Modeling Time-course Gene Expression Data through Bayesian Partition Functional Principal Component Analysis

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.