DISCERN

DISCERN is a deep learning approach to reconstruction expression of single-cell RNAseq data sets using a high-quality reference. Please also look at our manuscript DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. The full documentation contains further information and tutorials to get started.

Prerequisites

We use poetry for dependency management. You can get poetry by

pip install poetry

or (the officially recommended way)

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python

Installing

To get discern you can clone the repository by

git clone https://github.com/imsb-uke/discern.git

poetry can be used to install all further dependencies in a virtual environment.

cd discern
poetry install --no-dev

To finally run discern you can also directly use poetry with

poetry run commands

or spawn a new shell in the virtual environment

poetry shell

For all further examples, the first approach is used.

Using discern

You can use the main function of discern for most use cases. Usually, you have to preprocess your data by:

poetry run discern process <parameters.json>

An example parameters.json is provided together with an hyperparameter_search.json for hyperparameter optimization using ray[tune]. The training can be done with

poetry run discern train <parameters.json>

Hyperparameter optimization needs a ray server with can be started with

poetry run ray start --head --port 57780 --redis-password='password'

and can be started with

poetry run discern optimize <parameters.json>

For projection 2 different modes are available: Eval mode, which is a more general approach and can save a lot of files:

poetry run discern project --all_batches <parameters.json>

Or projection mode which offers a more fine grained controll to which is projected.

poetry run discern project --metadata="metadatacolumn:value" --metadata="metadatacolumn:" <parameters.json>

which creates to files, one is projected to the average batch calculated by a metadatacolumn and a contained value. The second file is projected to the the average for each value in “metadatacolumn”; individually.

DISCERN also supports online training. You can add new batches to your dataset after the usual train with:

poetry run discern onlinetraining --freeze --filename=<new_not_preprocessed_batch[es].h5ad> <parameters.json>

The data gets automatically preprocessed and added to the dataset. You can run project afterwards as usual (without the --filename flag). --freeze is important to freeze non-conditional layers in training.

Authors

• Can Ergen

• Pierre Machart

• Fabian Hausmann

Citation

If you use discern in your work, please cite the publication as follows:

DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection

Fabian Hausmann, Can Ergen, Robin Khatri, Mohamed Marouf, Sonja Hänzelmann, Nicola Gagliani, Samuel Huber, Pierre Machart & Stefan Bonn

Genome Biology 2023 doi: 10.1186/s13059-023-03049-x.