Limma Coefficients using lmscFit

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Gordon Smyth 53k

@gordon-smyth

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WEHI, Melbourne, Australia

> Date: Tue, 20 Dec 2005 21:12:24 -0500 > From: Naomi Altman <naomi at="" stat.psu.edu=""> > Subject: [BioC] Limma Coefficients using lmscFit > To: bioconductor at stat.math.ethz.ch > Cc: QING ZHANG <qxz5 at="" psu.edu=""> > > Being a big believer in single-channel analysis of loop designs, I > used lmscFit to analyze my loop design data. All went well until the > investigator requested the normalized single channel data. > Since lmscFit actually operates on M and A, we took the same MAlist, > and created R and G using RG.MA. > > To check the computation, we then computed the treatment means by > hand for a few genes. These did not work out to the same treatment > means obtained from lmscFit (using model ~-1+Trt). > > I hope that someone can explain why these are not equal. (And I > really hope that this is not another case where I did not read the > documentation sufficiently carefully.) > > > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 I assume you're taking means of log-intensities. lmscFit() uses generalised least squares with block weights (as for a mixed model analysis), so the values from lmscFit()$coef will be simple means only for balanced designs. The coefficients should not in a different ball park to the means however. Best wishes Gordon

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ADD COMMENT • link updated 20.0 years ago by Sean Davis 21k • written 20.0 years ago by Gordon Smyth 53k

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Sean Davis 21k

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United States

On 12/22/05 2:27 AM, "Gordon K Smyth" <smyth at="" wehi.edu.au=""> wrote: >> Date: Tue, 20 Dec 2005 21:12:24 -0500 >> From: Naomi Altman <naomi at="" stat.psu.edu=""> >> Subject: [BioC] Limma Coefficients using lmscFit >> To: bioconductor at stat.math.ethz.ch >> Cc: QING ZHANG <qxz5 at="" psu.edu=""> >> >> Being a big believer in single-channel analysis of loop designs, I >> used lmscFit to analyze my loop design data. All went well until the >> investigator requested the normalized single channel data. >> Since lmscFit actually operates on M and A, we took the same MAlist, >> and created R and G using RG.MA. >> >> To check the computation, we then computed the treatment means by >> hand for a few genes. These did not work out to the same treatment >> means obtained from lmscFit (using model ~-1+Trt). >> >> I hope that someone can explain why these are not equal. (And I >> really hope that this is not another case where I did not read the >> documentation sufficiently carefully.) >> >> >> >> Naomi S. Altman 814-865-3791 (voice) >> Associate Professor >> Dept. of Statistics 814-863-7114 (fax) >> Penn State University 814-865-1348 (Statistics) >> University Park, PA 16802-2111 > > I assume you're taking means of log-intensities. lmscFit() uses generalised > least squares with > block weights (as for a mixed model analysis), so the values from > lmscFit()$coef will be simple > means only for balanced designs. The coefficients should not in a different > ball park to the > means however. Spot weights could also make the simple means different from the estimates, even for balanced designs, couldn't they? Sean

ADD COMMENT • link 20.0 years ago Sean Davis 21k

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Dear Sean, lmscFit does not use the spot weights. --Naomi At 06:57 AM 12/22/2005, Sean Davis wrote: >On 12/22/05 2:27 AM, "Gordon K Smyth" <smyth at="" wehi.edu.au=""> wrote: > > >> Date: Tue, 20 Dec 2005 21:12:24 -0500 > >> From: Naomi Altman <naomi at="" stat.psu.edu=""> > >> Subject: [BioC] Limma Coefficients using lmscFit > >> To: bioconductor at stat.math.ethz.ch > >> Cc: QING ZHANG <qxz5 at="" psu.edu=""> > >> > >> Being a big believer in single-channel analysis of loop designs, I > >> used lmscFit to analyze my loop design data. All went well until the > >> investigator requested the normalized single channel data. > >> Since lmscFit actually operates on M and A, we took the same MAlist, > >> and created R and G using RG.MA. > >> > >> To check the computation, we then computed the treatment means by > >> hand for a few genes. These did not work out to the same treatment > >> means obtained from lmscFit (using model ~-1+Trt). > >> > >> I hope that someone can explain why these are not equal. (And I > >> really hope that this is not another case where I did not read the > >> documentation sufficiently carefully.) > >> > >> > >> > >> Naomi S. Altman 814-865-3791 (voice) > >> Associate Professor > >> Dept. of Statistics 814-863-7114 (fax) > >> Penn State University 814-865-1348 (Statistics) > >> University Park, PA 16802-2111 > > > > I assume you're taking means of log-intensities. lmscFit() uses > generalised > > least squares with > > block weights (as for a mixed model analysis), so the values from > > lmscFit()$coef will be simple > > means only for balanced designs. The coefficients should not in > a different > > ball park to the > > means however. > >Spot weights could also make the simple means different from the estimates, >even for balanced designs, couldn't they? > >Sean > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111

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