[Biopython] quantile normalization method
Laurent Gautier
lgautier at gmail.com
Sat Mar 20 19:30:45 UTC 2010
On 3/20/10 7:26 PM, Vincent Davis wrote:
> @Laurent Gautier
>
> The algorithm is fairly straightforward, as you noted it, but beware
> of details such missing values, ability to normalize against a
> target distribution, or ties when ranking (although I'd have to
> check if those receive a special treatment).The quantile
> normalization code in the R package "preprocessCore" is in C and
> might outperform a pure Python implementation.
>
>
> Not sure about speed. I have 84 microarrays samples with ~190,000 probes
> and it normalizes in 7 sec. I have no idea how fast R is or how many
> arrays are common to normalize.
So speed is not an issue for your use-case; even a 10x speedup might not
justify the effort required to move to C, as this operation is performed
once in a while (once per dataset mostly).
I am not sure there is a "common" number. When still working with
arrays, I can find myself with several hundred arrays with ~2 million
probes each.
> There is a variety of normalization methods in bioconductor, and it
> might make sense to embrace it as a dependency (rather than
> reimplement it). I have bindings for Bioconductor up my sleeve about
> to be distributed to few people for testing. The public release
> might be around ISMB, BOSC time.
>
>
> I considered this and in the long run you might be right. But I don't
> know R and I placed more value on understanding the normalization than
> learning R. This is in part because there is little advantage in using R
> in the next steps of my analysis.
Surprising, but you'll know best.
> Bindings seem like a good idea but
> they would be a black box to me. I guess for me since most of this is
> new the value of implementing my own normalization in both learning more
> about python and understanding the normalization out ways the benefits
> of implementing it in R.
Everyone's mileage will vary. I often like building on existing
libraries (although I frequently read how methods work): this makes my
palette of tools richer than if I had to reimplement everything, and
gives me time to create my own.
Having this said, learning a language by implementing is a great way to go.
> As a side question, why use biopython, are there ways in which it is
> better than R ?
In short (and therefore with some imprecision and/or distortion),
Biopython is a "Python package" (i.e., collection of modules) for
bioinformatics, with a forte in handling a number of bioinformatics file
formats. R is a language for statistics, data analysis and graphics.
> For me it is purely that I know python (a little) and can nothing about
> R. Sure If I am just doing through step by step instruction from
> a bioconductor use manual I am fine but once I what to do something new
> am am lost. Not that I can't learn I am just prioritizing my learning.
Then the idea is that you consider R/bioconductor as a Python library.
Should you want something new, you can then implement it in Python.
Laurent
>
> And thanks for this
>
> norm_a = numpy.array(normq(m))
>
> can be replaced by
>
> norm_a = numpy.as_array(normq(m))
>
> to improve performances whenever m is of substantial size (as no
> copy is made - see
> http://rpy.sourceforge.net/rpy2/doc-2.1/html/numpy.html#from-rpy2-to-numpy)
>
>
>
>
>
> *Vincent Davis
> 720-301-3003 *
> vincent at vincentdavis.net <mailto:vincent at vincentdavis.net>
>
> my blog <http://vincentdavis.net> | LinkedIn
> <http://www.linkedin.com/in/vincentdavis>
>
>
>
> On Sat, Mar 20, 2010 at 12:05 PM, Laurent Gautier <lgautier at gmail.com
> <mailto:lgautier at gmail.com>> wrote:
>
> Hi Bartek and Vincent,
>
> Few comments:
>
> A/
>
> The algorithm is fairly straightforward, as you noted it, but beware
> of details such missing values, ability to normalize against a
> target distribution, or ties when ranking (although I'd have to
> check if those receive a special treatment).
> The quantile normalization code in the R package "preprocessCore" is
> in C and might outperform a pure Python implementation.
>
> B/
>
> There is a variety of normalization methods in bioconductor, and it
> might make sense to embrace it as a dependency (rather than
> reimplement it). I have bindings for Bioconductor up my sleeve about
> to be distributed to few people for testing. The public release
> might be around ISMB, BOSC time.
>
> C/
>
>
> norm_a = numpy.array(normq(m))
>
> can be replaced by
>
> norm_a = numpy.as_array(normq(m))
>
> to improve performances whenever m is of substantial size (as no
> copy is made - see
> http://rpy.sourceforge.net/rpy2/doc-2.1/html/numpy.html#from-rpy2-to-numpy
> )
>
>
>
> Best,
>
>
> Laurent
>
>
>
>
> On 3/20/10 5:00 PM, biopython-request at lists.open-bio.org
> <mailto:biopython-request at lists.open-bio.org> wrote:
>
> > Is there a quantile normalization method in biopython, I
> search but did not
> > find. If not it looks straight forward would it be of
> any interest to the
> > community for me to contribute a method
> >
> > 1. given n arrays of length p, form X of dimension
> > p ? n where each array is a column;
> > 2. sort each column of X to give X sort ;
> > 3. take the means across rows of X sort and assign this
> > mean to each element in the row to get X sort ;
> > 4. get X normalized by rearranging each column of
> > X sort to have the same ordering as original X
> >
> > From
> > A comparison of normalization methods for high
> > density oligonucleotide array data based on
> > variance and bias
> > B. M. Bolstad 1,?, R. A. Irizarry 2, M. Astrand 3 and T.
> P. Speed 4, 5
> > ?
> >
>
> Hi,
>
> I don't think there is such a method available.
>
> I'm myself using the original R implementation by Bolstad et al.
> It requires
> rPy and R installed. It can be achieved in a few lines of code:
>
> <pre>
> import rpy2.robjects as robjects
> #ll = list of concatenated values to normalize
> v = robjects.FloatVector(ll)
> #numrows=number of vectors that made up ll
> m = robjects.r['matrix'](v, nrow = numrows, byrow=True)
> robjects.r('require("preprocessCore")')
> normq=robjects.r('normalize.quantiles')
> norm_a=numpy.array(normq(m))
> #norm_a=normalized array
> </pre>
>
> If your method is a pure python implementation which is
> comparably fast I
> think it would be worth to have it in Biopython since the method
> is (in my
> opinion) quite useful and it would remove the dependency on R
> from some of
> my scripts.
>
> cheers
> Bartek
>
>
>
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