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mdbioproduct教你用excel做elisa的标准曲线(很详细但必须慎用)

(2011-02-18 15:56:28)
标签:

elisa

标准曲线

杂谈

分类: 实验基本知识

MD Bioproducts Resource: Generating an ELISA standard curve in Excel:
If software capable of generating a 4-Parameter Logistic curve fit is unavailable, a standard curve using linear regression analysis in excel can be used.

Entering Data into Microsoft Excel

Step 1.

After Optical Densities (ODs) have been collected, enter each standard OD into a worksheet as shown.

http://www.mdbioproducts.com/sites/default/files/elisas/excel1.jpg

Step 2.

Take the averages of the duplicate values either by adding them up and dividing by two on a calculator, or entering the formula =AVERAGE(A2+B2). Note: change the cell location from A2 and B2 to the desired cell location of each Standard OD.

http://www.mdbioproducts.com/sites/default/files/elisas/excel2.jpg

 Step 3.

Enter the Standard concentrations that each OD should represent next to their corresponding OD average.

 http://www.mdbioproducts.com/sites/default/files/elisas/excel3.jpg

Creating a Standard Curve

Step 1.

Highlight your data range and click on the "Chart Wizard" icon in the excel tool bar.

http://www.mdbioproducts.com/sites/default/files/elisas/excel4.jpg

Step 2.

Choose XY (Scatter) and click "Next"

http://www.mdbioproducts.com/sites/default/files/elisas/excel5.jpg

Step 3.

Click on "Series" located at the top of the Chart Wizard Window.

  • Make sure that your X Values are your concentrations and your Y values are your Average OD values.
  • Name your Series or add another Series if you want to compare Standard Curves
Click "Next"
http://www.mdbioproducts.com/sites/default/files/elisas/excel6.jpg

Step 4.

Add information to your chart such as titles and axis labels. Once you customize the chart to your preferences, click "Next".

http://www.mdbioproducts.com/sites/default/files/elisas/excel7.jpg

Step 5.

Place your chart on a new Excel sheet or insert into your current worksheet.

http://www.mdbioproducts.com/sites/default/files/elisas/excel8.jpg

Step 6.

Double click on the x-acis values and a "Format Axis" window will pop up. Click on the "Scale" tab on the top of the window. Check the "logarithmic scale" box.

http://www.mdbioproducts.com/sites/default/files/elisas/excel9.jpg

Step 7.

Do the same to the y-axis

http://www.mdbioproducts.com/sites/default/files/elisas/excel10.jpg

Step 8.

Highlight data points by clicking on one of them. Click on the "Chart" tab and on the top menu bar click on "Add Trendline".

http://www.mdbioproducts.com/sites/default/files/elisas/excel12.jpg

Step 9.

A "Add Trendline" window will appear. Select Linear under "Trend/Regression type".

http://www.mdbioproducts.com/sites/default/files/elisas/excel16.jpg

Step 10.

Go to the "Options" tab on the top of the window and Select "Display equation on chart" and "Display R-squared value on chart"

http://www.mdbioproducts.com/sites/default/files/elisas/excel13.jpg

Step 11.

Click "OK" to view your chart with equations.

http://www.mdbioproducts.com/sites/default/files/elisas/excel14.jpg

Analyzing Data

Now that you have your equation, you can solve for "x" and enter your sample OD values as Y. You can do this by using a calculator and manually solving for "X" or by inputing a formula as shown.

Use Excel Equation: y=0.062x+0.1579

Enter Formula: =(G7-0.1579)/0.0062)

where G7 is your OD value (select the appropriate cell for your data)

http://www.mdbioproducts.com/sites/default/files/elisas/excel15.jpg

Note: Linear regression on Excel should only be used if software that can create a 4-Parameter (4-PL) logistic curve fit is unavailable. Please keep in mind that values that are represented at the low end of your curve will not be as reliable as values at the higher end of your curve.

Sample Data comparing standard curve values using 4-PL vs Linear regression.

Concentration (ng/mL) Mean OD 4-PL (ng/mL) Linear regression (ng/mL)
300 1.996 300.05 296.44
150 1.153 150.42 160.47
75 0.649 74.33 79.18
37.5 0.392 37.46 37.73
18.75 0.268 19.72 17.73
9.38 0.198 9.26 6.44
4.69 0.165 4.15 1.11

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