x >>>x Chapel Hill, NC 27599 x >>>x Tel: (919)843-6105 x >>>x Fax: (919)843-3103 x >>>x E-mail: jjin at email.unc.edu x >>>xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >>> >>>_______________________________________________ >>>Bioconductor mailing list >>>Bioconductor at stat.math.ethz.ch >>>https://stat.ethz.ch/mailman/listinfo/bioconductor >> >> >>-- >>James W. MacDonald, M.S. >>Biostatistician >>Affymetrix and cDNA Microarray Core >>University of Michigan Cancer Center >>1500 E. Medical Center Drive >>7410 CCGC >>Ann Arbor MI 48109 >>734-647-5623 > > > > > xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx > x Jianping Jin Ph.D. x > x Bioinformatics scientist x > x Center for bioinformatics x > x 3133 Bioinformatics Building x > x CB# 7104 x > x University of North Carolina x > x Chapel Hill, NC 27599 x > x Tel: (919)843-6105 x > x Fax: (919)843-3103 x > x E-mail: jjin at email.unc.edu x > xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623
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Many thanks for your explanation! have a nice weekend! JP- --On Friday, February 17, 2006 3:11 PM -0500 "James W. MacDonald" <jmacdon at="" med.umich.edu=""> wrote: > Hi Jianping, > > Jianping Jin wrote: >> Thanks James! >> >> Please forgive my ignorance if I am wrong. I knew "fit$coefficients" >> stuff. That just give me "coefficients". Is that "b" in y = a + bx? It >> must be more complicated than that I guess. In my case I just used >> default "design" in fitting as below: > > Well, that formula is conventionally used to describe a linear regression > where there is some concept of slope. We are fitting an ANOVA model, and > since there is no ordering of the factor levels, there is no concept of > slope. A more conventional formula for ANOVA (without the subscripts) > would be something like > > y = a + b + error > > The coefficients would correspond to 'a' and 'b', which depending on the > design matrix can mean different things. > >> >> fitR <- lmFit(RG$R, ndups=2, spacing=722, correlation=0.5205939) > > Hmmm. I haven't done any cDNA work in years, but I don't think that looks > right. If I am not mistaken, what you are doing is fitting a one- sample > t-test on just the cy5 (or is cy3 red? I can never remember) values which > is usually not what you want. The convention is to fit a one-sample > t-test on the log ratios, which then tests to see if the average log > ratio is different from zero (which corresponds to testing if the average > fold change is different from one). > > The model here would be > > y = a + error > > >> >> What if I am interested in getting all "y" values for each gene in each >> chip after lmFit fitting? Do I need to calculate them out following some >> formula? Or I can extract them from somewhere within the limma package? > > If by 'y' values you mean the fitted values, you would get those from the > fit$coefficients, which in your case should just be a vector, one value > per gene. > > HTH, > > Jim > > > >> >> Thanks again! >> >> Jianping >> >> --On Friday, February 17, 2006 1:27 PM -0500 "James W. MacDonald" >> <jmacdon at="" med.umich.edu=""> wrote: >> >> >>> Jianping Jin wrote: >>> >>>> Dear list, >>>> >>>> Could anyone point me out how to extract the results from lmFit? The >>>> on-line document said "the probe-wise fitted model results are stored >>>> in a compact form". What I wanted to get is ALL data values after >>>> linear model normalization instead of going through all limma >>>> processes. >>> >>> The results are output in a named list. As with all named lists, you can >>> extract the names using names(listname). If, for example, your lmFit() >>> output is called 'fit', you can get information in several ways: >>> >>> names(fit) ## gives names >>> fit ## outputs first 10 or so values in each list component >>> fit$coefficients ##extract coefficients >>> fit$p.value ## p-values >>> etc. >>> >>> See ?list, ?"[[" for more information. >>> >>> HTH, >>> >>> Jim >>> >>> >>> >>>> Thanks in advance! >>>> >>>> Jianping >>>> >>>> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >>>> x Jianping Jin Ph.D. x >>>> x Bioinformatics scientist x >>>> x Center for bioinformatics x >>>> x 3133 Bioinformatics Building x >>>> x CB# 7104 x >>>> x University of North Carolina x >>>> x Chapel Hill, NC 27599 x >>>> x Tel: (919)843-6105 x >>>> x Fax: (919)843-3103 x >>>> x E-mail: jjin at email.unc.edu x >>>> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at stat.math.ethz.ch >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> >>> >>> -- >>> James W. MacDonald, M.S. >>> Biostatistician >>> Affymetrix and cDNA Microarray Core >>> University of Michigan Cancer Center >>> 1500 E. Medical Center Drive >>> 7410 CCGC >>> Ann Arbor MI 48109 >>> 734-647-5623 >> >> >> >> >> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >> x Jianping Jin Ph.D. x >> x Bioinformatics scientist x >> x Center for bioinformatics x >> x 3133 Bioinformatics Building x >> x CB# 7104 x >> x University of North Carolina x >> x Chapel Hill, NC 27599 x >> x Tel: (919)843-6105 x >> x Fax: (919)843-3103 x >> x E-mail: jjin at email.unc.edu x >> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor > > > -- > James W. MacDonald, M.S. > Biostatistician > Affymetrix and cDNA Microarray Core > University of Michigan Cancer Center > 1500 E. Medical Center Drive > 7410 CCGC > Ann Arbor MI 48109 > 734-647-5623 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx x Jianping Jin Ph.D. x x Bioinformatics scientist x x Center for bioinformatics x x 3133 Bioinformatics Building x x CB# 7104 x x University of North Carolina x x Chapel Hill, NC 27599 x x Tel: (919)843-6105 x x Fax: (919)843-3103 x x E-mail: jjin at email.unc.edu x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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