I am trying to detect gonadotropin-releasing hormone pulses for my research. I am having trouble finding suitable software for this. Does anyone have any suggestions on how or where I could find this?
There are good stats books on analysis of Time Series Data, which is what I assume you have.
But if you have continuous monitoring of the levels, ordinary eye-balling will show you where the pulses are. Simple convention for testing if a presumed "pulse" is a statistically significant increase in level, you need only compare the peak with the troughs before and after it with a non-parametric equivalent of the "t"-test.
Yes Colin, that was my initial thought as well but there is a little more that goes into it apparently. Because there is so much variability in the assays I need something that will tell me if maybe a collection of points that doesn't necessarily "look" like a pulse, is in fact really a pulse.
Waldemar, I tried the Rproject and it was very intimidating and not user friendly. I would have to find someone that has used it for what I need and maybe get the code for it.
I have software called Dynapeak that fits my needs but again it is not very user friendly. Here is a link to the paper that talks about Dynapeak:
I think PC-PULSAR software would be useful to you. You should consult Dr. Kenneth W. Wachter ([email protected]) or University of Illinois, Chicago, IL, USA. I have gone through a few recent papers referring University of Illinois in relation to this software.
You can calculate area under the curve and pulsatility by using the Cluster Analysis. More information at: Veldhuis JD, Johnson ML. Cluster analysis: a simple, versatile and robust algorithm for endocrine pulse detection. American Journal of Physiology 1986;250(4 Pt 1):E486–93.
Our lab is currently working with the Dynapeak software. However, I cannot tell you much about it because I have not worked with it yet, but I am told it has been working well.
Yang, Y. (2002), Detecting Change Points and Hormone Pulses Using Partial Spline Models, Ph.D. Thesis, University of California-Santa Barbara, Dept. of Statistics and Applied Probability.