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Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities

机译:架构:公制学习使得可解释合成异质单细胞样式

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摘要

Integration of simultaneously assayed modalities using Schema. a Schema is designed for assays where multiple modalities are simultaneously measured for each cell. The researcher designates one high-confidence modality as the primary (i.e., reference) and one or more of the remaining modalities as secondary. b Each modality’s observations are mapped to points in a multi-dimensional space, with an associated distance metric that encapsulates modality-specific similarity between observations. Across the three graphs, the dashed and dotted lines indicate distances between the same pairs of observations. c Schema transforms the primary modality space by scaling each of its dimensions so that the distances in the transformed space have a higher (or lower, as desired) correlation with corresponding distances in the secondary modalities; arbitrary distance metrics are allowed for the latter. Importantly, the transformation is provably guaranteed to limit the distortion of the original space, thus ensuring that information in the primary modality is preserved. d The new point locations represent information synthesized from multiple modalities into a coherent structure. To compute the transformation, Schema weights features in the primary modality by their importance to its objective; we have found this feature selection aspect very useful in biological interpretation of its results
机译:使用模式集成同时测定的方式。该模式被设计用于测定,其中对于每个细胞同时测量多种模式。研究人员将一个高置信态度指定为次级(即,参考)和一个或多个次要模式。 B每个模态的观察被映射到多维空间中的点,其中相关距离度量封装观察之间的模态特定的相似度。在三个图中,虚线和虚线表示同一对观察之间的距离。 C架构通过缩放其每个尺寸来使主模态空间变换,使得变换空间中的距离具有更高的(或更低的,根据需要的)相关性与次级方式中的相应距离;后者允许任意距离指标。重要的是,可被证明可以保证转换以限制原始空间的失真,从而确保保留主模块中的信息。 D新点位置代表从多个模态合成的信息到相干结构中。要计算转换,架构权重的特征在主要模型中的重要性至其目标;我们发现这个特征选择方面非常有用于其结果的生物解释

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