Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGS

TL;DR

This paper introduces ICGS, a novel Bayesian Network-based method for identifying interpretable, development-specific gene signatures in the human prefrontal cortex, aiding understanding of neurodevelopment and disorders.

Contribution

The paper presents a new gene selection approach combining contrastive learning, VAE, and Markov Blanket to identify causally relevant gene signatures in human PFC.

Findings

Effectively identified development-specific gene signatures in human PFC.

Gene signatures reveal key biological processes and pathways.

Potential implications for neurodevelopment disorder research.

Abstract

In this paper, to unveil interpretable development-specific gene signatures in human PFC, we propose a novel gene selection method, named Interpretable Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to represent causality between multiple gene variables and a development variable. The proposed ICGS method combines the positive instances-based contrastive learning with a Variational AutoEncoder (VAE) to obtain this optimal BN structure and use a Markov Blanket (MB) to identify gene signatures causally related to the development variable. Moreover, the differential expression genes (DEGs) are used to filter redundant genes before gene selection. In order to identify gene signatures, we apply the proposed ICGS to the human PFC single-cell transcriptomics data. The experimental results demonstrate that the proposed method can effectively identify interpretable development-specific gene signatures in human PFC. Gene ontology enrichment analysis and ASD-related gene analysis show that these identified gene signatures reveal the key biological processes and pathways in human PFC and have more potential for neurodevelopment disorder cure. These gene signatures are expected to bring important implications for understanding PFC development heterogeneity and function in humans.