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ProXimal pathway Enrichment Analysis
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# PxEA: ProXimal pathway Enrichment Analysis

Code for the ProXimal pathway enrichment analysis introduced in the "Targeting comorbid diseases via network endopharmacology" manuscript.

## Requirements

  • Python 2 or 3
  • numpy
  • scipy
  • networkx

## Installing & running tests

Download (i.e. clone) the files to your computer, you can use the package as a bare package (importing from the same folder) or install it using one of the following commands:

>>> python install


>>> pip install pxea

Several test cases for the methods are provided in test/ To run these, on the parent directory (where this README file resides) type

>>> python -m unittest test.test_pxea


>>> python test

It should give an output similar to below .. ---------------------------------------------------------------------- Ran 2 tests in 1.220s


## Usage

### PXEA

>>> from pxea.utilities.set_enrichment import get_enrichment_score_and_pval
get_enrichment_score_and_pval(ranked_list, candidates, n_random=n_random, alternative="greater", seed=51234)
""" KS based score (~max difference between cumulative distributions of the sample and expected random walk) """
Input parameters:
ranked_list: a list with the ranking of the elements (e.g., pathways proximal to a drug) candidates: set of elements (e.g., pathways common to two diseases) N: number of pathways in the candidates set (if None, len(candidates) will be used n_random: number of shufflings to the ranked list for permutation test based P-value calculation (if none, no pvalue is calculated and None is returned instead) alternative: greater | less | two-sided seed: number to be used initializing random generator (for reproducibility)
Returns enrichment score and pvalue

### Proximity

>>> from import calculate_proximity
calculate_proximity(network, nodes_from, nodes_to, nodes_from_random=None, nodes_to_random=None, bins=None, n_random=1000, min_bin_size=None, seed=51234, lengths=None)
""" Calculate proximity (average distance to the closest node from the first to second)from nodes_from to nodes_to (if degree binning or random nodes are not given, they are generated) """
Input parameters:
network: networkx Graph object nodes_from: set of nodes from which proximity is calculated nodes_to: set of nodes proximity to which is calculated nodes_from_random: random from nodes to check proximity nodes_to_random: random to nodes to check proximity bins: degree equivalence bins n_random: number of randomizations for background closest distance calculation min_bin_size: minimum size of the bins for degree binning if None, len(network) // 100 is used seed: integer for initializing the state of the random generator lengths: precalculated shortest path length dictionary
Returns proximity z-score and average path length to nearest nodes in nodes_to

## Data sets

The data sets used in the analysis for the autoimmune diseases are under data/ folder at [pxea](

## See also

See [toolbox]( package for various related code.

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