Unbiased and robust analysis of co-localization in super-resolution images

Xueyan Liu, Clifford S. Guy, Emilio Boada-Romero, Douglas R. Green, Margaret E. Flanagan, Cheng Cheng, Hui Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy, provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false-positive errors and are restricted to only 2-dimensional images. In this paper, we develop a novel statistical method to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of co-localization of protein light chain 3 and lysosomal-associated membrane protein 1 in cell autophagy. Moreover, this method is directly applicable to co-localization analyses in other disciplines, such as diagnostic imaging, epidemiology, environmental science, and ecology.

Original languageEnglish (US)
Pages (from-to)1484-1499
Number of pages16
JournalStatistical Methods in Medical Research
Issue number8
StatePublished - Aug 2022
Externally publishedYes


  • Co-localization
  • Pearson’s correlation
  • point process
  • spatial statistics
  • stochastic optical reconstruction microscopy
  • super-resolution images

ASJC Scopus subject areas

  • Health Information Management
  • Epidemiology
  • Statistics and Probability


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