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

Rebecca Chen, Abhinav B. Das, Lav R. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 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.

Original languageEnglish (US)
Title of host publication2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728111513
DOIs
StatePublished - Apr 16 2019
Event53rd Annual Conference on Information Sciences and Systems, CISS 2019 - Baltimore, United States
Duration: Mar 20 2019Mar 22 2019

Publication series

Name2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019

Conference

Conference53rd Annual Conference on Information Sciences and Systems, CISS 2019
Country/TerritoryUnited States
CityBaltimore
Period3/20/193/22/19

Keywords

  • finite rate of innovation
  • image registration
  • image-based transcriptomics
  • multiinformation

ASJC Scopus subject areas

  • Information Systems

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