# `robustica`
Fully customizable robust Independent Component Analysis (ICA).
[](https://pypi.python.org/pypi/robustica)
[](https://opensource.org/licenses/BSD-3-Clause)
## Description
This package contains 3 modules:
- `RobustICA`
Defines the most important class that allows to perform and customize robust independent component analysis.
- `InferComponents`
Retrieves the number of components that explain a user-defined percentage of variance.
- `examples`
Contains handy functions to quickly create or access example datasets.
A more user-friendly documentation can be found at https://crg-cnag.github.io/robustica/.
## Requirements
In brackets, versions of packages used to develop `robustica`.
- `numpy` (1.19.2)
- `pandas` (1.1.2)
- `scipy` (1.6.2)
- `scikit-learn` (0.23.2)
- `joblib` (1.0.1)
- `tqdm` (4.59.0)
- (optional) `scikit-learn-extra` (0.2.0): required only for clustering algorithms KMedoids and CommonNNClustering
## Installation
### [optional] `scikit-learn-extra` incompatibility
To use the clustering algorithms KMedoids and CommonNNClustering, install a forked version first to avoid incompatibility with the newest `numpy` (see [#6](https://github.com/CRG-CNAG/robustica/issues/6) for more info on this).
```shell
pip install git+https://github.com/TimotheeMathieu/scikit-learn-extra
```
### pip
```shell
pip install robustica
```
### local (latest version)
```shell
git clone https://github.com/CRG-CNAG/robustica
cd robustica
pip install -e .
```
## Usage
```python
from robustica import RobustICA
from robustica.examples import make_sampledata
X = make_sampledata(ncol=300, nrow=2000, seed=123)
rica = RobustICA(n_components=10)
# note that by default, we use DBSCAN algorithm and the number of components can be smaller
# than the number of components defined.
S, A = rica.fit_transform(X)
# source matrix (nrow x n_components)
print(S.shape)
print(S)
```
```shell
(2000, 3)
[[ 0.00975714 0.00619138 0.00502649]
[-0.0021527 -0.0376857 0.0117938 ]
[ 0.00046302 0.01712561 0.00518039]
...
[ 0.00128344 -0.00767099 0.0047334 ]
[ 0.00644422 -0.00498327 0.01325542]
[ 0.0017873 -0.01739889 -0.00445954]]
```
```python
# mixing matrix (ncol x n_components)
print(A.shape)
print(A)
```
```shell
(300, 3)
[[-1.79503194e-02 -1.05611924e+00 5.36688700e-01]
[ 1.03342514e-01 7.43471382e-02 4.90472157e-01]
[ 4.89753256e-01 -1.11300905e+00 -7.55809647e-01]
...
[ 4.30468472e-01 -4.87992838e-01 -7.77965512e-01]
[ 3.44078031e-02 4.09029805e-01 -7.29076312e-01]
[ 2.15557427e-02 2.89301273e-01 -2.96690459e-01]]
```
## Tutorials
- [Basic pipeline for exploratory analysis](https://crg-cnag.github.io/robustica/basics.html)
- [Using a custom clustering class](https://crg-cnag.github.io/robustica/customize_clustering.html)
- [Inferring the number of components](https://crg-cnag.github.io/robustica/infer_components.html)
## Contact
This project has been fully developed at the [Centre for Genomic Regulation](https://www.crg.eu/) within the group of [Design of Biological Systems](https://www.crg.eu/en/luis_serrano)
Please, report any issues that you experience through this repository's ["Issues"](https://github.com/CRG-CNAG/robustica/issues) or email:
- [Miquel Anglada-Girotto](mailto:miquel.anglada@crg.eu)
- [Sarah A. Head](mailto:sarah.dibartolo@crg.eu)
- [Luis Serrano](mailto:luis.serrano@crg.eu)
## License
`robustica` is distributed under a BSD 3-Clause License (see [LICENSE](https://github.com/CRG-CNAG/robustica/blob/main/LICENSE)).
## Citation
*Anglada-Girotto, M., Miravet-Verde, S., Serrano, L., Head, S. A.*. "*robustica*: customizable robust independent component analysis". BMC Bioinformatics 23, 519 (2022). DOI: https://doi.org/10.1186/s12859-022-05043-9
## References
- *Himberg, J., & Hyvarinen, A.* "Icasso: software for investigating the reliability of ICA estimates by clustering and visualization". IEEE XIII Workshop on Neural Networks for Signal Processing (2003). DOI: https://doi.org/10.1109/NNSP.2003.1318025
- *Sastry, Anand V., et al.* "The Escherichia coli transcriptome mostly consists of independently regulated modules." Nature communications 10.1 (2019): 1-14. DOI: https://doi.org/10.1038/s41467-019-13483-w
- *Kairov, U., Cantini, L., Greco, A. et al.* Determining the optimal number of independent components for reproducible transcriptomic data analysis. BMC Genomics 18, 712 (2017). DOI: https://doi.org/10.1186/s12864-017-4112-9
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"description": "# `robustica`\n\n\n\nFully customizable robust Independent Component Analysis (ICA).\n\n[](https://pypi.python.org/pypi/robustica)\n[](https://opensource.org/licenses/BSD-3-Clause)\n\n## Description\nThis package contains 3 modules:\n- `RobustICA`\n\n Defines the most important class that allows to perform and customize robust independent component analysis.\n \n- `InferComponents`\n\n Retrieves the number of components that explain a user-defined percentage of variance.\n\n- `examples`\n \n Contains handy functions to quickly create or access example datasets.\n\nA more user-friendly documentation can be found at https://crg-cnag.github.io/robustica/.\n\n## Requirements\nIn brackets, versions of packages used to develop `robustica`.\n- `numpy` (1.19.2)\n- `pandas` (1.1.2)\n- `scipy` (1.6.2)\n- `scikit-learn` (0.23.2)\n- `joblib` (1.0.1)\n- `tqdm` (4.59.0)\n- (optional) `scikit-learn-extra` (0.2.0): required only for clustering algorithms KMedoids and CommonNNClustering\n\n## Installation\n### [optional] `scikit-learn-extra` incompatibility\nTo use the clustering algorithms KMedoids and CommonNNClustering, install a forked version first to avoid incompatibility with the newest `numpy` (see [#6](https://github.com/CRG-CNAG/robustica/issues/6) for more info on this).\n```shell\npip install git+https://github.com/TimotheeMathieu/scikit-learn-extra\n```\n### pip\n```shell\npip install robustica\n```\n### local (latest version)\n```shell\ngit clone https://github.com/CRG-CNAG/robustica\ncd robustica\npip install -e .\n```\n\n## Usage\n```python\nfrom robustica import RobustICA\nfrom robustica.examples import make_sampledata\n\nX = make_sampledata(ncol=300, nrow=2000, seed=123)\n\nrica = RobustICA(n_components=10)\n# note that by default, we use DBSCAN algorithm and the number of components can be smaller\n# than the number of components defined.\nS, A = rica.fit_transform(X)\n\n# source matrix (nrow x n_components)\nprint(S.shape)\nprint(S)\n```\n```shell\n(2000, 3) \n[[ 0.00975714 0.00619138 0.00502649]\n [-0.0021527 -0.0376857 0.0117938 ]\n [ 0.00046302 0.01712561 0.00518039]\n ...\n [ 0.00128344 -0.00767099 0.0047334 ]\n [ 0.00644422 -0.00498327 0.01325542]\n [ 0.0017873 -0.01739889 -0.00445954]]\n```\n```python\n# mixing matrix (ncol x n_components)\nprint(A.shape)\nprint(A)\n```\n```shell\n(300, 3)\n[[-1.79503194e-02 -1.05611924e+00 5.36688700e-01]\n [ 1.03342514e-01 7.43471382e-02 4.90472157e-01]\n [ 4.89753256e-01 -1.11300905e+00 -7.55809647e-01]\n ...\n [ 4.30468472e-01 -4.87992838e-01 -7.77965512e-01]\n [ 3.44078031e-02 4.09029805e-01 -7.29076312e-01]\n [ 2.15557427e-02 2.89301273e-01 -2.96690459e-01]]\n```\n\n## Tutorials\n- [Basic pipeline for exploratory analysis](https://crg-cnag.github.io/robustica/basics.html)\n- [Using a custom clustering class](https://crg-cnag.github.io/robustica/customize_clustering.html)\n- [Inferring the number of components](https://crg-cnag.github.io/robustica/infer_components.html)\n\n\n## Contact\nThis project has been fully developed at the [Centre for Genomic Regulation](https://www.crg.eu/) within the group of [Design of Biological Systems](https://www.crg.eu/en/luis_serrano)\n\nPlease, report any issues that you experience through this repository's [\"Issues\"](https://github.com/CRG-CNAG/robustica/issues) or email:\n- [Miquel Anglada-Girotto](mailto:miquel.anglada@crg.eu)\n- [Sarah A. Head](mailto:sarah.dibartolo@crg.eu)\n- [Luis Serrano](mailto:luis.serrano@crg.eu)\n\n## License\n\n`robustica` is distributed under a BSD 3-Clause License (see [LICENSE](https://github.com/CRG-CNAG/robustica/blob/main/LICENSE)).\n\n## Citation\n*Anglada-Girotto, M., Miravet-Verde, S., Serrano, L., Head, S. A.*. \"*robustica*: customizable robust independent component analysis\". BMC Bioinformatics 23, 519 (2022). DOI: https://doi.org/10.1186/s12859-022-05043-9\n\n## References\n- *Himberg, J., & Hyvarinen, A.* \"Icasso: software for investigating the reliability of ICA estimates by clustering and visualization\". IEEE XIII Workshop on Neural Networks for Signal Processing (2003). DOI: https://doi.org/10.1109/NNSP.2003.1318025\n- *Sastry, Anand V., et al.* \"The Escherichia coli transcriptome mostly consists of independently regulated modules.\" Nature communications 10.1 (2019): 1-14. DOI: https://doi.org/10.1038/s41467-019-13483-w\n- *Kairov, U., Cantini, L., Greco, A. et al.* Determining the optimal number of independent components for reproducible transcriptomic data analysis. BMC Genomics 18, 712 (2017). DOI: https://doi.org/10.1186/s12864-017-4112-9\n",
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