  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.
