[R] Curve fitting,FDA for biological data

[spencerg]

Dear Dr Gkikopoulos: 


      1.  Have you looked at "bioconductor.org"?  They have substantive 
extensions to R specifically for "genomic data", which I assume would 
include chromosome. 


      2.  To "identify periodicities at different timescales", I agree 
with Stephen that "spectrum" would likely help. 


      3.  The best software to "fit data into discrete number of curves" 
depends on the particular "discrete number of curves" you want to 
consider and how you want to "fit data into" them.  A finite Fourier 
series could be the best tool IF the the multiple periodicities are all 
integer fractions of a common scale.  In that case, using a "fourier" 
base in the "fda" package could be your method of choice.  Otherwise, 
you might consider Bayesian Model Averaging.  RSiteSearch("Bayesian 
Model Averaging") produced 80 hits for me just now, and 
RSiteSearch("Bayesian Model Averaging", "function") produced 60.  
"RSiteSearch.function" in the "RSiteSearch" package [available via 
install.packages("RSiteSearch",repos="http://r-forge.r-project.org")] 
told me that 27 of the 60 were in the "ensembleBMA" package, and another 
14 were in the "BMA" package. 


      4.  The best way to "compare data from different experiments" 
depends on your evaluation of "3" above.  The "fda" package includes an 
"fRegress" function that might be useful. 


      Hope this helps. 
      Spencer Graves
      

trias wrote:
> There are a couple of different goals for this projects
>
>  *identify periodicities at different timescales (ie different dT)
>  *fit data into discrete number of curves, ie 6 different basic functions
> should be enough to describe the basic repeating elements in this data (ie 6
> different categories of peaks)
>  *comapre data from different experiments of the same "time" reference (in
> my case this is location on chromosome) for changes in the underlying basic
> elements (ie changes of the basic funtions,periodicity etc)
>
>  I think if I can find a strategy to answer some of these question I be in a
> good position to explore this data analysis further if needed.
>
>  Thanks a lot
>
>
>
> stephen sefick wrote:
>   
>> What is your end goal?  If it is to try and account for the
>> variability of the "timeseries" you may want to look at ?spectrum
>> If it is to model the periodicity...
>>
>> Stephen Sefick
>>
>> On Fri, Apr 3, 2009 at 11:30 AM, trias <t.gkikopoulos at dundee.ac.uk> wrote:
>>     
>>> Here is the gif that didn't come through earlier
>>> http://www.nabble.com/file/p22870832/signal.gif signal.gif
>>> --
>>> View this message in context:
>>> http://www.nabble.com/Curve-fitting%2CFDA-for-biological-data-tp22868069p22870832.html
>>> Sent from the R help mailing list archive at Nabble.com.
>>>
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>>
>> -- 
>> Stephen Sefick
>>
>> Let's not spend our time and resources thinking about things that are
>> so little or so large that all they really do for us is puff us up and
>> make us feel like gods.  We are mammals, and have not exhausted the
>> annoying little problems of being mammals.
>>
>> 								-K. Mullis
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>>     
>
>