Integrative Learning of Semi-Confirmatory Factor Analysis for Multi-Site High Dimensional Data

Music City Center 

Integrating datasets from multiple sites has become increasingly common in various scientific fields, such as neuroimaging studies. Factor analysis is a widely used statistical method for elucidating relationships between multivariate observations and identifying the underlying factors that explain their interdependencies. However, there has been limited exploration on multi-site factor analysis that accounts for the variability and heterogeneity inherent in data collected from multiple sites or institutions. In this talk, we propose an integrative semi-confirmatory factor analysis (i-SCFA) model, that identifies shared latent factors across sites while accommodating site-specific heterogeneity in factor scores. The i-SCFA model relaxes the requirement for the prior knowledge of "non-zero loadings" in confirmatory factor analysis (CFA) by collaboratively learning the latent covariance structure across multiple sites. With its computational efficiency in identifying latent structures and providing closed-form solutions for CFA parameters, i-SCFA is particularly well-suited for high-throughput datasets. We demonstrate the empirical performance of i-SCFA through extensive simulations and apply it to a multi-site neuroimaging study. The empirical performance of i-SCFA is assessed through extensive simulations and demonstrated with the multi-site neuroimaging analysis of the Adolescent Brain Cognitive Development Study.

Adolescent Brain Cognitive Development Study

Factor analysis

Multi-site study

Network structure

Neuroimaging