Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectively, to screen cell type-specific ASD-associated genes and gene sets. These two kinds of predictive models can predict the diagnosis of a nucleus with known cell type. Then, we construct a multi-label predictive model for predicting the cell type and diagnosis of a nucleus at the same time. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. The functions of genes with predictive power for ASD are different and the top important genes are distinct across different cells, highlighting the cell-type heterogeneity of ASD. The constructed predictive models can promote the diagnosis of ASD, and the prioritized cell type-specific ASD-associated genes and gene sets may be used as potential biomarkers of ASD.

自闭症谱系障碍（ASD）的大量转录组学分析揭示了失调的途径，而ASD的脑细胞类型特异性分子病理学仍需要研究。可以针对ASD进行基于机器学习的研究，对高可信度的基因候选者进行优先排序并促进有效干预措施的设计。使用ASD和控件的人脑核基因表达，我们分别基于单个基因和基因集构建针对ASD的细胞类型特异性预测模型，以筛选与细胞类型特异性ASD相关的基因和基因集。这两种预测模型可以预测已知细胞类型的细胞核的诊断。然后，我们构建了一个多标签预测模型，用于同时预测细胞类型和诊断细胞核。我们的发现表明，第2/3和第4层兴奋性神经元，第5/6层皮层皮质投射神经元，小白蛋白中间神经元和原生质星形胶质细胞在ASD中受到优先影响。具有对ASD预测能力的基因的功能是不同的，并且最重要的基因在不同的细胞之间是不同的，这突出了ASD的细胞类型异质性。构建的预测模型可以促进ASD的诊断，并且优先与细胞类型相关的特定ASD相关基因和基因集可以用作ASD的潜在生物标记。突出了ASD的细胞类型异质性。构建的预测模型可以促进ASD的诊断，并且优先与细胞类型相关的特定ASD相关基因和基因集可以用作ASD的潜在生物标记。强调了ASD的细胞类型异质性。构建的预测模型可以促进ASD的诊断，并且优先与细胞类型相关的特定ASD相关基因和基因集可以用作ASD的潜在生物标记。