Metadata-Version: 2.1
Name: scHPL
Version: 1.0.2
Summary: Hierarchical progressive learning pipeline for single-cell RNA-sequencing datasets
Home-page: https://github.com/lcmmichielsen/hierarchicalprogressivelearning
Author: Lieke Michielsen
Author-email: l.c.m.michielsen@tudelft.nl
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# Hierarchical progressive learning of cell identities in single-cell data

We present a hierarchical progressive learning method which automatically finds relationships between cell populations across multiple datasets and uses this to construct a hierarchical classification tree. For each node in the tree either a linear SVM or one-class SVM, which enables the detection of unknown populations, is trained. The trained classification tree can be used to predict the labels of a new unlabeled dataset. 

NOTE: scHPL is not a batch correction tool, we advise to align the datasets before matching the cell populations using scHPL.

### Installation

scHPL requires Python 3.6 or 3.7. The easiest way to install scHPL is through the following command::

    pip install scHPL

### General usage

The ```tutorial.ipynb``` notebook explains the basics of scHPL. The [vignette folder](vignettes) contains notebooks to reproduce the inter-dataset experiments.  

### Datasets

All datasets used are publicly available data and can be downloaded from Zenodo. The simulated data and aligned datasets used during the interdataset experiments can be downloaded from the [scHPL Zenodo](https://doi.org/10.5281/zenodo.4557712). The filtered PBMC-FACS and AMB2018 dataset can be downloaded from the [scRNA-seq benchmark Zenodo](https://doi.org/10.5281/zenodo.3357167)

For citation and further information please refer to:
"Hierarchical progressive learning of cell identities in single-cell data" ([biorxiv](https://www.biorxiv.org/content/10.1101/2020.03.27.010124v2))
