NOTE: This repository is no longer actively maintained. If you want to get the latest update or have any inquiries, please refer to https://github.com/dpeerlab/seqc instead.
SEQC is a python package that processes single-cell sequencing data in the cloud and analyzes it interactively on your local machine.
To faciliate easy installation and use, we have made available Amazon Machine Images (AMIs) that come with all of SEQC's dependencies pre-installed. In addition, we have uploaded common genome indices (-i/--index parameter) and barcode data (--barcode-files) to public amazon s3 repositories. These links can be provided to SEQC and it will automatically fetch them prior to initiating an analysis run. Finally, it can fetch input data directly from BaseSpace or amazon s3 for analysis.
For users with access to in-house compute clusters, SEQC can be installed on your systems and run using the --local parameter.
Python must be installed on your local machine to run SEQC. We recommend installing python3 through your unix operating system's package manager. For Mac OSX users we recommend homebrew. Typical installation commands would be:
brew install python3 # mac
apt-get install python3 # debian
yum install python3 # rpm-based
Installing these libraries is necessary before installing SEQC.
pip3 install Cython
pip3 install numpy
pip3 install bhtsne
To process data locally using SEQC, you must install the STAR Aligner, Samtools, and hdf5. If you only intend to use SEQC to trigger remote processing on AWS, these dependencies are optional. We recommend installing samtools and hdf5 through your package manager, if possible.
For processing a single lane (~200M reads) against human- and mouse-scale genomes, SEQC requires 30GB RAM, approximately 200GB free hard drive space, and scales linearly with additional compute cores. If running on AWS (see below), jobs are automatically scaled up or down according to the size of the input. There are no hardware requirements for the computer used to launch remote instances.
SEQC can be run on any unix-based operating system, however it also features the ability to automatically spawn Amazon Web Services instances to process your data. If you wish to take advantage of AWS, you will need to follow their instructions to:
Once all dependencies have been installed, SEQC can be installed on any machine by typing:
$> git clone https://github.com/ambrosejcarr/seqc.git
$> cd seqc && python3 setup.py install
Please note that to avoid passing the -k/--rsa-key command when you execute SEQC runs, you can also set the environment variable AWS_RSA_KEY to the path to your newly created key.
All the unit tests in class TestSEQC in test.py have been tested. Currently, only two platforms ten_x_v2 and in_drop_v2 have been tested. Old unit tests from these two platforms together with other platforms are stored at s3://dp-lab-data/seqc-old-unit-test/.
After SEQC is installed, help can be listed:
SEQC [-h] [-v] {run,progress,terminate,instances,start,index} ...
Processing Tools for scRNA-seq Experiments
positional arguments:
{run,progress,terminate,instances,start,index}
run initiate SEQC runs
progress check SEQC run progress
terminate terminate SEQC runs
instances list all running instances
start initialize a seqc-ready instance
index create a SEQC index
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
In addition to processing sequencing experiments, SEQC.py provides some convenience tools to create indices for use with SEQC and STAR, and tools to check the progress of remote runs, list current runs, start instances, and terminate them.
To seamlessly start an AWS instance with automatic installation of SEQC from your local machine you can run:
SEQC start