Welcome to Enhlink’s documentation!

_images/workflow_presentation.readme.png

Introduction

Enhlink is a fast, accurate, and efficient algorithm designed to perform linkage analysis, such as inferring enhanceer-promoter links, from single-cell ATAC-seq (scATAC-seq) datasets. It is also applicable to other single-cell genomic datasets (e.g., methylation, DNase, MNase, ChIP-seq) containing high-dimensional, sparse data with binary features.

Key Features

Versatile Application: Works with various single-cell genomic datasets, including scATAC-seq, methylation, DNase, MNase, and ChIP-seq.
Covariate Modeling: Allows modeling of covariates to enhance inference accuracy and can integrate into existing workflows.
Multi-Omic Integration: Optionally integrates multi-omic datasets, like gene expression, to boost the quality of linkage inference.
Predictive Feature Modeling: Models enhancers and covariates as predictive features of target accessibility or gene expression.
Modified Information Gain Score: Employs this metric within a random forest framework to estimate significant features linked to specific targets.
Second-Order Analysis: Identifies linkages that are specific to particular covariates.
Distal Enhancer-Gene Linkage: Capable of inferring linkages beyond proximal enhancers.
Simulation Workflow: Integrates a simulation workflow based on experimentally validated enhancer-promoter signals, improving prediction accuracy.

Introduction and Tutorials:

Access

The package is accessible at this link: https://gitlab.com/grouumf/enhlinktools/

Citation

Enhlink is published in Genome Biology: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-024-03374-9

License

GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007

Support

If you are having issues, please let us know. You can reach us using the following email address:

Olivier Poirion, Ph.D. o.poirion@gmail.com (personal) or olivier.poirion@jax.org (current work)

Data availability

The PCHi-C data used in this manuscript are available with currently a limited access through GEO with the following accession: GSE214107. The data are planned to be released to the public the 31 December 2023. In the meantime, they will be available on demand. The Linkage atlases for the islet and adipose datasets are available as Figshare datasets fith the following DOIs: https://doi.org/10.6084/m9.figshare.22336033.v1 (adipose) and https://doi.org/10.6084/m9.figshare.22335919.v1 (islet).

Module API: