Sparse recovery of imaging transcriptomics data

John P. Bryan, Brian Cleary, Samouil L. Farhi, Yonina C. Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Imaging transcriptomics (IT) techniques enable characterization of gene expression in cells in their native context by imaging barcoded mRNA probes with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods. We propose an algorithm for decoding lower magnification IT data than that used in standard experimental workflows. Our approach, Joint Sparse method for Imaging Transcriptomics (JSIT), incorporates codebook knowledge and sparsity assumptions into an optimization problem. Using simulated low-magnification data, we demonstrate that JSIT enables improved throughput and recovery performance over standard decoding methods.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
Pages802-806
Number of pages5
ISBN (Electronic)9781665412469
DOIs
StatePublished - 13 Apr 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period13/04/2116/04/21

Keywords

  • Compressed Sensing
  • Fluorescence Microscopy
  • Imaging Transcriptomics

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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