Estimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease

Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregated view of the underlying gene regulations and can be confounded by heterogeneity in cell type compositions, failing to reveal gene coordination that may be distinct across different cell types. In this article, we introduce a flexible framework for estimating cell-type-specific gene co-expression networks from bulk sample data, without making specific assumptions on the distributions of gene expression profiles in different cell types. We develop a novel sparse least squares estimator, referred to as CSNet, that is efficient to implement and has good theoretical properties. Using CSNet, we analyzed the bulk gene expression data from a cohort study on Alzheimer’s disease and identified previously unknown cell-type-specific co-expressions among Alzheimer’s disease risk genes, suggesting cell-type-specific disease mechanisms. Supplementary materials for this article are available online.

推断并刻画基因共表达网络（gene co-expression networks），可为复杂疾病的分子机制提供重要见解。迄今为止，绝大多数共表达分析均基于从批量组织（bulk tissues）中采集的基因表达数据开展，而不同样本间的细胞类型组成存在差异。由此得到的共表达估计仅能反映潜在基因调控的整体视图，且易受细胞类型组成异质性的混杂影响，无法揭示不同细胞类型间可能存在差异的基因协同表达关联。本文提出一种灵活的分析框架，可从批量样本数据中估算细胞类型特异性（cell-type-specific）基因共表达网络，且无需对不同细胞类型的基因表达谱分布做出特定假设。本文开发了一种名为CSNet的新型稀疏最小二乘估计器，其实现效率高且具备优良的理论性质。借助CSNet，我们对阿尔茨海默病（Alzheimer’s disease）队列研究的批量基因表达数据进行了分析，在阿尔茨海默病风险基因中发现了此前未被报道的细胞类型特异性共表达模式，提示了细胞类型特异性的疾病发病机制。本文的补充材料可在线获取。