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- interpolate x values from a standard curve

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Aug 4, 2015 9:09 PM
(3741 views)

Hello,

We have five control samples with known concentration and a quantitative assay result. Using the controls, we can draw a standard curve so that the concentration is represented on the x-axis and the quantitative assay result is on the y axis. The data is expected to be linear. I have clinical samples with quantitative assay result, but unknown concentration. I would like to determine the concentration of the clinical samples based on the quantitative assay result.

Using the fit Y by X function, I fit a line and obtain the regression line equation. I can solve the equation for x and set a formula in jmp to solve for x.

The challenge is that for each clinical run I will have a standard curve for each fluorophore, and a new standard curve for each new clinical run. Therefore, I will have different equations and will need to write different formula to solve for x.

Is there a more automated way for me to solve for x? I have used GraphPad prism in the past (describe in this link: GraphPad - FAQ 1753 - Prism 3 -- Calculating "Unknown" Concentrations using a Standard Curve)and I would imagine that there is a more straightforward way for me to interpolate my x values with JMP. I would really appreciate a step by step procedure as I am a relatively new JMP user. Thank you!

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Accepted Solutions

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Aug 5, 2015 8:49 AM
(5529 views)

Solution

Use the "nonlinear" platform under "analyze" and "modeling".

After fitting curves, you can use the "Custom Inverse Prediction" for interpolating "x" values.

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Aug 5, 2015 1:33 PM
(5528 views)

Solution

Hello,

Inverse prediction is available is several platforms including fit model.

Best,

Bill

2 REPLIES

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Aug 5, 2015 8:49 AM
(5530 views)

Use the "nonlinear" platform under "analyze" and "modeling".

After fitting curves, you can use the "Custom Inverse Prediction" for interpolating "x" values.

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Aug 5, 2015 1:33 PM
(5529 views)

Solution

Hello,

Inverse prediction is available is several platforms including fit model.

Best,

Bill