Structured correlation detection with application to colocalization analysis in dual-channel fluorescence microscopic imaging

Shulei Wang, Jianqing Fan, Ginger Pocock, Ellen T. Arena, Kevin W. Eliceiri, Ming Yuan

Research output: Contribution to journalArticlepeer-review

Abstract

Current work flows for colocalization analysis in fluorescence microscopic imaging introduce significant bias in terms of the user's choice of region of interest (ROI). In this work, we introduce an automatic, unbiased structured detection method for correlated region detection between two random processes observed on a common domain. We argue that although intuitive, using the maximum loglikelihood statistic directly suffers from potential bias and substantially reduced power. Therefore, we introduce a simple size-based normalization to overcome this problem. We show that scanning using the proposed statistic leads to optimal correlated region detection over a large collection of structured correlation detection problems.

Original languageEnglish (US)
Pages (from-to)333-360
Number of pages28
JournalStatistica Sinica
Volume31
Issue number1
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Colocalization analysis
  • Optimal rate
  • Scan statistics
  • Signal detection
  • Structured signal

Fingerprint Dive into the research topics of 'Structured correlation detection with application to colocalization analysis in dual-channel fluorescence microscopic imaging'. Together they form a unique fingerprint.

Cite this