A Bayesian mixture of multivariate FPCAs jointly infers gene partitions and shared temporal scores, recovering true groups far more often than two-step FPCA-plus-clustering and revealing influenza immune signatures.
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Modeling Time-course Gene Expression Data through Bayesian Partition Functional Principal Component Analysis
A Bayesian mixture of multivariate FPCAs jointly infers gene partitions and shared temporal scores, recovering true groups far more often than two-step FPCA-plus-clustering and revealing influenza immune signatures.