coincidencetest 0.1.39

An exact test for coincidence of feature values along a sample set.

This exact test assesses the statistical significance of finding a feature subset in binary feature data such that the number of simultaneously-positive samples is large.

Everything needed to perform the test is located in the self-contained module _coincidencetest.py.

Example

Install as usual with pip install . from inside the cloned repository directory.

Install from PyPI:

pip install coincidencetest

Usage is shown below:

import coincidencetest
from coincidencetest import coincidencetest
coincidencetest(2, [3, 3, 3, 3], 10)

0.0008877

This example shows that the probability is about 0.09% that four features, each occurring with frequency 3/10, will simultaneously occur in 2 or more samples.

The example coincidencetest(1, [5, 3, 7], 100) yields p=0.01047, showing that the probability of even just one sample having all features can be very low, provided that enough of the features are individually relatively rare.

CLI application

To make the test immediately useful, this package is distributed together with a lightweight "Formal Concept Analysis" feature set discovery tool.

The installed package exposes the command-line program coincidence-clustering incoporating this tool. Use it like so:

coincidence-clustering --input-filename=example_data/bc_cell_data.tsv --output-tsv=signatures.tsv --level-limit=100 --max-recursion=4

Web application

A Javascript port of the signature discovery and testing program is located in webapp/. To run it locally, use:

cd webapp/
chmod +x build.py
./build.py
python -m http.server 8080

Then open your browser to localhost:8080 or 0.0.0.0:8080.

Note: The Javascript application only requires the server to have the capability of serving static files, namely the files index.html and worker.js created by the build process. However, most browsers block the use of the "web workers" from the local file system, so this minimal Python server is needed for local deployment. We use web workers in order to allow dynamic display of feature sets in real-time as they are identified.

Code testing

The package is tested with

pytest .

The key step is a computation of the number of covers of a set of a given size by sets of prescribed sizes (equivalently, the number of subsets of prescribed sizes without common intersection), so the most important tests check that several different algorithms for cover counting agree in small-number cases.