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