[BioC] autocorrelation analysis

[Zeljko Debeljak]

Hi, Mattia.

I am not an expert in the field of nonlinear dynamics, deterministic
chaos, fractals etc but I know a little bit about it. You should tka a
look at the packages related to the chaotic (nonlinear) data series
and related analytical tools like false nearest neighbors, average
mutual information, Lyapunov exponent etc. There are few R packages
that contain related information and applications. I must warn you
that this area of mathematical science is very demanding (although
very appealing also). If you are very keen to start the journey read
the available basic texts before doing any experiments of yours. There
are many applications of these tools in the field of molecular
biology, and especially biochemistry. Hope this helps.

Zeljko Debeljak, PhD
Medical Biochemistry Specialist
Osijek Clinical Hospital

2009/5/25, mattia pelizzola <mattia.pelizzola at gmail.com>:
> Hi,
>
> I am trying to get some autocorrelation analysis based on positions of
> DNA words. Given a certain DNA motif or word, you can have its
> positions on the genome. I am trying to figure out if there is any
> regularity (pattern) in that. What I did is to determine pair-wise
> distances and see how many pairs have a certain distance. The result
> indicates that is more likely to have such words at a given relative
> distance than at others, but it is not so easy to interpret. Is there
> any way to test this? There is a lot of functions in R about testing
> autocorrelation on time-series data or 2D geographical data .. but
> these do not seem really appropriate here to me.
> Also, it would be nice to test weather two positions sets for two
> different motifs are correlated ..
>
> thanks for any suggestion!
>
> mattia
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>