Gene Regulatory nETwork Analysis (GRETA)

Cells regulate their functions through gene expression, driven by a complex interplay of transcription factors and other regulatory mechanisms that together can be modeled as gene regulatory networks (GRNs). The emergence of single-cell multi-omics technologies has driven the development of several methods that integrate transcriptomics and chromatin accessibility data to infer GRNs. Gene Regulatory nETwork Analysis (GRETA) is a Snakemake pipeline that implements state-of-the-art multimodal GRN inference methods. It organizes the steps of these methods into a modular framework, enabling users to infer, compare, and benchmark GRN approaches.

Installation

Clone repo:

git clone git@github.com:saezlab/greta.git
cd greta

In some cases, execute permissions must be granted to the SLURM configuration files before running GRETA. You can do this with chmod +x config/slurm/*.py

Then create a new enviroment specific for Snakemake:

mamba create -c conda-forge -c bioconda -n snakemake snakemake=7.32.4
mamba activate snakemake

This pipeline uses Singularity for containerized execution. It must be installed at the system level prior to running. For installation instructions, refer to the official Singularity documentation(local, cluster) or contact your cluster administrator.

Overview

Due to the magnitude of datasets and analyses, the repository is organized as a reproducible Snakemake pipeline and uses singularity images to handle dependencies and SLURM to sumbit jobs:

greta/
├── config/
│   ├── slurm/            # Cluster configuration (assumes Slurm architecture)
│   ├── config.yaml       # Specifies methods, datasets, and databases
│   └── prior_cats.json   # Specifies database labels for each dataset
└── workflow/
    ├── envs/             # Singularity definition (.def) and image (.sif) files
    ├── rules/            # Snakemake rules for:
    │   ├── anl              # analyses
    │   ├── dbs              # databases
    │   ├── dts              # datasets
    │   ├── mth              # methods
    │   └── plt              # plots
    ├── scripts/          # Helper scripts for:
    │   ├── anl              # analyses
    │   ├── dbs              # databases
    │   ├── dts              # datasets
    │   ├── mth              # methods
    │   └── plt              # plots
    └── Snakefile         # Main Snakemake file

Here are some lines to generate important intermediate outputs:

# Downloads and processes a dataset, for example pbmc10k
snakemake --profile config/slurm/ dts/pbmc10k/cases/all/mdata.h5mu

# Computes Pando's preprocessing step on the pbmc10k dataset
snakemake --profile config/slurm/ dts/pbmc10k/cases/all/runs/pando.pre.h5mu

# Computes GRaNIE's p2g step on Pando's pre
snakemake --profile config/slurm/ dts/pbmc10k/cases/all/runs/pando.granie.p2g.csv

# Computes CellOracles's tfb step on GRaNIE's p2g
snakemake --profile config/slurm/ dts/pbmc10k/cases/all/runs/pando.granie.celloracle.tfb.csv

# Computes Dictys's mdl step on the previous results
snakemake --profile config/slurm/ dts/pbmc10k/cases/all/runs/pando.granie.celloracle.dictys.mdl.csv

# Runs all possible method combinations, baselines and original implementations
snakemake --profile config/slurm/ anl/topo/pbmc10k.all.sims_mult.csv

# Downloads and processess all databases
snakemake --profile config/slurm/ anl/dbs/stats.csv

# Runs the mechanistic metric forecasting (perturbation) for all method combinations
snakemake --profile config/slurm/ anl/metrics/mech/prt/knocktf/pbmc10k.all.scores.csv

# Runs the benchmark for all databases and metrics
snakemake --profile config/slurm/ anl/metrics/summary/pbmc10k.all.csv

How to

• add methods
• add datasets
• add databases

Citation

Badia-i-Mompel et al. Comparison and evaluation of methods to infer gene regulatory networks from multimodal single-cell data. bioRxiv (2024) doi:10.1101/2024.12.20.629764