[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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