Please feel free to cross post this solution to other forums.It is worth noting a little question mark down in the bottom left, of all of these options screens will bring up the very intuitive Prism help screen in order to understand further what these tests will be doing to your data. You need to pick better seeding/starting values for parameters. You don't have enough data points, this will cause a fail in the curve fitting. In my example I have one of these points and the result is NaN (a complex number). bottom parameter = d = 2 and you are trying to calculate the concentration when an OD = 1.9). You are taking the square root of a negative number - for example when solving for OD's where they lie outside the curve you have generated (i.e. Stdcrvdata$logconcplus <-stdcrvdata$logconc + 10 You are dividing by zero somewhere, move your numbers around ie take the log and add 10. This is because an OD of 0.19 lies outside of the curve I calculated. Note that one of my samples (0.19) didn't return a value. Lines(samples$loganswer,samples$OD, type="points", col="blue") Write.table(samples,file="mydata.csv",sep=",")įinally, a little bit of checking, we plot our calculated data on our chart (in blue): Then we solve using the equation, convert the calculated log concentration to concentration and our answer is returned to us. Using the equation above and the parameters determined from fitting our model (stored in the object fit) we input our measured OD data into a dataframe called samples. Next we have our unknowns, you can take the parameters calculated from R and use the equations in excel, or this is the R code. Then add a red line showing your fit to your already generated plot #this lets you graph your calculated equations nice and pretty Look at your original plot and find the lowest and highest x values you want to use. I am using cc instead of c because I think c is a reserved letter (not sure). Next step is the non-linear fit, the seeded/starting parameters are in start=list().
Play with the equation in excel to see what changing the numbers does. Use these values for seeding your parameters in the next step. The inflection point on the curve is parameter c and b is the degree of curvature.
When you plot the data you can see the top of the curve which equals parameter a and the bottom is parameter d. Plot(stdcrvdata$logconc, stdcrvdata$OD, main="log standard curve", xlab="x=log(conc)", ylab="y=OD") Stdcrvdata$logconc <-log10(stdcrvdata$conc)
Get your data into a dataframe and take the log of the concentration (R code in red): X is log concentrations, y is OD signal read from your instrument.
You can fit an ELISA curve using free software called R. Where is the 4 parameter option in GraphPad Prism? Do i really have to do 4 parameter logistics or linear regression is also fine (the values i get are copmpletely different).Ģ.
I can also use SPSS for the data analysis, but i am not sure whether that choice will be available plus i will have to learn to use the program from scratch.ġ. Does it have any other name (synonym) or is there a specific equation that i can fill in in custom analysis? And if there is one, there are also some A,B,C,D values i have to fill in that i dont know anything about. I know it belongs in the non linear category (i think) but its not in the drop down menu of available analysis. Up to now i was using linear regression for getting the values, but according to the manufacturer this is not the correct way to process the data. The data type are simple and straight forward, meaning i get some optical density values from samples of known concentration and from that i interpolate to find the values of samples of unknown concentration (cytokines). I am using GraphPad Prism 4 for the statistics and i cant find anywhere this type of analysis - it simply isnt there.
I am doing research ELISA (I am a medical doctor) and the kit manufacturer says on the manual that i should do 4 parameter logistic analysis of the results i get. Dont know mutch about statistics and i thought some of you might be able to help.