Statistical tests for measures of colocalization in biological microscopy

John H. Mcdonald, Kenneth Dunn

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

Colocalization analysis is the most common technique used for quantitative analysis of fluorescence microscopy images. Several metrics have been developed for measuring the colocalization of two probes, including Pearson's correlation coefficient (PCC) and Manders' correlation coefficient (MCC). However, once measured, the meaning of these measurements can be unclear; interpreting PCC or MCC values requires the ability to evaluate the significance of a particular measurement, or the significance of the difference between two sets of measurements. In previous work, we showed how spatial autocorrelation confounds randomization techniques commonly used for statistical analysis of colocalization data. Here we use computer simulations of biological images to show that the Student's one-sample t-test can be used to test the significance of PCC or MCC measurements of colocalization, and the Student's two-sample t-test can be used to test the significance of the difference between measurements obtained under different experimental conditions.

Original languageEnglish
Pages (from-to)295-302
Number of pages8
JournalJournal of Microscopy
Volume252
Issue number3
DOIs
StatePublished - Dec 2013

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Microscopy
Students
Statistical Data Interpretation
Spatial Analysis
Random Allocation
Fluorescence Microscopy
Computer Simulation

Keywords

  • Colocalization
  • Pearson's correlation coefficient
  • Student's t-test

ASJC Scopus subject areas

  • Histology
  • Pathology and Forensic Medicine

Cite this

Statistical tests for measures of colocalization in biological microscopy. / Mcdonald, John H.; Dunn, Kenneth.

In: Journal of Microscopy, Vol. 252, No. 3, 12.2013, p. 295-302.

Research output: Contribution to journalArticle

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