PT  - JOURNAL ARTICLE
AU  - Rongqian Zhang
AU  - Lindsay D. Oliver
AU  - Aristotle N. Voineskos
AU  - Jun Young Park
TI  - A structured multivariate approach for removal of latent batch effects
AID  - 10.1101/2022.08.01.502396
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.08.01.502396
4099  - http://biorxiv.org/content/early/2022/08/03/2022.08.01.502396.short
4100  - http://biorxiv.org/content/early/2022/08/03/2022.08.01.502396.full
AB  - Combining data collected from multiple studies is becoming common and is advantageous to researchers to increase the reproducibility of scientific discoveries. However, at the same time, unwanted “batch effects” are commonly observed across neuroimaging data collected from multiple study sites or scanners, rendering difficulties in combining such data to obtain reliable findings. While methods for handling such unwanted variations have been proposed recently, most of them use univariate approaches which would be too simple to capture all sources of batch effects which could be represented by the batch-specific latent patterns. In this paper, we propose a novel multivariate harmonization method, called UNIFAC harmonization, for estimating and removing both explicit and latent batch effects. Our approach is based on the simultaneous dimension reduction and factorization of interlinked matrices through a penalized objective, which provides a new direction in neuroimaging research for harmonizing multivariate features across batches. Using the Social Processes Initiative in Neurobiology of the Schizophrenia (SPINS) dataset and extensive simulation studies, we show that UNIFAC harmonization performed better than the existing methods in entirely removing batch effects as well as retaining associations of interest to increase statistical power. The proposed method is publicly available as a R package.Competing Interest StatementThe authors have declared no competing interest.