Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

将表型与特定基因表达谱联系起来是生物学中一个极其重要的问题，主要通过相关方法或更根本地通过研究基因扰动的影响来解决这个问题。然而，全基因组扰动涉及大量的实验工作，这对于某些生物体来说可能是令人望而却步的。另一方面，各种表型的表征常常需要专家的主观解释，例如组织病理学家根据复杂的视觉特征（例如“腺泡结构”）对组织切片图像的描述。在本文中，我们使用深度学习来消除这些视觉组织学特征固有的主观性质，并将它们与基因组数据联系起来，从而在转录组和表型之间建立更精确的可量化相关性。使用整个幻灯片图像的数据集以及来自 39 种正常组织类型的匹配基因表达数据，我们首先开发了准确度为 94% 的深度学习组织分类器。然后，我们搜索其表达与分类器推断的特征相关的基因，并证明深度学习可以自动导出与转录组密切相关的视觉（表型）特征，因此具有生物学解释性。由于我们特别关注推断的组织学模型的可解释性和可解释性，因此我们还开发了推断特征的可视化，并将它们与免疫组织化学确定的基因表达模式进行比较。这可以被视为弥合基因水平和组织细胞组织之间差距的第一步。