I'm going to be involved in a massive screening of physiological properties of yeast. I will generate thousands of growth curves that have to be analyzed in this way:
Normalize data (I will do a personal script for this)->Graph representation->Parameters from Gompertz parametrized equation (lag, growth rate, and maximum OD) and Area Under Curve (AUC).
I'm interested to obtain all these data in an automatic manner using any script performed in R or Python.
Any suggestion for that? Which kind of script are available?
You could use a loop. For R i have used a for loop.
from Cran : http://cran.r-project.org/doc/manuals/R-lang.html#for
"The syntax of the for loop is
for ( name in vector )
statement1
where vector can be either a vector or a list. For each element in vector the variable name is set to the value of that element and statement1 is evaluated. A side effect is that the variable name still exists after the loop has concluded and it has the value of the last element of vector that the loop was evaluated for. "
The vector list should be the list of the curves you want to do.
Hi Davis,
You can use the script as Benjamin told you...the for () comand is not complicated and good references are Zuur et.al (2009). A Beginner’s Guide to R.!....they have a detailed example of using for () and to include the function () (of corse you can do both). If you have interest in other code I have found this paper:
"A functional growth model with intraspecific competition applied to sea urchins". Grosjean, Ph., Ch. Spirlet & M. Jangoux, 2003, Can. J. Fish. Aquat. Sci., 60:237-246.
where the author in his personal page have uploaded the script, I attached it in a zip file (...Houpfully will be available for you....I'm young in Reseacrh Gate). Anyway its wabe page is: http://www.sciviews.org/_phgrosjean/
He has many interesting package for R.
I hope this will help to you!....
Luck!
For what purposes do you plan to use the data normalization. In the analysis of growth curves, you can not consider the data as a homogeneous sample. Perhaps it would be better to use model parametric identification methods.
When I say normalize data I want to mean, try to solve the different initial OD in each sample. Normalization -> ODt/ODi where ODt is the OD in each time and ODi is the initial OD.
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