nimble-raccoon 0.1.1

# nimble raccoon

A slim and fast reimplementation of [RACCOON](https://github.com/shlienlab/raccoon) (Resolution-Adaptive Coarse-to-fine Clusters OptimizatiON), iterative clustering library for python 3.

<!– <img src=”docs/figs/sps_logo.png” width=400, padding=100> [](https://zenodo.org/badge/latestdoi/91130860) [](https://badge.fury.io/py/simpsom) [](https://simpsom.readthedocs.io/en/latest/?badge=latest)  [](https://codecov.io/gh/fcomitani/simpsom) –>

It relies on [faiss](https://github.com/facebookresearch/faiss) for quick nearest neighbors searches and better memory management, but offers fewer functionalities, only allowing cosine and euclidean distances, Louvain as its clustering algorithm, and grid search.

## Installation

nimble-raccoon can be installed with pip with the following command

pip install nimble-raccoon

<!– For the GPU-enable version, clone this repo and install add the -gpu flag –>

## Usage

To identify clusters and build the hierarchy initialize a Raccoon object with set parameters and then call it on the input_data object, a pandas dataframe.

import numpy as np from functools import partial

from nimbloon import Raccoon

You can define custom functions to dynamically adapt the search range to the size of the dataset.

def half_sqrt_range(x, num_elements=5):

sq = np.sqrt(x) return np.linspace(sq/2, sq, num_elements, dtype=int)

rc_args = {‘metric’: ‘cosine’,

‘scale’: False, ‘cumulative_variance’: [.75, .8, .9, .95, .99], ‘clustering_parameter’: np.logspace(-2, 1.5, 10), ‘n_neighbors’: partial(half_sqrt_range, num_elements=3), ‘target_dimensions’: 12, ‘min_cluster_size’: 25, ‘max_neighbors’: 100, ‘silhouette_threshold’: 0., ‘max_depth’: 5}

Once everything is set up you can instantiate the Raccoon object.

rc = Raccoon(outh_path=’./rc_output’, **rc_args)

And then call it on the input dataset to build the clusters hierarchy.

rc_labels, rc_tree = rc(input_table)

Available parameters are:

• metric: the distance metric, currently only cosine and euclidean can be set as metrics.

• scale: scale features at every iteration.

• cumulative_variance: the limit of cumulative variance for low-information

  features removal with tSVD. Can be a single float or a list of float

• clustering_parameter: the range of resolutions for the Louvain clustering

  algorithm. Can be a single float or a list of float

• n_neighbors: the number of nearest neighbors to use across the search.

  Can be a single int or a list of int. Can also be a function, in which case this will be applied to the input set size, adapting this parameter at each iteration.

• target_dimensions: the dimensionality of the target space after

  applying UMAP.

• min_cluster_size: minimum size of clusters, if a cluster is identified

  with size below this threshold, it will not be further split.

• max_neighbors: maximum number of neighbors, useful when n_neighbors is

  dynamic and population dependent avoiding excessively costly operations for large datasets.

• silhouette_threshold: minimum silhouette score value to reach for a

  partition to be accepted.

• max_depth: maximum depth of the clusters hierarchy.

The output will be a one-hot-encoded dataframe with samples as rows and cluster labels as columns, and an anytree object with information on the hierarchical relationship among clusters. This information will also be automatically saved to disk in the out_path folder.

Both tSVD and UMAP steps can be skipped, by setting cumulative_variance to 1 and/or target_dimensions to None respectively.

## Citation

If you are using this library for your work, please cite the original RACCOON publication.

> Comitani, F., Nash, J.O., Cohen-Gogo, S. et al. Diagnostic classification of childhood cancer using multiscale transcriptomics. Nat Med 29, 656–666 (2023). https://doi.org/10.1038/s41591-023-02221-x

## Contributions

Contributions are always welcome. If you would like to help us improve this library please fork the main branch and make sure pytest pass after your changes.