Multimodal single-cell technologies enable the simultaneous collection of
diverse data types from individual cells, enhancing our understanding of
cellular states. However, the integration of these datatypes and modeling the
interrelationships between modalities presents substantial computational and
analytical challenges in disease biomarker detection and drug discovery.
Established practices rely on isolated methodologies to investigate individual
molecular aspects separately, often resulting in inaccurate analyses. To
address these obstacles, distinct Machine Learning Techniques are leveraged,
each of its own kind to model the co-variation of DNA to RNA, and finally to
surface proteins in single cells during hematopoietic stem cell development,
which simplifies understanding of underlying cellular mechanisms and immune
responses. Experiments conducted on a curated subset of a 300,000-cell time
course dataset, highlights the exceptional performance of Echo State Networks,
boasting a remarkable state-of-the-art correlation score of 0.94 and 0.895 on
Multi-omic and CiteSeq datasets. Beyond the confines of this study, these
findings hold promise for advancing comprehension of cellular differentiation
and function, leveraging the potential of Machine Learning.

利用多模式单细胞技术，通过整合不同数据类型并建模模态之间的相互关系，为疾病生物标志物检测和药物发现提供了改进，实验结果表明机器学习技术对于建模细胞发展中 DNA 到 RNA 及蛋白质之间的协变性具有显著效果，有望推动对细胞分化和功能的理解并发挥机器学习的潜能。

基于回归的多模态单细胞数据整合策略分析

Regression-Based Analysis of Multimodal Single-Cell Data Integration  Strategies

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。