Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization

Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan, Paul Thompson, Jiayu Zhou

Abstract:

Independent and identically distributed (i.i.d.) data is essential to
many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common
strategy that guarantees sufficient clinical diversity, determined
by the decentralized nature of medical data. However, data from
various sites are easily biased by the local environment or facilities,
thereby violating the i.i.d. rule. A common strategy is to harmonize
the site bias while retaining important biological information. The
ComBat is among the most popular harmonization approaches
and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in
training or evaluating data from unknown/unseen sites, ComBat
lacks compatibility and requires retraining with data from all the
sites. The retraining leads to significant computational and logistic
overhead that is usually prohibitive. In this work, we develop a
novel Cluster ComBat harmonization algorithm, which leverages
cluster patterns of the data in different sites and greatly advances
the usability of ComBat harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the
superiority of the proposed approach. Our codes are provided in
https://github.com/illidanlab/distributed-cluster-harmonization.