Experimental single-cell data often presents an incomplete picture due to its destructive nature: 1) we collect certain experimental measurements of cells but lack measurements under different experimental conditions or data modalities; 2) we collect data of cells at certain time points but lack measurements at other time points; or 3) we collect data of cells under certain perturbations but lack data for other types of perturbations. In this article, we will discuss machine learning approaches to address these types of translation and counterfactual problems. We will begin by giving an overview on single-cell biology applications and the relevant translation problems. Subsequently, we will provide an overview of approaches for multidomain alignment and translation in machine learning, including methods based on generative modeling, optimal transport, and causal inference. The bulk of this article will focus on how these approaches have been tailored and applied to important translation problems in single-cell biology, illustrated through concrete examples from our own work. We end with open problems and a perspective on how biology may not only be uniquely suited to being one of the greatest beneficiaries of machine learning but also one of the greatest sources of inspiration for it.

中文翻译：

---

单细胞数据集成和翻译的机器学习方法

由于其破坏性，实验性单细胞数据通常呈现不完整的画面：1）我们收集了某些细胞的实验测量值，但缺乏在不同实验条件或数据模式下的测量值；2）我们在某些时间点收集细胞数据，但在其他时间点缺乏测量；或 3）我们收集了某些扰动下的细胞数据，但缺乏其他类型扰动的数据。在本文中，我们将讨论解决这些类型的翻译和反事实问题的机器学习方法。我们将首先概述单细胞生物学应用和相关的翻译问题。随后，我们将概述机器学习中多域对齐和翻译的方法，包括基于生成建模的方法，最优传输和因果推理。本文的大部分内容将侧重于这些方法如何被定制并应用于单细胞生物学中的重要翻译问题，并通过我们自己工作中的具体示例进行说明。我们以开放性问题和关于生物学如何不仅特别适合成为机器学习的最大受益者之一而且也是它最大的灵感来源之一的观点结束。