| Name | pyFlowSOM JSON |
| Version |
0.1.16
JSON |
| download |
| home_page | |
| Summary | A Python implementation of the SOM training functionality of FlowSOM |
| upload_time | 2023-10-20 18:11:59 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | Modified Apache License 2.0 |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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# pyFlowSOM
<div align="center">
| | |
| --- | --- |
| CI / CD | [](https://github.com/angelolab/pyFlowSOM/actions/workflows/ci.yml) [](https://coveralls.io/github/angelolab/pyFlowSOM?branch=main) |
| Package | [](https://pypi.org/project/pyFlowSOM/) [](https://pypi.org/project/pyFlowSOM/) [](https://pypi.org/project/pyFlowSOM/) |
|Meta | [](LICENSE) |
</div>
Python runner for the [FlowSOM](https://github.com/SofieVG/FlowSOM) library.
Basic usage:
```python
import numpy as np
import pandas as pd
from pyFlowSOM import map_data_to_nodes, som
# generate example input data, rows are observations (e.g. cells), columns are features (e.g. proteins)
df = pd.DataFrame(np.random.rand(500, 16))
# alternatively, specify path to your own input data
df = pd.read_csv('path/to/som/input.csv')
example_som_input_arr = df.to_numpy()
# train the SOM
node_output = som(example_som_input_arr, xdim=10, ydim=10, rlen=10)
# use trained SOM to assign clusters to each observation in your data
clusters, dists = map_data_to_nodes(node_output, example_som_input_arr)
```
To put the data back into dataframes:
```python
eno = pd.DataFrame(data=node_output, columns=df.columns)
eco = pd.DataFrame(data=clusters, columns=["cluster"])
```
To export to csv:
```python
eno.to_csv('examples/example_node_output.csv', index=False)
eco.to_csv('examples/example_clusters_output.csv', index=False)
```
To plot the output as a heatmap:
```python
import seaborn as sns
# Append results to the input data
df['cluster'] = clusters
# Find mean of each cluster
df_mean = df.groupby(['cluster']).mean()
# Make heatmap
sns_plot = sns.clustermap(df_mean, z_score=1, cmap="vlag", center=0, yticklabels=True)
sns_plot.figure.savefig(f"example_cluster_heatmap.png")
```
# Develop
The C code (`pyFlowSOM/flowsom.c`) is wrapped using Cython (`pyFlowSOM/cyFlowSOM.cyx`).
Tests do an exact comparison to cluster id ground truth and an approximate comparison to node values only because of floating point differences. Randomness works in tandem to the `seed` flag to the `som` function.
To run the tests, use the following command:
```shell
pytest
```
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"description": "# pyFlowSOM\n\n<div align=\"center\">\n\n| | | \n| --- | --- |\n| CI / CD | [](https://github.com/angelolab/pyFlowSOM/actions/workflows/ci.yml) [](https://coveralls.io/github/angelolab/pyFlowSOM?branch=main) |\n| Package | [](https://pypi.org/project/pyFlowSOM/) [](https://pypi.org/project/pyFlowSOM/) [](https://pypi.org/project/pyFlowSOM/) |\n|Meta | [](LICENSE) |\n\n</div>\n\n\nPython runner for the [FlowSOM](https://github.com/SofieVG/FlowSOM) library.\n\nBasic usage:\n\n```python\nimport numpy as np\nimport pandas as pd\nfrom pyFlowSOM import map_data_to_nodes, som\n\n# generate example input data, rows are observations (e.g. cells), columns are features (e.g. proteins)\ndf = pd.DataFrame(np.random.rand(500, 16))\n\n# alternatively, specify path to your own input data\ndf = pd.read_csv('path/to/som/input.csv')\n\nexample_som_input_arr = df.to_numpy()\n\n# train the SOM\nnode_output = som(example_som_input_arr, xdim=10, ydim=10, rlen=10)\n\n# use trained SOM to assign clusters to each observation in your data\nclusters, dists = map_data_to_nodes(node_output, example_som_input_arr)\n```\n\nTo put the data back into dataframes:\n\n```python\neno = pd.DataFrame(data=node_output, columns=df.columns)\neco = pd.DataFrame(data=clusters, columns=[\"cluster\"])\n```\nTo export to csv:\n\n```python\neno.to_csv('examples/example_node_output.csv', index=False)\neco.to_csv('examples/example_clusters_output.csv', index=False)\n```\n\nTo plot the output as a heatmap:\n\n```python\nimport seaborn as sns\n\n# Append results to the input data\ndf['cluster'] = clusters\n\n# Find mean of each cluster\ndf_mean = df.groupby(['cluster']).mean()\n\n# Make heatmap\nsns_plot = sns.clustermap(df_mean, z_score=1, cmap=\"vlag\", center=0, yticklabels=True)\nsns_plot.figure.savefig(f\"example_cluster_heatmap.png\")\n```\n\n# Develop\n\nThe C code (`pyFlowSOM/flowsom.c`) is wrapped using Cython (`pyFlowSOM/cyFlowSOM.cyx`).\n\nTests do an exact comparison to cluster id ground truth and an approximate comparison to node values only because of floating point differences. Randomness works in tandem to the `seed` flag to the `som` function.\n\nTo run the tests, use the following command:\n\n```shell\npytest\n```\n\n",
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