cellbender 0.2.0

A software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data

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