Dear all, Another newbie just got attracted to this mailing list.
I am a biologist currently working my way through R, had sort play around with python earlier this year. I have some data exhibiting periodicity ** my data consists of peaks and valleys, with peaks arising due to the presence of a repetitive structural unit,** with x being a reference grid (position along a chromosome) and y being strength of signal (this y signal fluctuates to give rise to the peaks and valleys). ie presence of a structural unit along a chromosome gives rise to a peak in my data. I would like to use a curve fitting algorithm (I guess something like a fourier analysis and/or splines). Due to the nature of the data I would like to look for periodicities at different scales (along the x grid). So say 2-4 different splines/curves are probably enough to describe the 40,000 occourences of the repetitive structural unit in my data, while say 4-6 of these units could exhibit certain patterns in the way they group together. I assume in my case, I can consider my x axis (position) to be equivalent to a time x axis as in signal processing. I considered using the FDA package (silverman and ramsey I think). Does anyone have an ideas if this is the right way to go or suggestions etc PS I have highlighted in the attached gif with red, the occourence of the repetitive signal (differences in the wavelength for example could be important but not more than 4 would be required to fit all data), and in yellow a hypothetical occourence of a periodicity in a different scale Thanks a lot Dr Triantafyllos Gkikopoulos The University of Dundee is a registered Scottish charity, No: SC015096
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