Year: 2022
Type: conference-paper
Abstract: Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics. However, it is challenging to learn the joint representations from the multimodal data, model the relationship between modalities, and, more importantly, incorporate the vast amount of single-modality datasets into the downstream analyses. To address these challenges and correspondingly facilitate multimodal single-cell data analyses, three key tasks have been introduced: Modality prediction, Modality matching andJoint embedding. In this work, we present a general Graph Neural Network framework scMoGNN to tackle these three tasks and show that scMoGNN demonstrates superior results in all three tasks compared with the state-of-the-art and conventional approaches. Our method is an official winner in the overall ranking ofModality prediction from NeurIPS 2021 Competition (https://openproblems.bio/neurips_2021/), and all implementations of our methods have been integrated into DANCE package (https://github.com/OmicsML/dance).
Institution: Michigan State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: III: Small: Collaborative Research: Effective Labeled Data Generation via Generative Adversarial Learning, G125128811, G1432373144, CAREER: Real-World Networks: Modeling and Analysis of Signed Networks with Positive and Negative Links, SaTC: CORE: Small: Side-channel Attacks Against Mobile Users: Singularity Detection, Behavior Identification, and Automated Rectification, FW-HTF-RM: Intelligent Social Network Interventions to Augment Human Cognition for Interdisciplinary Interactions in Project Teams, III: Small: Unsupervised Feature Selection in the Era of Big Data, TRTech-PGR: Maize Hub: An Open-source Simulation Platform & Resources Integrating Extensive Field Trial Data, DNA Sequences, and Historical Weather Records, G7452299184, III: Medium: Collaborative Research: Towards Scalable and Interpretable Graph Neural Networks, TRTech-PGR: Connecting sequences to functions within and between species through computational modeling and experimental studies
OpenAlex ID: W4226218349