Author Topic: Standard Curve question  (Read 16682 times)

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Offline ahiscox

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Standard Curve question
« 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.

Standard Curve question
« on: November 29, 2007, 10:40:33 AM »

Offline ASluo

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Re: Standard Curve question
« Reply #1 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!

Offline researcher4life

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Re: Standard Curve question
« Reply #2 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

Offline aliu

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Re: Standard Curve question
« Reply #3 on: March 08, 2011, 01:58:18 PM »
For ELISA standard curves, the 4 parameter logistic (4PL) 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) 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) 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 and/or the 5 parameter logistic (5PL) curve fit.

Since we are on the topic, here is a blog post for Tips for ELISA Data Analysis.

I hope this information helps.

Allen Liu

* Disclaimer: I am a Hitachi Solutions employee.

Re: Standard Curve question
« Reply #3 on: March 08, 2011, 01:58:18 PM »