在组织表征和癌症诊断中，多式联运成像已成为一种强大的技术。由于计算进步，可以利用大型数据集来改善病理中的诊断和发现模式。但是，这需要高效且可扩展的图像检索方法。跨型号图像检索特别要求，因为在不同方式捕获的相同内容的图像可以显示很少的常见信息。我们提出了一种基于内容的图像检索系统（CBIR），用于反向（子）图像搜索，以给定个模态中的显微镜图像给出给定由不同的模态捕获的相应图像，其中图像不对齐并且仅共享少量结构。我们建议将深度学习结合生成嵌入共同空间中的模型的陈述，具有经典，快速，强大的特征提取器（Sift，Surf），以创建一个用于有效可靠的检索的文字模型。我们独立的自主方法显示了有希望的明菲尔德和二次谐波产生显微镜图像的公共数据集。我们获得75.4％和83.6％的前10名检索成功，以便在一个或另一个方向中检索。我们所提出的方法显着优于原始多模式（子）图像的直接检索，以及它们对相应的生成对抗网络（GaN）的图像到图像转换。我们确定所提出的方法与最近的子图像检索工具包，GAN的图像到图像翻译和用于跨模型图像检索的下游任务的学习特征提取器更好。我们突出了后一种方法的缺点，并遵守CBIR管道中学习陈述和特征提取器的体征和不变性属性的重要性。代码将在github.com/mida-group上获得。

Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the interpretability of latent variables as biological processes that define a cell's identity. Outside of biological applications, this problem is commonly referred to as learning disentangled representations. Although several disentanglement-promoting variants of the VAE were introduced, and applied to single-cell genomics data, this task has been shown to be infeasible from independent and identically distributed measurements, without additional structure. Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic. Here, we extend the application of these methodological advances to the analysis of single-cell genomics data with genetic or chemical perturbations. More precisely, we propose a deep generative model of single-cell gene expression data for which each perturbation is treated as a stochastic intervention targeting an unknown, but sparse, subset of latent variables. We benchmark these methods on simulated single-cell data to evaluate their performance at latent units recovery, causal target identification and out-of-domain generalization. Finally, we apply those approaches to two real-world large-scale gene perturbation data sets and find that models that exploit the sparse mechanism shift hypothesis surpass contemporary methods on a transfer learning task. We implement our new model and benchmarks using the scvi-tools library, and release it as open-source software at \url{https://github.com/Genentech/sVAE}.