Many thanks to our amazing team, especially co-first author @mayayayas.bsky.social, and supervisors @randersson.bsky.social, @jengreitz.bsky.social

12/12

You can run scE2G on your data today using our pipeline here: github.com/EngreitzLab/...

We are looking forward to hearing your feedback on how scE2G works on your dataset!

11/12

We have also applied scE2G to identify and validate disease-related enhancers in the human coronary artery and fetal heart – check out these preprints to learn more!

Coronary artery: www.medrxiv.org/content/10.1...
Fetal heart: www.medrxiv.org/content/10.1...

10/12

We also integrate scE2G predictions with orthogonal information to prioritize causal genes and cell types for noncoding variants associated with complex traits.

For example, here we nominate regulatory interactions linking INPP4B and IL15 to lymphocyte counts in T cells.

9/12

For example, here we identify cell-type specific links for SPTA1 in erythroblasts and normoblasts.

8/12

We applied scE2G to over 40 cell types from PBMCs, BMMCs, and pancreatic islets, validating that scE2G predictions reflect expected patterns of cell-type specificity.

7/12

We show that scE2G has robust performance for cell types with at least 2 million total ATAC fragments and 1 million RNA UMIs – about 200-400 cells from a typical 10x Multiome experiment in a tissue.

6/12

Key features in scE2G include 1) ABC score, 2) Kendall correlation between peak accessibility and gene expression, and 3) whether the gene is “ubiquitously-expressed”.

Notably, the Kendall correlation improves long-range predictions and appears to detect stochastic transcriptional bursting.

5/12

In systematic benchmarking against CRISPR perturbations (below), fine-mapped eQTLs, and GWAS variant-gene associations, scE2G models outperforms existing single-cell models and distance-derived baselines.

We applied and extended ENCODE benchmarking pipelines: www.biorxiv.org/content/10.1...

4/12

Using a gold-standard CRISPR perturbation dataset, we trained two logistic regression models: scE2G (ATAC) and scE2G (Multiome), that use single-cell ATAC-seq or single-cell multiomic ATAC and RNA-seq data, respectively.

3/12

scE2G tackles the challenge of building cell-type-specific maps of enhancer-gene regulation.

If we can do this well, we can build enhancer maps across thousands of human cell types from emerging single-cell atlases to interpret genetic variants and understand gene regulation.

2/12