Single-cEll Aggregation for High Resolution Cell States
-
SEACells has been implemented in Python3.8 can be installed via pip:
$> pip install cmake $ > pip install SEACells It can also be installed directly from source.$> git clone https://github.com/dpeerlab/SEACells.git $> cd SEACells $> python setup.py install
-
If you are using
conda
, you can use theenvironment.yaml
to create a new environment and install SEACells.
conda env create -n seacells --file environment.yaml
conda activate seacells
- You can also use
pip
to install the requirements
pip install -r requirements.txt
And then follow step (1)
- MulticoreTSNE issues can be solved using
conda create --name seacells -c conda-forge -c bioconda cython python=3.8
conda activate seacells
pip install git+https://github.com/settylab/Palantir@removeTSNE
git clone https://github.com/dpeerlab/SEACells.git
cd SEACells
python setup.py install
-
SEACells depends on a number of
python3
packages available on pypi and these dependencies are listed insetup.py
.All the dependencies will be automatically installed using the above commands
-
To uninstall: $> pip uninstall SEACells
-
To install the developer installation of SEACells, run
git clone https://github.com/dpeerlab/SEACells.git
cd SEACells.git
pip install -e ".[dev]"
pre-commit install
-
ATAC preprocessing:
notebooks/ArchR
folder contains the preprocessing scripts and notebooks including peak calling using NFR fragments. See notebook here to get started. A version of ArchR that supports NFR peak calling is available here. -
Computing SEACells: A tutorial on SEACells usage and results visualization for single cell data can be found in the [SEACell computation notebook] (https://github.com/dpeerlab/SEACells/blob/main/notebooks/SEACell_computation.ipynb).
-
Gene regulatory toolkit: Peak gene correlations, gene scores and gene accessibility scores can be computed using the [ATAC analysis notebook] (https://github.com/dpeerlab/SEACells/blob/main/notebooks/SEACell_ATAC_analysis.ipynb).
-
TF activity inference: TF activities along differenitation trajectories can be computed using the [TF activity notebook] (https://github.com/dpeerlab/SEACells/blob/main/notebooks/SEACell_tf_activity.ipynb).
-
Large-scale data integration using SEACells : Details are avaiable in the [COVID integration notebook] (https://github.com/dpeerlab/SEACells/blob/main/notebooks/SEACell_COVID_integration.ipynb)
-
Cross-modality integration : Integration between scRNA and scATAC can be performed following the Integration notebook
SEACells manuscript is available on bioRxiv. If you use SEACells for your work, please cite our paper.
@article {Persad2022.04.02.486748,
author = {Persad, Sitara and Choo, Zi-Ning and Dien, Christine and Masilionis, Ignas and Chalign{\'e}, Ronan and Nawy, Tal and Brown, Chrysothemis C and Pe{\textquoteright}er, Itsik and Setty, Manu and Pe{\textquoteright}er, Dana},
title = {SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data},
elocation-id = {2022.04.02.486748},
year = {2022},
doi = {10.1101/2022.04.02.486748},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2022/04/03/2022.04.02.486748},
eprint = {https://www.biorxiv.org/content/early/2022/04/03/2022.04.02.486748.full.pdf},
journal = {bioRxiv}
}