A software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data
Project description
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. At the moment, only the count matrices produced by the CellRanger count pipeline is supported. Support for additional tools and protocols will be added in the future. A quick start tutorial can be found here.
Please refer to the documentation for a quick start tutorial on using CellBender.
Installation and Usage
Manual installation
The recommended installation is as follows. Create a conda environment and activate it:
$ conda create -n cellbender python=3.7
$ source activate cellbender
Install the pytables module:
(cellbender) $ conda install -c anaconda pytables
Install pytorch (shown below for CPU; if you have a CUDA-ready GPU, please skip this part and follow these instructions instead):
(cellbender) $ conda install pytorch torchvision -c pytorch
Clone this repository and install CellBender:
(cellbender) $ pip install -e CellBender
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
Terra Users
For Terra users, a workflow is available as:
cellbender/remove-background
Citing CellBender
If you use CellBender in your research (and we hope you will), please consider citing our paper on bioRxiv.
Stephen J Fleming, John C Marioni, and Mehrtash Babadi. CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets. bioRxiv 791699; doi: https://doi.org/10.1101/791699
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