Package: pyscenic
pySCENIC is a lightning-fast python implementation of theSCENICpipeline (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].
A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols[4].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 thedaskframework for distributed computing[2].Full documentationfor pySCENIC is available onRead the DocspySCENIC is part of the SCENIC Suite of tools!
See the mainSCENIC websitefor additional information and a full list of tools available.News and releases0.12.0 | 2022-08-16Only databases in Feather v2 format are supported now (ctxcore>= 0.2),
which allow uses recent versions of pyarrow (>=8.0.0) instead of very old ones (<0.17).
Databases in the new format can be downloaded fromhttps://resources.aertslab.org/cistarget/databases/and end with*.genes_vs_motifs.rankings.featheror*.genes_vs_tracks.rankings.feather.Support clustered motif databases.Use custom multiprocessing instead of dask, by default.Docker image uses python 3.10 and contains only needed pySCENIC dependencies for CLI usage.Remove unneeded scripts and notebooks for unused/deprecated database formats.0.11.2 | 2021-05-07Split some core cisTarget functions out into a separate repository,ctxcore. This is now a required package for pySCENIC.0.11.1 | 2021-02-11Fix bug in motif url construction (#275)Fix for export2loom with sparse dataframe (#278)Fix sklearn t-SNE import (#285)Updates to Docker image (expose port 8787 for Dask dashboard)0.11.0 | 2021-02-10Major features:Updatedarboretorelease (GRN inference step) includes:Support for sparse matrices (using the--sparseflag inpyscenic grn, or passing a sparse matrix togrnboost2/genie3).Fixes to avoid dask metadata mismatch errorUpdated cisTarget:Fix for metadata mismatch in ctx prune2df stepSupport for databases Apache Parquet formatFaster loading from feather databasesBugfix: loading genes from a database (previously missing the last gene name in the database)Support for Anndata input and outputPackage updates:Upgrade to newer pandas versionUpgrade to newer numba versionUpgrade to newer versions of dask, distributedInput checks and more descriptive error messages.Check that regulons loaded are not empty.Bugfixes:In the regulons output from the cisTarget step, the gene weights were incorrectly assigned to their respective target genes (PR #254).Motif url construction fixed when running ctx without pruningCompression of intermediate files in the CLI stepsHandle loom files with non-standard gene/cell attribute namesReformat the genesig gmt input/outputFix AUCell output to loom with non-standard loom attributes0.10.4 | 2020-11-24Included new CLI option to add correlation information to the GRN adjacencies file. This can be called withpyscenic add_cor.See also the extendedRelease Notes.OverviewThe pipeline has three steps: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. Thearboretopackage is used for this step.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.Finally, the original cells are differentiated and clustered on the activity of these discovered regulons.The most impactful speed improvement is introduced by thearboretopackage 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:You can leverage multi-core and multi-node clusters usingdaskand itsdistributedscheduler.We implemented a version of the recovery of input genes that takes into account weights associated with these genes.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.Additional resourcesFor more information, please visitLCB,
the mainSCENIC website,
orSCENIC (R version).
There is a tutorial tocreate new cisTarget databases.
The CLI to pySCENIC has also been streamlined into a pipeline that can be run with a single command, using the Nextflow workflow manager.
There are two Nextflow implementations available:SCENICprotocol: A Nextflow DSL1 implementation of pySCENIC alongside a basic “best practices” expression analysis. Includes details on pySCENIC installation, usage, and downstream analysis, along with detailed tutorials.VSNPipelines: A Nextflow DSL2 implementation of pySCENIC with a comprehensive and customizable pipeline for expression analysis. Includes additional pySCENIC features (multi-runs, integrated motif- and track-based regulon pruning, loom file generation).AcknowledgmentsWe 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).References[1]Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017).doi:10.1038/nmeth.4463[2]Rocklin, M. Dask: parallel computation with blocked algorithms and task scheduling. conference.scipy.org[3]Huynh-Thu, V. A. et al. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, (2010).doi:10.1371/journal.pone.0012776[4]Van de Sande B., Flerin C., et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. June 2020:1-30.doi:10.1038/s41596-020-0336-2