Liver Annotation is a Python package designed to annotate clusters in single-cell RNA sequencing (scRNA-seq) data from liver samples. This package provides a machine learning model that is specifically trained on liver cells, enabling out-of-the-box functionality without the need for pre-existing expert-annotated data.
- Machine learning model trained specifically on liver cells.
- Supports both neural network and random forest classifier models.
- Annotates clusters using either the most common annotation or probability-based methods.
To install the package, use pip:
pip install liver_annotationYou can classify cells by cell type using the classify_cells function. The function requires an input in_data which is a standard scanpy/anndata object with gene expression data.
from liver_annotation import classify_cells
# Example usage
classify_cells(ann_data_obj, species="human", model_type="nn")species: Choose between"human"or"mouse".model_type: Choose between"rfc"(random forest classifier) or"nn"(neural network).
Annotate clusters using the cluster_annotations function. This function requires an input in_data and allows you to specify the clustering algorithm and model type.
from liver_annotation import cluster_annotations
# Example usage
cluster_annotations(in_data, species="human", clusters="louvain", algorithm="mode", model_type="nn")clusters: The column inin_data.obsto use for cluster data.algorithm: Choose between"mode"or"prob"for cluster annotation.model_type: Choose between"rfc"or"nn".
torchjoblibscipynumpyscanpy
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
For any questions or issues, please contact Madhavendra Thakur at madhavendra.thakur@gmail.com.