Registration for Image-Based Transcriptomics: Parametric Signal Features and Multivariate Information Measures

Abstract

AbstractImage-based transcriptomics involves determining spatial patterns in gene expression across cells and tissues. Image registration is a necessary component of data analysis pipelines that study gene expression levels across different cells and intracellular structures. We consider images from multiplexed single molecule fluorescent in situ hybridization (smFISH) and multiplexed in situ sequencing (ISS) datasets from the Human Cell Atlas project and demonstrate a novel approach to groupwise image registration using a parametric representation of images based on finite rate of innovation sampling, together with practical optimization of empirical multivariate information measures.