AI 中文总结
研究针对高维时程基因表达数据降维等挑战，提出贝叶斯分区函数主成分分析（PFPCA），结合混合模型与多元函数主成分分析，开发变分算法，模拟和实际应用均显示其能准确恢复结构并揭示相关特征。
AI 中文摘要
高维生物标志物如基因表达水平现在常随时间测量，以便动态研究生物过程。但现有方法无法充分应对此类数据带来的挑战，如降维、量化个体间变异性及揭示生物标志物间共享的时间结构。我们引入分区函数主成分分析（PFPCA），一种贝叶斯模型，能联合学习共享时间模式并根据潜在动态对变量聚类。它结合混合模型与每组内的多元函数主成分分析。我们开发了一种可扩展的平均场变分算法用于联合推断函数主成分载荷、个体水平得分、组分配和分区大小。模拟显示联合推断有明显优势，PFPCA比两步基线更准确地恢复分区和潜在功能结构。应用于H3N2流感病毒感染个体的纵向基因表达数据时，PFPCA识别出具有协调激活模式的基因组，并揭示与免疫反应动态和症状状态相关的时间特征。
英文摘要
High-dimensional biomarkers such as gene expression levels are now routinely measured over time, allowing biological processes to be studied dynamically rather than through cross-sectional snapshots. However, existing methods do not adequately address the central applied challenges posed by such data: simultaneously reducing dimensionality, quantifying inter-individual variability and uncovering temporal structure shared across biomarkers. We introduce Partition Functional Principal Component Analysis (PFPCA), a Bayesian model that jointly learns shared temporal patterns and clusters variables according to their latent dynamics. PFPCA combines a mixture model with multivariate functional principal component analysis performed within each group. We develop a scalable mean-field variational algorithm for joint inference of functional principal component loadings, individual-level scores, group assignments and partition sizes. Simulations show clear gains from joint inference: PFPCA recovers both the partition and the latent functional structure more accurately than a two-step baseline. In the most challenging settings, PFPCA retrieves the true partition in 27% of replicates compared with 1% for the two-step baseline. Applied to longitudinal gene-expression data from individuals experimentally infected with H3N2 influenza virus, PFPCA identifies groups of genes with coordinated activation patterns and reveals temporal signatures associated with immune-response dynamics and symptom status.
