LINGER

Introduction

LINGER (LIfelong neural Network for GEne Regulation) is a novel method to infer GRNs from single-cell multiome data built on top of PyTorch.

LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference.

Analysis tasks for single cell multiome data

• Infer gene regulatory network
• Benchmark gene regulatory network
• Explainable dimensionality reduction (transcription factor activity, availiable for single cell or bulk RNA-seq data)
• In silico pertubation

In the user guide, we provide an overview of each task.

Basic installation

LINGER can be installed by pip

conda create -n LINGER python==3.10.0
conda activate LINGER
pip install LingerGRN==1.96
conda install bioconda::bedtools # Requirment

Documentation

We provide several tutorials and user guide. If you find our tool useful for your research, please consider citing the LINGER manuscript.

User guide	PBMCs tutorial	H1 cell line tutorial
GRN benchmark	In silico perturbation	Other species
Downstream analysis-Module detection	Downstream analysis-TF Driver score

Reference

If you use LINGER, please cite:

Yuan, Qiuyue, and Zhana Duren. "Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data." Nature Biotechnology (2024): 1-11.