A New Approach to Batch Effect Removal Based on Distribution Matching in Latent Space Advanced measurement techniques such as genomics are capable of acquiring highthroughput data in high dimensions, enabling new scientific discoveries, and offering unique insights in biomedical research. However, biological measurements can be easily affected by systematic variations especially when those measures are obtained from distinct batches involving different platforms and experimental conditions. Such batch effect is usually larger than biological signal of interest and can cause invalid downstream analysis and false discovery if not properly handled. Here we proposed a new learning approach based on multivariate distribution matching in the latent space for batch effect removal while preserving signals of most interest. This new datadriven approach consists of three key components: an autoencoder trained to encode the data into lowdimension neurons that represent data pattern; a similarity measurement procedure to identify batcheffect associated neurons; and a residual networkbased matching framework to transform the affected neuronsu0027 distribution from one batch to another where the adjusted neurons will be decoded to reconstruct new datasets with batch effect removed. The effectiveness of the proposed approach has been validated in several ways using public genomic data on Alzheimer disease. This new method provides a highly promising tool for complex batcheffect adjustment and outperforms other commonly used methods.

Neural Processes MixedEffect Models for Deep Normative Modeling of Clinical Neuroimaging Data Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders. Current implementations rely on Gaussian process regression, which provides coherent estimates of uncertainty needed for the method but also suffers from drawbacks including poor scaling to large datasets and a reliance on fixed parametric kernels. In this paper, we propose a deep normative modeling framework based on neural processes (NPs) to solve these problems. To achieve this, we define a stochastic process formulation for mixedeffect models and show how NPs can be adopted for spatially structured mixedeffect modeling of neuroimaging data. This enables us to learn optimal feature representations and covariance structure for the randomeffect and noise via global latent variables. In this scheme, predictive uncertainty can be approximated by sampling from the distribution of these global latent variables. On a publicly available clinical fMRI dataset, we compare the novelty detection performance of multivariate normative models estimated by the proposed NP approach to a baseline multitask Gaussian process regression approach and show substantial improvements for certain diagnostic problems.