Methods and Techniques Discussion => Enzyme Linked ImmunoSorbentAssay (ELISA) => Topic started by: ahiscox on November 29, 2007, 10:40:33 AM

Title: Standard Curve question
Post by: ahiscox on November 29, 2007, 10:40:33 AM
I've been doing ELISAs for several years as a grad student, and I've always used a linear best fit line for my standard curve.  I'm currently trying to troubleshoot an ELISA that someone else has done, and noticed that he was using a 4-parameter standard curve.  When I do a linear line on all of his data, I get r-squared values that are a bit lower than his 4-parameter best fit lines.
 

Is a 4-parameter best fit line OK to use?  I've always been told to use a linear line, but when I use a linear line on his raw data, I get slightly smaller r-squared values (.9978 vs. .9789 for example).

Thanks.
Title: Re: Standard Curve question
Post by: ASluo on February 24, 2008, 02:10:46 AM
HI there,

You can certainly use a 4-parameter best fit. But I think it depends on what the company you order the product from suggests. I order my ELISA kits from R & D Systems (Duoset) and was recommended to use a 4-parameter fit. I think is important to follow the company's instructions to get the best results! Good Luck!
Title: Re: Standard Curve question
Post by: researcher4life on April 19, 2010, 04:01:46 PM
For future reference, check out this step-by-step guide for generating a standard curve in Excel.

http://www.mdbioproducts.com/resources/protocols/standard-curve (http://www.mdbioproducts.com/resources/protocols/standard-curve)
Title: Re: Standard Curve question
Post by: aliu on March 08, 2011, 01:58:18 PM
For ELISA standard curves, the 4 parameter logistic (4PL) (http://www.miraibio.com/blog/2010/08/the-4-parameter-logistic-4pl-nonlinear-regression-model/) is definitely better than a linear fit.  A linear fit has no limits (i.e. maximum and minimum asymptotes) which does not accurately describe biological systems.  I would even go a step further and say that the 5 parameter logistic (5PL) (http://www.miraibio.com/blog/2009/02/5-pl-logistic-regression/) is an even better fit than the 4PL because the 4PL assumes symmetry around the inflection point where the 5PL does not.  In addition, I would not recommend using R^2 to compare linear vs. nonlinear fits because it is like comparing apples to oranges.  The R^2 is good for comparing linear fits to each other but it is not accurate for nonlinear regression curves like the 4PL and 5PL.  Instead, I would recommend using the root mean square error (RMSE) (http://en.wikipedia.org/wiki/Root_mean_square_deviation) as a goodness of fit.

Here are some blog posts for those that are curious in learning more about the 4 parameter logistic (4PL) curve fit (http://www.miraibio.com/blog/2010/08/the-4-parameter-logistic-4pl-nonlinear-regression-model/) and/or the 5 parameter logistic (5PL) curve fit (http://www.miraibio.com/blog/2009/02/5-pl-logistic-regression/).

Since we are on the topic, here is a blog post for Tips for ELISA Data Analysis (http://www.miraibio.com/blog/2009/06/tips-for-data-analysis/).

I hope this information helps.

Allen Liu

* Disclaimer: I am a Hitachi Solutions employee.