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: $\textit{modality prediction}$,
$\textit{modality matching}$ and $\textit{joint embedding}$. In this work, we
present a general Graph Neural Network framework $\textit{scMoGNN}$ to tackle
these three tasks and show that $\textit{scMoGNN}$ 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 of
$\textit{Modality prediction}$ from NeurIPS 2021 Competition, and all
implementations of our methods have been integrated into DANCE
package~https://github.com/OmicsML/dance.

本文介绍了一种名为 scMoGNN 的图神经网络框架，用于多模态单细胞技术数据的模态预测，模态匹配和联合嵌入，取得了 SOTA 的成果，并在 NeurIPS 2021 竞赛中获胜，所有实现都已整合到 DANCE 软件包中。