作者: Rebecca Chen , Abhinav B. Das , Lav R. Varshney
DOI: 10.1101/521898
关键词: Multivariate statistics 、 Fluorescence 、 Component (UML) 、 Parametric statistics 、 Image registration 、 Signal 、 Image (mathematics) 、 Artificial intelligence 、 In situ hybridization 、 Sampling (statistics) 、 Computer science 、 Representation (mathematics) 、 Pattern recognition
摘要: Image-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 levels different intracellular structures. We consider images from multiplexed single molecule fluorescent situ hybridization (smFISH) sequencing (ISS) datasets the Human Cell Atlas project demonstrate novel approach to groupwise image using parametric representation based on finite rate innovation sampling, together with practical optimization empirical multivariate information measures.