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Project Description
Aclust
======
Streaming agglomerative clustering with custom distance and correlation


*Agglomerative clustering* is a very simple algorithm.
The function `aclust` provided here is an attempt at a simple implementation
of a modified version that allows a stream of input so that data is not
required to be read into memory all at once. Most clustering algorithms operate
on a matrix of correlations which may not be feasible with high-dimensional
data.

`aclust` **defers** some complexity to the caller by relying on a stream of
objects that support an interface (I know, I know) of:

obj.distance(other) -> numeric
obj.is_correlated(other) -> bool

While this does add some infrastructure, we can imagine a class with
position and values attributes, where the former is an integer and the
latter is a list of numeric values. Then, those methods would be implemented
as:

def distance(self, other):
return self.position - other.position

def is_correlated(self, other):
return np.corrcoef(self.values, other.values)[0, 1] > 0.5

This allows the `aclust` function to be used on **any** kind of data. We can
imagine that distance might return the Levenshtein distance between 2 strings
while is\_correlated might indicate their presence in the same sentence or in
sentences with the same sentiment.

Since the input can be- and the output is- streamed, it is assumed the the objs
are in sorted order. This is important for things like genomic data, but may be
less so in text, where the max\_skip parameter can be set to a large value to
determine how much data is kept in memory.

See the function docstring for examples and options. The function signature is:

aclust(object\_stream, max\_dist,
max\_skip=1, linkage='single', multi\_member=False)

It yields clusters (lists) of objects from the input object stream.

`multi\_member` allows a feature to be a member of multiple clusters as long as
it meets the distance and correlation constraints. The default is to only
allow a feature to be added to the *nearest* cluster with which it is
correlated.

Uses
====

+ Clustering methylation data which we know to be locally correlated. We can
use this to reduce the number of tests (of association) from 1 test per CpG,
to 1 test per correlated unit.
See: https://github.com/brentp/aclust/blob/master/examples/methylation-clustering-asthma.py for a full example.

```
chrom start end n_probes probes asthma.pvalue asthma.tstat asthma.coef
chr1 566570 567501 8 chr1:566570,chr1:566731,chr1:567113,chr1:567206,chr1:567312,chr1:567348,chr1:567358,chr1:567501 0.4566 -0.74 -0.06
chr1 713985 714021 3 chr1:713985,chr1:714012,chr1:714021 0.1185 -1.56 -0.13
chr1 845810 846195 3 chr1:845810,chr1:846155,chr1:846195 0.5913 0.54 0.04
chr1 848379 848440 3 chr1:848379,chr1:848409,chr1:848440 0.3399 -0.95 -0.06
chr1 854766 855046 7 chr1:854766,chr1:854824,chr1:854838,chr1:854918,chr1:854951,chr1:854966,chr1:855046 0.7482 -0.32 -0.02
chr1 870791 871546 8 chr1:870791,chr1:870810,chr1:870958,chr1:871033,chr1:871057,chr1:871308,chr1:871441,chr1:871546 0.2198 -1.23 -0.11
chr1 892857 892948 3 chr1:892857,chr1:892914,chr1:892948 0.2502 -1.15 -0.05
chr1 901062 901799 5 chr1:901062,chr1:901449,chr1:901685,chr1:901725,chr1:901799 0.6004 0.52 0.04
chr1 946875 947091 4 chr1:946875,chr1:947003,chr1:947018,chr1:947091 0.9949 0.01 0.00
```
So we can filter on the asthma.pvalue to find regions associated with asthma.


INSTALL
=======

`aclust` is available on pypi, as such it can be installed with:

pip install aclust


Acknowledgments
===============

The idea of this is taken from this paper:

Sofer, T., Schifano, E. D., Hoppin, J. A., Hou, L., & Baccarelli, A. A. (2013). A-clustering: A Novel Method for the Detection of Co-regulated Methylation Regions, and Regions Associated with Exposure. Bioinformatics, btt498.

The example uses a pull-request implementing GEE for python's statsmodels:
https://github.com/statsmodels/statsmodels/pull/928
Release History

Release History

0.1.3

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