[SciPy-User] FW: curve fitting by a sum of gaussian with scipy
Charles R Harris
charlesr.harris@gmail....
Thu Apr 18 09:59:04 CDT 2013
On Thu, Apr 18, 2013 at 6:24 AM, Stéphanie haaaaaaaa <
flower_des_iles@hotmail.com> wrote:
> Dear all,
>
>
> I'm doing bioinformatics and we map small RNA on mRNA. We have the mapping
> coordinate of a protein on each mRNA and we calculate the relative distance
> between the place where the protein is bound on the mRNA and the site that
> is bound by a small RNA.
> I obtain the following dataset :
>
>
> dist eff-69 3-68 2-67 1-66 1-60 1-59 1-58 1-57 2-56 1-55 1-54 1-52 1-50 2-48 3-47 1-46 3-45 1-43 10 11 22 123 184 185 136 97 78 59 310 113 214 315 216 217 218 219 220 221 322 124 125 126 128 231 138 140 2
>
>
> When i plot the data, i have 3 pics : 1 at around 3/4 another one around
> 20 and a last one around -50. (see attached file, upper graph)
>
> I try cubic spline interpolation, but it does'nt work very well for my
> data (see attached file 2, red curve).
> My idea was to do curve fitting with a sum of gaussians. For example in my
> case, estimate 3 gaussian curve around the peak (at point 5,20 and -50).
> How can i do so ?
> I looked at scipy.optimize.curve_fit(), but how can i fit the curve at
> precise intervalle ? How can i add the curve to have one single curve ?
>
>
That's interesting. On thinking about it, I think if you used the design
matrix for, say, fitting a uniform spline with fairly closely spaced sample
points, that it would be pretty singular, which would be a good thing
because the pseudo inverse would minimize the sum of squares of the
coefficients, which in turn would knock down the curve where there is no
data. Mind, I'm just speculating here, haven't tried it. Is the data you
posted complete?
Chuck
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