MOTIVATION: Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model (GGM) for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships. Additionally, GGM assumes data follows a multivariate Gaussian distribution, which is often not the case with zero-inflated scRNA-seq data. RESULTS: We propose an Adaptive Joint Graphical Model (AJGM) for estimating multiple gene networks from single-cell or bulk data with unknown heterogeneity. In AJGM, an overall network is introduced to capture relationships shared by all samples. The model establishes connections between the subtype networks and the overall network through adaptive weights, enabling it to focus more effectively on gene relationships shared across all networks, thereby enhancing the accuracy of network estimation. On synthetic data, the proposed approach outperforms existing methods in terms of sample classification and network inference, particularly excelling in the identification of shared relationships. Applying this method to gene expression data from triple-negative breast cancer confirms known gene pathways and hub genes, while also revealing novel biological insights. AVAILABILITY AND IMPLEMENTATION: The Python code and demonstrations of the proposed approaches are available at https://github.com/yyytim/AJGM, and the software is archived in Zenodo with DOI: 10.5281/zenodo.14740972.

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

BACKGROUND: For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.