SIMBA

SIMBA: SIngle-cell eMBedding Along with features

Main website, documentation and tutorials: https://simba-bio.readthedocs.io

Preprint: Huidong Chen, Jayoung Ryu, Michael E. Vinyard, Adam Lerer & Luca Pinello. “SIMBA: SIngle-cell eMBedding Along with features. bioRxiv, 2021.10.17.464750v1 (2021).”

The scripts used for the comparison analyses in the manuscript can be found here.

Installation

Before installing SIMBA make sure to have the correct channels priority by executing these commands:

conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict

To install the simba package with conda, run:

conda create -n env_simba jupyter simba

To enable the k-mer and TF analyses please install these additional dependencies(optional):

conda install r-essentials r-optparse bioconductor-jaspar2020 bioconductor-biostrings bioconductor-tfbstools bioconductor-motifmatchr bioconductor-summarizedexperiment r-doparallel bioconductor-rhdf5 bioconductor-hdf5array

SIMBA v1.2 (dev) update

We have added the support for

• Continuous edge weight encoding for scRNA-seq (tutorial)
• Significance testing of features’ cell type specificity metrics (tutorial)

SIMBA v1.2 Installation

To install the latest development version of simba:

conda create -n env_simba_dev jupyter pytorch pybedtools -y
pip install 'simba @ git+https://github.com/pinellolab/simba@dev'

To enable the k-mer and TF analyses please install these additional dependencies(optional):

conda install r-essentials r-optparse bioconductor-jaspar2020 bioconductor-biostrings bioconductor-tfbstools bioconductor-motifmatchr bioconductor-summarizedexperiment r-doparallel bioconductor-rhdf5 bioconductor-hdf5array

Please refer to the main documentation website to learn how to use SIMBA with the provided tutorials: https://simba-bio.readthedocs.io