I'm very much a novice to working working with elisas! From what I gather, it is usually unwise to use linear regression to interpolate data and a 4-parameter logistic fit may be a better choice. Is this possible to do in excel? Or can someone recommend some software?

Excel seems to be fairly poor for anything other than basic stats (from my limited and software ignorant position). Do you have SPSS or Prism? Sadly I came from a background of having wonderful software linked to our reader which involved me plugging in my plate plan and it giving me all the data.

Beware, this is wonderful for giving you effortless answers but rubbish if you want to be able to do it in the event of having a more basic setup when you don't know how to calculate the concentrations yourself (as I am experiencing now).

The 4-parameter logistic assumes symmetry around the inflection point. A better option would be the 5-parameter logistic which takes aymmetry into account (hence the 5th parameter) which is a better fit for bioassays. Here is a blog post for detail on the 5-parameter logistic model equation.

We do offer an elisaanalysissoftware, MasterPlex ReaderFit, which includes both the 4-PL and 5-PL model equations as well as a couple of others in addition to weighting algorithms. I would like to welcome you to try out our free 14-day trial.

Another great thing about the 4-PL (if you are doing dose response curves) is that the C parameter represents your EC50 or IC50 value depending on the type of assay you are doing. For the 5-PL, the EC50/IC50 would be a function of the followin:

x = c(2^(1/e)-1)^(1/b)

c = inflection point on the curve (same as in 4PL)

I think
Prism and other softwares are good but in my experieance For 4 -parameter logistic Softmax Pro is very good software this is available on molecular device site for 60 days free demo

Hitachi Solutions just recently launched a free online ELISAanalysis application called ReaderFit.com. It fully supports the 4 parameter logistic (4PL) and 5 parameter logistic (5PL) models with weighting options. ReaderFit.com also interpolates and extrapolates unknown values.

The 4PL and 5PL algorithms are 8+ years in the making and has gone through several iterations since it's initial launch in our MasterPlex QT multiplexanalysissoftware.

The registration process is simple: If you are already logged into Google it only takes one click to register and you can begin analysis immediately. If not, all you need is an email address and password to begin using the application.

If you have any questions regarding ELISAanalysis or usage of ReaderFit.com, I'd be more than happy to assist you. You can reach me at aliu [at] miraibio [dot] com.

plot(stdcrvdata$logconc, stdcrvdata$OD, main="log standard curve", xlab="x=log(conc)", ylab="y=OD")

When you plot the data you can see the top of the curve which equals parameter a and the bottom is parameter d. The inflection point on the curve is parameter c and b is the degree of curvature. Use these values for seeding your parameters in the next step. Play with the equation in excel to see what changing the numbers does.

Next step is the non-linear fit, the seeded/starting parameters are in start=list(). If you pick bad ones your fit will fail. I am using cc instead of c because I think c is a reserved letter (not sure).

################## fit the data ##################
fit <- nls(OD ~ d+(a-d)/(1+(logconc/cc)^b), data=stdcrvdata, start=list(d=0.2, a=1.5, cc=2, b=-11), trace=TRUE)
summary(fit)

Then lets plot the fit. Look at your original plot and find the lowest and highest x values you want to use. We make a list of numbers (mine go from .5 to 4.5)

################ Plot the results ################
#this lets you graph your calculated equations nice and pretty
x <- seq(.5,4.5, length=100)
y <- (coef(fit)["d"]+(coef(fit)["a"]-coef(fit)["d"])/(1+(x/coef(fit)["cc"])^coef(fit)["b"]))

Then add a red line showing your fit to your already generated plot

lines(x,y, lty="dotted", col="red")

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.

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. Then we solve using the equation, convert the calculated log concentration to concentration and our answer is returned to us. The write.table outputs our data in a .csv format for inputting into excel.

Finally, a little bit of checking, we plot our calculated data on our chart (in blue): lines(samples$loganswer,samples$OD, type="points", col="blue")

Note that one of my samples (0.19) didn't return a value. This is because an OD of 0.19 lies outside of the curve I calculated.

Some problems that can occur:
You are dividing by zero somewhere, move your numbers around ie take the log and add 10.
stdcrvdata$logconcplus <-stdcrvdata$logconc + 10

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. bottom parameter = d = 2 and you are trying to calculate the concentration when an OD = 1.9). In my example I have one of these points and the result is NaN (a complex number).

You don't have enough data points, this will cause a fail in the curve fitting.

You need to pick better seeding/starting values for parameters.

########## cross posting #########

Please feel free to cross post this solution to other forums.

Since your data are already in excel if find it more practicle to have the calculations in Excel. I have made an excel plugin that does four parameter logistic as well as linear, point to point, five parameter logistics second order polynomial and third order polynomail fitting. If this is not enough you can also add your own fit formula. It is specially made for ELISA and other immunoassays.You can then see for yourself what fit is the best. The program is called IVDfit and the software and a trial version can be downloaded from www.ivdtools.com.

The linear regression is a bad way to fit a standard curve as bioassays (like ELISA) nomrally have a sigmoidal curve OD vs. concentration. To fit a good standard curve 2 algorithms have been developed, the 4 parameter logistics for symmetrical curves and the 5 parameter logistics for asymmetrical curves.
Compare the goodness of curve fit of your calibration via the SSE (sum of squared errors) and you will see which model fits your curve best. Do not extrapolate outside the range of your calibrators.

You can download a free program for non-commercial use which I wrote in Excel from here :

Hi elisa model...could I ask you from: When you plot the data you can see the top of the curve which equals parameter a and the bottom is parameter d. The inflection point on the curve is parameter c and b is the degree of curvature. Use these values for seeding your parameters in the next step. Play with the equation in excel to see what changing the numbers does.

How do you get the values?? I was able to plot the data in R (although without a line) but no idea how to get the values...specially the slope one. Could you please explain a little further?

If you have plotted the raw data, you can look on the graph at the highest points, read across to the y-axis and whatever that value is, you use that as the seed value (a)
You do the same thing for the lower value, read it off the y-axis after you have plotted your data points. (d)
To find the inflection point c on the curve you look at your curve, find the spot midway between the top and bottom, and this time read down to the x-axis and whatever value that is, use it as your seed value. (cc)
The b parameter isn't obvious from the curve, I would use what's in my example (-11).
If you run the code example below (taken from my previous example), you can see where I get the numbers, on the y-axis the top and bottom are ~1.2 and 0.2, and the x-axis midpoint is about 2.1ish. In the code I used 1.5, 0.2 and 2. The parameters from the solution are a 1.3, d 0.2, cc 1.96 and b -5.2

############### Data input ###############
#input the data
stdcrvdata<-data.frame(conc=c(3,10,30,100,300,1000,3000,10000,30000), OD=c(.2,.22,.41,.75,1.07,1.18,1.2,1.27,1.29))
stdcrvdata$logconc <-log10(stdcrvdata$conc)
plot(stdcrvdata$logconc, stdcrvdata$OD, main="log standard curve", xlab="x=log(conc)", ylab="y=OD")
############### fit the data ###############
fit <- nls(OD ~ d+(a-d)/(1+(logconc/cc)^b), data=stdcrvdata, start=list(d=0.2, a=1.5, cc=2, b=-11), trace=TRUE)
summary(fit)
############### Plot the results ###############
#this lets you graph your calculated equations nice and pretty
x <- seq(.5,4.5, length=100)
y <- (coef(fit)["d"]+(coef(fit)["a"]-coef(fit)["d"])/(1+(x/coef(fit)["cc"])^coef(fit)["b"]))
lines(x,y, lty="dotted", col="red")

I am trying to create an R program to automatically take the x and y standard curve values generated on softmax pro. I am trying to compare the fits generated by the masterplex readerfit 4PL fit and the one generated by R using your solution. However, I keep getting this error:

Error in nlsModel(formula, mf, start, wts) :

singular gradient matrix at initial parameter estimates

Also, I tried using the inbuilt function LL2.4() in drc package, but the fitted coefficients are very different from the masterplex results.

Without going into your dataset there are two things I can think about that cause "singular gradient matrix at initial parameter estimates".
1. try different initial parameter estimates. You can also tackle this by fixing one of the parameters in your model and trying to fit the rest. Do this by assigning say b an initial value, and don't include b in your start=list(). Now b is a fixed parameter.
2. the nls alogorithm fails with a perfect data set, you must have a bit of noise.

Someone gave me this note with regards to adding some noise in R. Otherwise you can manually add noise, add it in excel etc, whatever solution works easiest for you.

# the equation is y=a*x^b
# the runif() function add some noise, since nls doesn't like zero-residue models ;-)
my.x<-seq(1,10,1)
my.y<-3*my.x^2+runif(10,-10,10)

As a Scientist Solutions member, you are able to register a positive vote for any topic which you believe is useful and relevant to our board or any reply which you believe is especially well worded and helpful.

By participating in the voting, you will be helping to identify the best topics & replies on the board.

You may vote once for any one post, and you may not vote for your own posts.

A post (topic or reply) will earn one "thumbs up" icon for every 10 votes received (up to 3 thumbs up), and the person who made the post will also earn two bonus points.