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The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human peripheral blood mononuclear cells (PBMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

Heart Failure (HF) is a leading cause of mortality among older-adults (OA); however, engaging in moderate-to-vigorous intensity of physical activity (MVPA) improves survival rates. Limited research has applied behavior theory to understand determinants of MVPA among OA with HF and compared the effects between races. This study aimed to use the Health Belief Model (HBM) to compare MVPA determinants between Black and White OA diagnosed with HF. Methods: The HF-ACTION Trial is a multi-site trial that helped facilitate physical activity among individuals with HF. The present study used structural equation modeling to test the change in HBM determinants and MVPA across 12 months among OA. Results: Participants were OA 72.28 (SD= 5.41) years old, (Black: n= 230; White, n= 441). The model found MVPA to be facilitated by perceived benefits (Black: β=.27, p=.001; White: β=.28; p<.001), self-efficacy (Black: β=.20, p=.003; White: β=.24; p<.001) and deterred by perceived barriers (Black: β=-.25, p=.001; White β=-.24; p<.001). Perceived threats deterred Black OA (β=-.23; p=.002) but not White OA (β=-.07; p=.278) from MVPA. Experimental arm predicted the development of self-efficacy (Black: β=.21. p=.001; White: β=.23; p=.001), and perceived benefits (Black: β=.21. p=.001; White: β=.23; p=.001) but not barriers (p>.05) for both races. Conclusions: Perceived barriers was a strong determinant to MVPA, and it was not resolved in the experimental arm for either race. The experimental arm did not resolve perceived threat, which was a deterrent for Black OA. Individual characteristics need to be considered when designing programs for HF patients to reduce health disparity.

The relation between electrophysiology and BOLD‐fMRI requires further elucidation. One approach for studying this relation is to find time‐frequency features from electrophysiology that explain the variance of BOLD time‐series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD‐fMRI data. We propose a framework for extracting the spatial distribution of these time‐frequency features while also estimating more flexible, region‐specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD‐fMRI and can be used to construct estimates of BOLD time‐series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task‐based and resting‐state EEG‐fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter‐subject variability with regards to EEG‐to‐BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.

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