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

The pioneering work was done in R and results were published in Nature Methods [1].

pySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells in no time. The latter is achieved via the dask framework for distributed computing [2].

Full documentation is available on Read the Docs

0.10.0 release

• Added a helper script arboreto_with_multiprocessing.py that runs the Arboreto GRN algorithms (GRNBoost2, GENIE3) without Dask for compatibility.
• Ability to set a fixed seed in both the AUCell step and in the calculation of regulon thresholds (CLI parameter --seed; aucell function parameter seed).
• (since 0.9.18) In the modules_from_adjacencies function, the default value of rho_mask_dropouts is changed to False. This now matches the behavior of the R version of SCENIC. The cli version has an additional option to turn dropout masking back on (--mask_dropouts).

The pipeline has three steps:

1. First transcription factors (TFs) and their target genes, together defining a regulon, are derived using gene inference methods which solely rely on correlations between expression of genes across cells. The arboreto package is used for this step.
2. These regulons are refined by pruning targets that do not have an enrichment for a corresponding motif of the TF effectively separating direct from indirect targets based on the presence of cis-regulatory footprints.
3. Finally, the original cells are differentiated and clustered on the activity of these discovered regulons.

The most impactful speed improvement is introduced by the arboreto package in step 1. This package provides an alternative to GENIE3 [3] called GRNBoost2. This package can be controlled from within pySCENIC.

All the functionality of the original R implementation is available and in addition:

1. You can leverage multi-core and multi-node clusters using dask and its distributed scheduler.
2. We implemented a version of the recovery of input genes that takes into account weights associated with these genes.
3. Regulons, i.e. the regulatory network that connects a TF with its target genes, with targets that are repressed are now also derived and used for cell enrichment analysis.

For more information, please visit LCB, SCENIC (R version), or SCENICprotocol (for a Nextflow implementation).

We are grateful to all providers of TF-annotated position weight matrices, in particular Martha Bulyk (UNIPROBE), Wyeth Wasserman and Albin Sandelin (JASPAR), BioBase (TRANSFAC), Scot Wolfe and Michael Brodsky (FlyFactorSurvey) and Timothy Hughes (cisBP).