Christopher Anderson
2011-Feb-01 16:08 UTC
[R] Fitting ELISA measurements "unknowns" to 4 parameter logistic model
Hello, I am trying to fit my Elisa results (absorbance readings) to a standard curve. To create the standard curve model, I performed a 4-parameter logistic fit using the 'drc' package (ExpectedConc~Absorbance). This gave me the following:> FourPA 'drc' model. Call: drm(formula = Response ~ Expected, data = SC, fct = LL.4()) Coefficients: b:(Intercept) c:(Intercept) d:(Intercept) e:(Intercept) 1.336 6.236 85.521 59.598> summary(FourP)Model fitted: Log-logistic (ED50 as parameter) (4 parms) Parameter estimates: Estimate Std. Error t-value p-value b:(Intercept) 1.33596 0.15861 8.42309 0.0011 c:(Intercept) 6.23557 3.18629 1.95700 0.1220 d:(Intercept) 85.52140 2.15565 39.67313 0.0000 e:(Intercept) 59.59835 5.18781 11.48815 0.0003 Residual standard error: 1.866876 (4 degrees of freedom) Now that I have the 4 parameters, how do I fit the absorbance readings for the analytical unknowns to the standard curve model (as to estimate the concentrations of my unknown analytical samples)? I can use the argument 'predict', but this predicts absorbance given concentrations (y given x), I need to predict concentrations give absorbance (x given y). Thanks! Chris [[alternative HTML version deleted]]
David Winsemius
2011-Feb-01 17:42 UTC
[R] Fitting ELISA measurements "unknowns" to 4 parameter logistic model
On Feb 1, 2011, at 11:08 AM, Christopher Anderson wrote:> Hello, > > I am trying to fit my Elisa results (absorbance readings) to a > standard > curve. To create the standard curve model, I performed a 4-parameter > logistic fit using the 'drc' package (ExpectedConc~Absorbance). This > gave me > the following: >> FourP > > A 'drc' model. > > Call: > drm(formula = Response ~ Expected, data = SC, fct = LL.4()) > > Coefficients: > b:(Intercept) c:(Intercept) d:(Intercept) e:(Intercept) > 1.336 6.236 85.521 59.598 > >> summary(FourP) > > Model fitted: Log-logistic (ED50 as parameter) (4 parms) > > Parameter estimates: > > Estimate Std. Error t-value p-value > b:(Intercept) 1.33596 0.15861 8.42309 0.0011 > c:(Intercept) 6.23557 3.18629 1.95700 0.1220 > d:(Intercept) 85.52140 2.15565 39.67313 0.0000 > e:(Intercept) 59.59835 5.18781 11.48815 0.0003 > > Residual standard error: > > 1.866876 (4 degrees of freedom) > > Now that I have the 4 parameters, how do I fit the absorbance > readings for > the analytical unknowns to the standard curve model (as to estimate > the > concentrations of my unknown analytical samples)? > I can use the argument 'predict', but this predicts absorbance given > concentrations (y given x), I need to predict concentrations give > absorbance > (x given y). >Use approxfun() to construct a curve using the predict() values as x at reasonable intervals and the y values you used for predict. It effectively swaps the inputs to predict x from y. There is a worked example from two weeks ago in the Archives with the Subject line: "Inverse Prediction with splines"> Thanks! > Chris >David Winsemius, MD West Hartford, CT
Christopher Anderson <recsa <at> channing.harvard.edu> writes:> > Hello, > > I am trying to fit my Elisa results (absorbance readings) to a standard > curve. To create the standard curve model, I performed a 4-parameter > logistic fit using the 'drc' package (ExpectedConc~Absorbance). This gave me > the following: > > FourP > > A 'drc' model. > > Call: > drm(formula = Response ~ Expected, data = SC, fct = LL.4()) > > Coefficients: > b:(Intercept) c:(Intercept) d:(Intercept) e:(Intercept) > 1.336 6.236 85.521 59.598 > > > summary(FourP) > > Model fitted: Log-logistic (ED50 as parameter) (4 parms) > > Parameter estimates: > > Estimate Std. Error t-value p-value > b:(Intercept) 1.33596 0.15861 8.42309 0.0011 > c:(Intercept) 6.23557 3.18629 1.95700 0.1220 > d:(Intercept) 85.52140 2.15565 39.67313 0.0000 > e:(Intercept) 59.59835 5.18781 11.48815 0.0003 > > Residual standard error: > > 1.866876 (4 degrees of freedom) > > Now that I have the 4 parameters, how do I fit the absorbance readings for > the analytical unknowns to the standard curve model (as to estimate the > concentrations of my unknown analytical samples)? > I can use the argument 'predict', but this predicts absorbance given > concentrations (y given x), I need to predict concentrations give absorbance > (x given y). > > Thanks! > Chris > > [[alternative HTML version deleted]] > >Hi Chris, Disclaimer: I am a technical support supervisor for Hitachi Solutions. I have a solution that does not involve using R but rather a software package that is dedicated to ELISA analysis with focus on the 4PL and 5PL model equations. If you are not interested, then please disregard the rest of this message. MasterPlex ReaderFit is a software package dedicated to the quantitative analysis of ELISA data. Once you fit your standards, all concentrations of your unknown samples will automatically be interpolated or extrapolated. Here is a link to a free 14-day trial of the software that is fully-functional: http://www.miraibio.com/masterplex-readerfit/curve-fitting-for-plate- readers.html If you would like, I can assist you with the analysis of your data. You can email me at aliu at miraibio dot com and I would more than happy to do some data analysis with you. In addition, here is a blog post that I have written a while back for some tips on ELISA data analysis: http://www.miraibio.com/blog/2009/06/tips-for-data-analysis/ I hope this helps. Best Regards, Allen Liu
Hugo Mildenberger
2011-Feb-01 20:18 UTC
[R] Fitting ELISA measurements "unknowns" to 4 parameter logistic model
Hello Chris, You may also use the R-package "calib". Hugo On Tuesday 01 February 2011 17:08:13 Christopher Anderson wrote:> Hello, > > I am trying to fit my Elisa results (absorbance readings) to a standard > curve. To create the standard curve model, I performed a 4-parameter > logistic fit using the 'drc' package (ExpectedConc~Absorbance). This gave me > the following: > > FourP > > A 'drc' model. > > Call: > drm(formula = Response ~ Expected, data = SC, fct = LL.4()) > > Coefficients: > b:(Intercept) c:(Intercept) d:(Intercept) e:(Intercept) > 1.336 6.236 85.521 59.598 > > > summary(FourP) > > Model fitted: Log-logistic (ED50 as parameter) (4 parms) > > Parameter estimates: > > Estimate Std. Error t-value p-value > b:(Intercept) 1.33596 0.15861 8.42309 0.0011 > c:(Intercept) 6.23557 3.18629 1.95700 0.1220 > d:(Intercept) 85.52140 2.15565 39.67313 0.0000 > e:(Intercept) 59.59835 5.18781 11.48815 0.0003 > > Residual standard error: > > 1.866876 (4 degrees of freedom) > > Now that I have the 4 parameters, how do I fit the absorbance readings for > the analytical unknowns to the standard curve model (as to estimate the > concentrations of my unknown analytical samples)? > I can use the argument 'predict', but this predicts absorbance given > concentrations (y given x), I need to predict concentrations give absorbance > (x given y). > > Thanks! > Chris > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >
assaywiz
2011-Aug-02 08:57 UTC
[R] Fitting ELISA measurements "unknowns" to 4 parameter logistic model
Try http://www.myassays.com/four-parameter-fit.assay It?s free, requires no install and pre-configured for ELISAs. Just paste and go AW -- View this message in context: http://r.789695.n4.nabble.com/Fitting-ELISA-measurements-unknowns-to-4-parameter-logistic-model-tp3252381p3711676.html Sent from the R help mailing list archive at Nabble.com.