devCellPy is a Python package designed for hierarchical multilayered classification of cells based on single-cell RNA-sequencing (scRNA-seq). It implements the machine learning algorithm Extreme Gradient Boost (XGBoost) (Chen and Guestrin, 2016) to automatically predict cell identities across complex permutations of layers and sublayers of annotation.
Given devCellPy's highly customizable classification scheme, users can input the annotation hierarchy of their scRNA-seq datasets into devCellPy to guide the automatic classification and prediction of cells according to the provided hierarchy. devCellPy allows users to designate any identity at each layer of classification and is not constrained by cell type——for example, assigning timepoint as one of the annotation layers allows for cell identity predictions at that layer to be conditioned on the age of the cells. In addition to hierarchical cell classification, devCellPy implements the SHapley Additive exPlanations (SHAP) package (Lundberg etal, 2020), which provides the user with interpretability methods for the model and determines the positive and negative gene predictors of cell identities across all annotation layers.
For more information please refer to our publication at:
Galdos, F.X., Xu, S., et al. devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data. Nat Commun 13, 5271 (2022). https://doi.org/10.1038/s41467-022-33045-x
To download the compiled Seurat object for the murine cardiac developmental atlas reported in our paper, please visit the following Zenodo link: https://zenodo.org/record/7183939#.Y0TuEC-B3T8
Full tutorial of devCellPy usage is available under: https://github.com/devCellPy-Team/devCellPy/tree/main/Tutorial
devcellpy hardware requirements vary depending on the modes used by the user.
Computer running with a minimum of 64 GB of RAM is recommended to train large scale datasets. We recommend users consider using a high performance computing cluster for training on large datasets that require greater than 64 GB.
Feature Ranking mode of devCellPy requires a minimum of 64 GB of RAM to run. We recommend users consider a high performance computing cluster for conducting feature ranking on large datasets.
Prediction of cell types using an already trained devCellPy prediction model can be run on a standard computer with a minimum of 8 GB RAM.
devCellPy has been formatted into a wrapper function that can be easily installed through pip and run through the command line of the Terminal. NOTE: All Python and XGBoost versions must remain the same throughout usage of all training, predicting, and feature ranking options. Ex) If Python 3.7 is used to train a dataset, Python 3.7 must be used to predict a query dataset using the trained dataset.
This package is supported for macOS and Linux. The package has been tested on the following systems:
- macOS Intel: Big Sur (11.6)
- macOS M1: Ventura (13.1)
- Linux: CentOS-7 Version 7.9.2009
devcellpy depends on the packages found in requirements.txt.
devCellPy has been formatted into a wrapper function that can be easily installed through pip and run through the command line of the Terminal or Command Prompt.
conda create -n ENV_NAME python=3.11
conda activate ENV_NAME
conda install pip
pip install devCellPy
NOTE: All Python and XGBoost versions must remain the same throughout usage of all training, predicting, and feature ranking options. Ex) If Python 3.7 is used to train a dataset, Python 3.7 must be used to predict a query dataset using the trained dataset. devCellPy should install within a few minutes on a "standard" computer supporting in-memory operations.
MIT License
Copyright (c) 2021 Stanford University
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