Single-cell multimodal assays that simultaneously profile chromatin accessibility and gene expression could predict tissue-specific causal links between noncoding loci and the genes they affect. However, current computational strategies either neglect the causal relationship between chromatin accessibility and transcription or lack variant-level precision, aggregating data across genomic ranges due to data sparsity.

We introduce GrID-Net, a graph neural network approach that generalizes Granger causal inference to detect new causal locus-gene associations in graph-structured systems such as single-cell trajectories. Inspired by the principles of optical parallax, which reveals object depth from static snapshots, we hypothesized that causal mechanisms could be inferred from static single-cell snapshots by exploiting the time lag between epigenetic and transcriptional cell states, a concept we term "cell-state parallax."

GrID-Net is described in the bioRxiv preprint, Unveiling causal regulatory mechanisms through cell-state parallax by Alexander P. Wu*, Rohit Singh*, Christopher A. Walsh, and Bonnie Berger.