Metadata-Version: 2.1
Name: hierdiff
Version: 0.1.2
Summary: Clustering high-dimensional instances (e.g. T cell receptors) and testing whether clusters of instances are differentially abundant in two or more categorical conditions, with interactive tree visualization.
Home-page: https://github.com/FredHutch/hierdiff
Author: Andrew Fiore-Gartland
Author-email: agartlan@fredhutch.org
Maintainer: Andrew Fiore-Gartland
Maintainer-email: agartlan@fredhutch.org
License: MIT
Description: # hierdiff
        
        [![Build Status](https://travis-ci.com/agartland/hierdiff.svg?branch=master)](https://travis-ci.com/agartland/hierdiff)
        [![PyPI version](https://badge.fury.io/py/hierdiff.svg)](https://badge.fury.io/py/hierdiff)
        [![Coverage Status](https://coveralls.io/repos/github/agartland/hierdiff/badge.svg?branch=master)](https://coveralls.io/github/agartland/hierdiff?branch=master)
        
        A package that is useful for clustering high-dimensional instances (e.g. T cell receptors) and testing whether clusters of instances are differentially abundant in two or more categorical conditions. The package provides d3/SVG rendering of scipy hierarchical clustering dendrograms with zooming, panning and tooltips. This uniquely allows for exploring large trees of datasets, conditioned on a categorical trait.
        
        ## Installation
        
        ```
        pip install hierdiff
        ```
        
        ## Example
        
        ```python
        import hierdiff
        from scipy.spatial.distance import squareform
        
        """Contains categorical variable column 'trait1' and
        instance counts in 'count'"""
        dat, pwdist = generate_data()
        
        res, Z = hierdiff.hcluster_tally(dat,
        				                  pwmat=squareform(pwdist),
        				                  x_cols=['trait1'],
        				                  count_col='count',
        				                  method='complete')
        
        res = hierdiff.cluster_association_test(res, method='fishers')
        
        """Plot frequency of trait at nodes with p-value < 0.05"""
        html = plot_hclust_props(Z, title='test_props2',
                                    res=res, alpha=0.05, alpha_col='pvalue')
        ```
        
        ![example](https://raw.githubusercontent.com/agartland/hierdiff/master/example_hier_props.png)
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
