pyMarkovClustering 0.1.1
Markov Clustering in Python
License: MIT
Requires Python: >=3.9
Keywords: bioinformatics, clustering, markov-clustering, network-graph

# pyMarkovClustering

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## Table of contents

- [Overview](#overview)
- [Installation](#installation)
- [API Usage](#api-usage)
- [CLI Usage](#cli-usage)

## Overview

pyMarkovClustering is a python package for Markov Clustering (MCL) and its graph visualization.
While there is already a python library [markov_clustering](https://github.com/GuyAllard/markov_clustering) that implements the MCL algorithm,
it has not been maintained for a long time and lacks some functionality.
pyMarkovClustering was developed to enable easy MCL and graph visualization from edges consisting of a list of source, target, and weight.

> [!NOTE]
> pyMarkovClustering adjusts matrix preprocessing and parameters to achieve the same results as [mcl](https://github.com/micans/mcl) command-line tool.
> This library uses scipy sparse matrix for its MCL implementation and can cluster simple graphs with a few thousand nodes without any problems.
> However, if you need to cluster a complex graph with tens of thousands of nodes or more,
> I recommend using [mcl](https://github.com/micans/mcl) command-line tool for better runtime performance and memory efficiency.

## Installation

`Python 3.9 or later` is required for installation.  
For visualization, [networkx](https://github.com/networkx/networkx) and extra packages (e.g. *matplotlib*, *pygraphviz*) are also required.

**Install PyPI package:**

```shell
pip install pymarkovclustering 
pip install networkx[default,extra] # For visualization
```

**Install conda-forge package:**

```shell
conda install -c conda-forge pymarkovclustering
conda install -c conda-forge networkx matplotlib-base pygraphviz pydot lxml # For visualization
```

> [!NOTE]
> pygraphviz installation requires graphviz and C/C++ compiler.  
> If you encounter installation troubles, see [pygraphviz install docs](https://pygraphviz.github.io/documentation/stable/install.html) for details.

## API Usage

pyMarkovClustering provides a easy-to-use API to perform Markov Clustering from edges (list of source, target, weight).
It also provides an API to visualize the clustering results using networkx.
See [notebooks](https://moshi4.github.io/pyMarkovClustering/example/) and [API docs](https://moshi4.github.io/pyMarkovClustering/api-docs/pymarkovclustering/) in documents for more details.

### Markov Clustering

#### Simple edges

```python
import pymarkovclustering as pymcl

# List of edges (source, target, weight)
edges = [
    ("A", "B", 10),
    ("A", "C", 10),
    ("B", "C", 2),
    ("D", "E", 5),
    ("F", "G", 2),
    ("H", "I", 0.0),
]
# load edges as matrix, MCL, extract clusters
matrix, labels = pymcl.edges_to_sparse_matrix(edges)
mcl_matrix = pymcl.mcl(matrix, quiet=False)
clusters = pymcl.extract_clusters(mcl_matrix, labels)

for i, cluster in enumerate(clusters, 1):
    print(f"Cluster{i:03d}: {cluster}")
```

Output:

```txt
Cluster001: ['A', 'B', 'C']  
Cluster002: ['D', 'E']  
Cluster003: ['F', 'G']  
Cluster004: ['H']  
Cluster005: ['I']  
```

#### Random generated edges

```python
import pymarkovclustering as pymcl

# Generate random edges for MCL test
edges = pymcl.random_edges(30, min_cluster_size=2, max_cluster_size=6)
print(f"Edges: {edges}\n")

# easymcl automates load edges as matrix, MCL, extract clusters
clusters = pymcl.easymcl(edges, inflation=2.0)

for i, cluster in enumerate(clusters, 1):
    print(f"Cluster{i:03d}: {cluster}")
```

Output:

```txt
Edges: [('5_2', '5_5', 0.625), ('2_1', '2_5', 0.602), ('6_4', '6_5', 0.301), ('5_3', '5_6', 0.73), ('5_2', '5_6', 0.612), ('5_3', '5_5', 0.333), ('2_3', '2_5', 0.33), ('5_1', '5_3', 0.918), ('1_2', '1_4', 0.218), ('7_1', '7_2', 0.291), ('4_2', '4_3', 0.553), ('3_1', '3_2', 0.354), ('5_3', '5_4', 0.828), ('2_2', '2_4', 0.099), ('6_2', '6_5', 0.875), ('2_1', '2_3', 0.533), ('2_1', '2_4', 0.705), ('5_4', '5_5', 0.704), ('1_1', '1_4', 0.968), ('2_2', '2_5', 0.074), ('5_1', '5_5', 0.093), ('1_2', '1_3', 0.892), ('6_2', '6_3', 0.091), ('1_3', '1_5', 0.095), ('6_2', '6_4', 0.993), ('5_2', '5_4', 0.785), ('1_1', '1_3', 0.83), ('4_3', '4_4', 0.521), ('6_1', '6_2', 0.222), ('4_1', '4_3', 0.64), ('2_3', '2_4', 0.85), ('4_1', '4_2', 0.316), ('6_1', '6_5', 0.543), ('6_3', '6_5', 0.489), ('5_1', '5_6', 0.84), ('4_1', '4_4', 0.204), ('1_3', '1_4', 0.14), ('1_2', '1_5', 0.139), ('7_1', '7_3', 0.125), ('6_1', '6_3', 0.803), ('5_4', '5_6', 0.063), ('2_2', '2_3', 0.147), ('2_1', '2_2', 0.987), ('4_2', '4_4', 0.443), ('5_2', '5_3', 0.71), ('7_2', '7_3', 0.333), ('6_3', '6_4', 0.998), ('1_4', '1_5', 0.799), ('1_1', '1_5', 0.358), ('5_1', '5_4', 0.916), ('5_1', '5_2', 0.062), ('2_4', '2_5', 0.56), ('1_1', '1_2', 0.918), ('5_5', '5_6', 0.917), ('6_1', '6_4', 0.142)]  

Cluster001: ['5_2', '5_5', '5_3', '5_6', '5_1', '5_4']  
Cluster002: ['2_1', '2_5', '2_3', '2_2', '2_4']  
Cluster003: ['6_4', '6_5', '6_2', '6_3', '6_1']  
Cluster004: ['1_2', '1_4', '1_1', '1_3', '1_5']  
Cluster005: ['4_2', '4_3', '4_4', '4_1']  
Cluster006: ['7_1', '7_2', '7_3']  
Cluster007: ['3_1', '3_2']  
```

### Visualization

```python
import pymarkovclustering as pymcl

# Generate random edges for MCL test
edges = pymcl.random_edges(100, random_add_rate=0.1, min_cluster_size=2)

# easymclviz automates load edges as matrix, MCL, extract clusters, visualization
fig = pymcl.easymclviz(edges, inflation=2.0, show_label=True)
fig.suptitle("Markov Clustering Result Visualization")
fig.savefig("clusters.png", dpi=100)
```

![clusters.png](https://raw.githubusercontent.com/moshi4/pyMarkovClustering/main/docs/clusters.png)

## CLI Usage

pyMarkovClustering provides simple CLI for running MCL and extract clusters from edges file.

### Option

    $ pymcl --help
    usage: pymcl [options] edges.tsv -o clusters.tsv

    Markov Clustering in Python

    positional arguments:
      edges              Input edges(source, target, weight) tab-delimited file

    optional arguments:
      -o , --outfile     Output tab-delimited clusters file (default: stdout)
      -I , --inflation   Inflation factor (default: 2.0)
      --max_iter         Max number of iteration (default: 100)
      -q, --quiet        No print log on screen (default: OFF)
      -v, --version      Print version information
      -h, --help         Show this help message and exit

### Example Command

    pymcl edges.tsv -I 2.0 -o clusters.tsv

e.g. [edges.tsv](https://github.com/moshi4/pyMarkovClustering/blob/main/docs/resources/edges.tsv) >>>  [clusters.tsv](https://github.com/moshi4/pyMarkovClustering/blob/main/docs/resources/clusters.tsv)