Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference.

ABSTRACT: Identifying the causal interactions in gene-regulatory networks requires an accurate understanding of the time-lagged relationships between transcription factors and their target genes. Here we describe DELAY (short for Depicting Lagged Causality), a convolutional neural network for the inference of gene-regulatory relationships across pseudotime-ordered single-cell trajectories. We show that combining supervised deep learning with joint probability matrices of pseudotime-lagged trajectories allows the network to overcome important limitations of ordinary Granger causality-based methods, for example, the inability to infer cyclic relationships such as feedback loops. Our network outperforms several common methods for inferring gene regulation and, when given partial gr

SUBMITTER: Reagor CC 

PROVIDER: S-EPMC10129065 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature