FlowSOM

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FlowSOM algorithm in Python, using self-organizing maps and minimum spanning tree for visualization and interpretation of cytometry data

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# FlowSOM

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This repository contains a Python implementation of [FlowSOM](http://bioconductor.org/packages/release/bioc/html/FlowSOM.html) algorithm for clustering and visualizing a mass cytometry data set.

For more details about the algorithm, please check ([En](https://hatchin.netlify.com/flowsom/?utm_source=github_flowsom_repo_read_me_en&utm_medium=flowsom_repo_readme&utm_campaign=read_me)|[中](https://hatchin.netlify.com/zh/flowsom/?utm_source=github_flowsom_repo_read_me_zh&utm_medium=flowsom_repo_readme&utm_campaign=read_me))

# Installation
Just use [pip](https://pypi.org/project/FlowSom/)
    
    pip install FlowSom
  
Or download this repository to a directory of your choice and then run:

pip install -r requirements.txt
    
    
How to use it
------------------
<dl>
  <dt>Read Files</dt>
  <dd>In order to use FlowSOM you need your data saved as a .csv file or a .fcs file.</dd>
</dl>

```python
file = r'flowmetry.fcs'
```
<dl>
  <dd>Or</dd>
</dl>

```python
file = 'flowmetry.csv'
```

<dl>
  <dt>Import Package</dt>
    <dd>Then you need to import the package.</dd>
    <dd>If you install the package via pip, then you should run</dd>
</dl>

```python
from flowsom import flowsom
```
<dl>
    <dd>If you download the repository, you should run</dd>
</dl>

```python
from flowsom import *
```
<dl>
    <dt>Play Around</dt>
    <dd>Then you can run FlowSOM just as follows:</dd>
</dl>

```python
fsom = flowsom(file) # read the data
fsom.som_mapping(50, 50, 31, sigma=2.5, 
                 learning_rate=0.1, batch_size=100)  # trains SOM with 100 iterations
fsom.meta_clustering(AgglomerativeClustering, min_n=40, 
                     max_n=45, 
                     iter_n=3) # train the meta clustering for cluster in range(40,45)       
```

### Use the trained output

After the training, you will be able to:

* Get the weights of SOM with method `fsom.map_som`
* Get the best number of clustering with method `fsom.bestk`
* Get the prediction dataframe with method `fsom.df` and `fsom.tf_df`
* Visualize the final clustering outcome with method`fsom.vis`

Examples
-------------------------
The demo code could be found [here](https://github.com/Hatchin/FlowSOM/blob/master/demo/demo.ipynb).

The distance map of SOM trained from a sample flow cytometry [data](https://github.com/Hatchin/FlowSOM/blob/master/demo/flowmetry_transformed.csv):

The visualization example after meta-clustering using Minimal Spanning Tree (MST):
<img src="https://github.com/Hatchin/FlowSOM/blob/master/img/mst.png" alt="MST example">

FlowSOM Algorithm
--------------------------

FlowSOM analyzes flow or mass cytometry data using a self-Organizing Map (SOM). Using a two-level clustering and star charts, FlowSOM helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise.

The algorithm consists of four steps: 
- reading the data
- building a Self-Organizing Map
- building a minimal spanning tree
- computing a meta-clustering
    
### Self-Organizing Map
SOM is a type of unsupervised Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. [Introduction](https://heartbeat.fritz.ai/introduction-to-self-organizing-maps-soms-98e88b568f5d)

### Minimum Spanning Tree

A minimum spanning tree (MST) or minimum weight spanning tree is a subset of the edges of a connected, edge-weighted undirected graph that connects all the vertices together, without any cycles and with the minimum possible total edge weight.

### Meta-clustering
The meta-clustering technique conducted on the SOM is hierarchical consensus meta-clustering, which clusters the weights of trained SOM into different groups.

Acknowledge
-----------------
FlowSOM is built based on [FlowCytometryTools](https://github.com/eyurtsev/FlowCytometryTools), [MiniSom](https://github.com/JustGlowing/minisom) and [Consensus Clustering](https://github.com/ZigaSajovic/Consensus_Clustering).

Update pypi: [source](https://stackoverflow.com/questions/52700692/a-guide-for-updating-packages-on-pypi)

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