SCENICprotocol:可扩展的SCENIC工作流程，用于单细胞基因调控网络分析

# A scalable SCENIC workflow for single-cell gene regulatory network analysis This repository describes how to run a pySCENIC gene regulatory network inference analysis alongside a basic "best practices" expression analysis for single-cell data. This includes: * Standalone Jupyter notebooks for an interactive analysis * A Nextflow DSL1 workflow, which provides a semi-automated and streamlined method for running these steps * Details on pySCENIC installation, usage, and downstream analysis See also the associated publication in **Nature Protocols**: https://doi.org/10.1038/s41596-020-0336-2. For an advanced implementation of the steps in this protocol, see **[VSN Pipelines](https://github.com/vib-singlecell-nf/vsn-pipelines)**, a Nextflow DSL2 implementation of pySCENIC with comprehensive and customizable pipelines for expression analysis. This includes additional pySCENIC features (multi-runs, integrated motif- and track-based regulon pruning, loom file generation). ## Overview * [Quick start](#quick-start) * [Requirements](#general-requirements-for-this-workflow) * [Installation](docs/installation.md) * Case studies * PBMC 10k dataset (10x Genomics) * Full SCENIC analysis, plus filtering, clustering, visualization, and SCope-ready loom file creation: * [Jupyter notebook](notebooks/PBMC10k_SCENIC-protocol-CLI.ipynb) | [HTML render](http://htmlpreview.github.io/?https://github.com/aertslab/SCENICprotocol/blob/master/notebooks/PBMC10k_SCENIC-protocol-CLI.html) * Extended analysis post-SCENIC: * [Jupyter notebook](notebooks/PBMC10k_downstream-analysis.ipynb) | [HTML render](http://htmlpreview.github.io/?https://github.com/aertslab/SCENICprotocol/blob/master/notebooks/PBMC10k_downstream-analysis.html) * To run the same dataset through the VSN Pipelines DSL2 workflow, see [this tutorial](https://vsn-pipelines-examples.readthedocs.io/en/latest/PBMC10k.html). * Cancer data sets * [Jupyter notebook](notebooks/SCENIC%20Protocol%20-%20Case%20study%20-%20Cancer%20data%20sets.ipynb) | [HTML render](http://htmlpreview.github.io/?https://github.com/aertslab/SCENICprotocol/blob/master/notebooks/SCENIC%20Protocol%20-%20Case%20study%20-%20Cancer%20data%20sets.html) * Mouse brain data set * [Jupyter notebook](notebooks/SCENIC%20Protocol%20-%20Case%20study%20-%20Mouse%20brain%20data%20set.ipynb) | [HTML render](http://htmlpreview.github.io/?https://github.com/aertslab/SCENICprotocol/blob/master/notebooks/SCENIC%20Protocol%20-%20Case%20study%20-%20Mouse%20brain%20data%20set.html) * [References and more information](#references-and-more-information) <p align="center"> <img src="docs/figs/Figure01.png" width="600" alt="SCENIC workflow diagram"> </p> --- ## Quick start ### Running the pySCENIC pipeline in a Jupyter notebook We recommend using [this notebook](notebooks/PBMC10k_SCENIC-protocol-CLI.ipynb) as a template for running an interactive analysis in Jupyter. See the [installation instructions](docs/installation.md) for information on setting up a kernel with pySCENIC and other required packages. ### Running the Nextflow pipeline on the example dataset #### Requirements (Nextflow/containers) The following tools are required to run the steps in this Nextflow pipeline: * [Nextflow](https://www.nextflow.io/) * A container system, either of: * [Docker](https://docs.docker.com/) * [Singularity](https://www.sylabs.io/singularity/) The following container images will be pulled by nextflow as needed: * Docker: [aertslab/pyscenic:latest](https://hub.docker.com/r/aertslab/pyscenic). * Singularity: [aertslab/pySCENIC:latest](https://www.singularity-hub.org/collections/2033). * [See also here.](https://github.com/aertslab/pySCENIC#docker-and-singularity-images) #### Using the test profile A quick test can be accomplished using the `test` profile, which automatically pulls the testing dataset (described in full below): nextflow run aertslab/SCENICprotocol \ -profile docker,test This small test dataset takes approximately 70s to run using 6 threads on a standard desktop computer. #### Download testing dataset Alternately, the same data can be run with a more verbose approach (this is more illustrative for how to substitute other data into the pipeline). Download a minimum set of SCENIC database files for a human dataset (approximately 78 MB). mkdir example && cd example/ # Transcription factors: wget https://raw.githubusercontent.com/aertslab/SCENICprotocol/master/example/test_TFs_tiny.txt # Motif to TF annotation database: wget https://raw.githubusercontent.com/aertslab/SCENICprotocol/master/example/motifs.tbl # Ranking databases: wget https://raw.githubusercontent.com/aertslab/SCENICprotocol/master/example/genome-ranking.feather # Finally, get a tiny sample expression matrix (loom format): wget https://raw.githubusercontent.com/aertslab/SCENICprotocol/master/example/expr_mat_tiny.loom #### Running the example pipeline Either Docker or Singularity images can be used by specifying the appropriate profile (`-profile docker` or `-profile singularity`). Please note that for the tiny test dataset to run successfully, the default thresholds need to be lowered. ##### Using loom input nextflow run aertslab/SCENICprotocol \ -profile docker \ --loom_input expr_mat_tiny.loom \ --loom_output pyscenic_integrated-output.loom \ --TFs test_TFs_tiny.txt \ --motifs motifs.tbl \ --db *feather \ --thr_min_genes 1 By default, this pipeline uses the container specified by the `--pyscenic_container` parameter. This is currently set to `aertslab/pyscenic:0.9.19`, which uses a container with both pySCENIC and Scanpy `1.4.4.post1` installed. A custom container can be used (e.g. one built on a local machine) by passing the name of this container to the `--pyscenic_container` parameter. ##### Expected output The output of this pipeline is a loom-formatted file (by default: `output/pyscenic_integrated-output.loom`) containing: * The original expression matrix * The pySCENIC-specific results: * Regulons (TFs and their target genes) * AUCell matrix (cell enrichment scores for each regulon) * Dimensionality reduction embeddings based on the AUCell matrix (t-SNE, UMAP) * Results from the parallel best-practices analysis using highly variable genes: * Dimensionality reduction embeddings (t-SNE, UMAP) * Louvain clustering annotations ## General requirements for this workflow * Python version 3.6 or greater * Tested on various Unix/Linux distributions (Ubuntu 18.04, CentOS 7.6.1810, MacOS 10.14.5) --- ## References and more information ### SCENIC * [SCENIC (R) on GitHub](https://github.com/aertslab/SCENIC) * [SCENIC website](http://scenic.aertslab.org/) * [SCENIC publication](https://doi.org/10.1016/j.cell.2018.05.057) * [pySCENIC on GitHub](https://github.com/aertslab/pySCENIC) * [pySCENIC documentation](https://pyscenic.readthedocs.io/en/latest/) * [VSN Pipelines](https://github.com/vib-singlecell-nf/vsn-pipelines), a repository of pipelines for single-cell data in Nextflow DSL2, including an implementation of pySCENIC. ### SCope * [SCope webserver](http://scope.aertslab.org/) * [SCope on GitHub](https://github.com/aertslab/SCope) * [SCopeLoomR](https://github.com/aertslab/SCopeLoomR) * [SCopeLoomPy](https://github.com/aertslab/SCopeLoomPy) ### Scanpy * [Scanpy on GitHub](https://github.com/theislab/scanpy) * [Scanpy documentation](https://scanpy.readthedocs.io/) * [Scanpy publication](https://doi.org/10.1186/s13059-017-1382-0)