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 [email protected]:saezlab/greta.git
cd greta

Then create a new enviroment specific for Snakemake:

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

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:

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/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