pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering). It enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

References:

• Bram Van de Sande, Christopher Flerin, Kristofer Davie, Maxime De Waegeneer, Gert Hulselmans, Sara Aibar, Ruth Seurinck, Wouter Saelens, Robrecht Cannoodt, Quentin Rouchon, Toni Verbeiren, Dries De Maeyer, Joke Reumers, Yvan Saeys and Stein Aerts,
  A scalable SCENIC workflow for single-cell gene regulatory network analysis.
  Nature Protocols volume 15, pages 2247–2276 (2020)
• Sara Aibar, Carmen Bravo González-Blas, Thomas Moerman, Vân Anh Huynh-Thu, Hana Imrichova, Gert Hulselmans, Florian Rambow, Jean-Christophe Marine, Pierre Geurts, Jan Aerts, Joost van den Oord, Zeynep Kalender Atak, Jasper Wouters & Stein Aerts, SCENIC : single-cell regulatory network inference and clustering, Nature Methods volume 14, pages 1083–1086 (2017)

• pySCENIC Github page

• Module Name: pySCENIC (see the modules page for more information)
• Unusual environment variables set
  • PYSCENIC_HOME  installation directory
  • PYSCENIC_BIN       executable directory
  • PYSCENIC_DATA  sample data directory

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive --mem=12g -c8 --grep=lscratch:20
[user@cig 3335 ~]$ module load pySCENIC
[+] Loading singularity  4.0.1   on cn3335
[+] Loading pySCENIC  0.12.1
[user@cn3335 ~]$ pyscenic 

usage: pyscenic [-h] {grn,add_cor,ctx,aucell} ...

Single-Cell rEgulatory Network Inference and Clustering (0.12.1)

positional arguments:
  {grn,add_cor,ctx,aucell}
                        sub-command help
    grn                 Derive co-expression modules from expression matrix.
    add_cor             [Optional] Add Pearson correlations based on TF-gene expression to the network adjacencies output from the GRN
                        step, and output these to a new adjacencies file. This will normally be done during the "ctx" step.
    ctx                 Find enriched motifs for a gene signature and optionally prune targets from this signature based on cis-regulatory
                        cues.
    aucell              Quantify activity of gene signatures across single cells.

options:
  -h, --help            show this help message and exit

Arguments can be read from file using a @args.txt construct. For more information on loom file format see http://loompy.org . For more
information on gmt file format see https://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats .
[user@cig 3335 ~]$ git clone https://github.com/aertslab/pySCENIC
[user@cig 3335 ~]$ ps_python  pySCENIC/tests/test_aucell.py
[user@cig 3335 ~]$ ps_python  pySCENIC/tests/test_featureseq.py
[user@cig 3335 ~]$ ps_python  pySCENIC/tests/test_math.py
[user@cn3335 ~]$ exit
user@biowulf]$