scHPL 1.0.5

Hierarchical progressive learning pipeline for single-cell RNA-sequencing datasets

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scHPL: Hierarchical progressive learning of cell identities in single-cell data

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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, kNN, 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. We advise doing this with scVI or scArches (see section treeArches below).

Installation

scHPL requires Python 3.6 or higher. 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. See the documentation <https://schpl.readthedocs.io/en/latest/>_ for more information.

treeArches

treeArches is a framework around scHPL and scArches <https://github.com/theislab/scarches>_ to automatically build and update reference atlases and the classification tree. Examples can be found in the treeArches reprodicibility Github <https://github.com/lcmmichielsen/treeArches-reproducibility>_ and in this notebook <https://github.com/theislab/scarches/blob/master/notebooks/treeArches_pbmc.ipynb>_.

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" <https://www.nature.com/articles/s41467-021-23196-8>_

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