Skip to content

Latest commit

 

History

History
93 lines (65 loc) · 3.65 KB

README.md

File metadata and controls

93 lines (65 loc) · 3.65 KB
Raw

SEACells:

Single-cEll Aggregation for High Resolution Cell States

Installation and dependencies

  1. 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

  2. If you are using conda, you can use the environment.yaml to create a new environment and install SEACells.

conda env create -n seacells --file environment.yaml
conda activate seacells
  1. You can also use pip to install the requirements
pip install -r requirements.txt

And then follow step (1)

  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

  1. SEACells depends on a number of python3 packages available on pypi and these dependencies are listed in setup.py.

    All the dependencies will be automatically installed using the above commands

  2. To uninstall:

     $> pip uninstall SEACells

Usage

  1. 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.

  2. 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).

  3. 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).

  4. 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).

  5. 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)

  6. Cross-modality integration : Integration between scRNA and scATAC can be performed following the Integration notebook

Citations

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}
}

Release Notes