CellBender
==========
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:alt: CellBender Logo
CellBender is a software package for eliminating technical artifacts from
high-throughput single-cell RNA sequencing (scRNA-seq) data.
The current release contains the following modules. More modules will be added in the future:
* ``remove-background``:
This module removes counts due to ambient RNA molecules and random barcode swapping from (raw)
UMI-based scRNA-seq count matrices. Also works for snRNA-seq and CITE-seq.
Please refer to `the documentation <https://cellbender.readthedocs.io/en/latest/>`_ for a quick start tutorial.
Installation and Usage
----------------------
CellBender can be installed via
.. code-block:: console
$ pip install cellbender
(and we recommend installing in its own ``conda`` environment to prevent
conflicts with other software).
CellBender is run as a command-line tool, as in
.. code-block:: console
(cellbender) $ cellbender remove-background \
--cuda \
--input my_raw_count_matrix_file.h5 \
--output my_cellbender_output_file.h5
See `the usage documentation <https://cellbender.readthedocs.io/en/latest/usage/index.html>`_
for details.
Using The Official Docker Image
-------------------------------
A GPU-enabled docker image is available from the Google Container Registry (GCR) as:
``us.gcr.io/broad-dsde-methods/cellbender:latest``
Available image tags track release tags in GitHub, and include ``latest``,
``0.1.0``, ``0.2.0``, ``0.2.1``, ``0.2.2``, and ``0.3.0``.
WDL Users
---------
A workflow written in the
`workflow description language (WDL) <https://github.com/openwdl/wdl>`_
is available for CellBender remove-background.
For `Terra <https://app.terra.bio>`_ users, a workflow called
``cellbender/remove-background`` is
`available from the Broad Methods repository
<https://portal.firecloud.org/#methods/cellbender/remove-background/>`_.
There is also a `version available on Dockstore
<https://dockstore.org/workflows/github.com/broadinstitute/CellBender>`_.
Advanced installation
---------------------
From source for development
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Create a conda environment and activate it:
.. code-block:: console
$ conda create -n cellbender python=3.7
$ conda activate cellbender
Install the `pytables <https://www.pytables.org>`_ module:
.. code-block:: console
(cellbender) $ conda install -c anaconda pytables
Install `pytorch <https://pytorch.org>`_ via
`these instructions <https://pytorch.org/get-started/locally/>`_, for example:
.. code-block:: console
(cellbender) $ pip install torch
and ensure that your installation is appropriate for your hardware (i.e. that
the relevant CUDA drivers get installed and that ``torch.cuda.is_available()``
returns ``True`` if you have a GPU available.
Clone this repository and install CellBender (in editable ``-e`` mode):
.. code-block:: console
(cellbender) $ git clone https://github.com/broadinstitute/CellBender.git
(cellbender) $ pip install -e CellBender
From a specific commit
~~~~~~~~~~~~~~~~~~~~~~
This can be achieved via
.. code-block:: console
(cellbender) $ pip install --no-cache-dir -U git+https://github.com/broadinstitute/CellBender.git@<SHA>
where ``<SHA>`` must be replaced by any reference to a particular git commit,
such as a tag, a branch name, or a commit sha.
Citing CellBender
-----------------
If you use CellBender in your research (and we hope you will), please consider
citing our paper in Nature Methods:
Stephen J Fleming, Mark D Chaffin, Alessandro Arduini, Amer-Denis Akkad,
Eric Banks, John C Marioni, Anthony A Phillipakis, Patrick T Ellinor,
and Mehrtash Babadi. Unsupervised removal of systematic background noise from
droplet-based single-cell experiments using CellBender.
`Nature Methods`, 2023. https://doi.org/10.1038/s41592-023-01943-7
See also `our preprint on bioRxiv <https://doi.org/10.1101/791699>`_.
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