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Statistical tests for measures of colocalization in biological microscopy.

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.

SUBMITTER: McDonald JH

PROVIDER: S-EPMC4428547 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Statistical tests for measures of colocalization in biological microscopy.

McDonald John H JH Dunn Kenneth W KW

Journal of microscopy 20131010 3

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 ...[more]

PMID: 24117417

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Project description:MotivationColocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure.ResultsWe present the circular colocalization affinity with network structures test (CIRCOAST), a novel statistical hypothesis test to probe for enriched network colocalization in 2D z-projected multichannel images by using agent-based Monte Carlo modeling and image processing to generate the pseudo-null distribution of random cell placement unique to each image. This hypothesis test was validated by confirming that adipose-derived stem cells (ASCs) exhibit enriched colocalization with endothelial cells forming arborized networks in culture and then applied to show that locally delivered ASCs have enriched colocalization with murine retinal microvasculature in a model of diabetic retinopathy. We demonstrate that the CIRCOAST test provides superior power and type I error rates in characterizing intercellular colocalization compared to generic approaches that are confounded by changes in cell or vessel density.Availability and implementationCIRCOAST source code available at: https://github.com/uva-peirce-cottler-lab/ARCAS.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Microarray technology has enabled the measurement of comprehensive transcriptomic information. However, each data entry may reflect trivial individual differences among samples and also contain technical noise. Therefore, the certainty of each observed difference should be confirmed at earlier steps of the analyses, and statistical tests are frequently used for this purpose. Since a microarray measures a huge number of genes and the results are processed simultaneously, concerns regarding problems of multiplicity have been raised to the tests. To deal with these problems, several methodologies have been proposed, making the tests very conservative. Indeed, arbitrary tuning of the test threshold has also been introduced to relax the test conditions. However, the appropriateness of the multiplicity problems as well as the compensation methods has not been confirmed. The appropriateness of the compensation methods was checked by means of coincidence of the premises of the methodologies with the observed characteristics found in real data of two typical platforms of microarray analysis. Normality was observed in within-group data variations, supporting applications of parametric tests. However, genes displayed their own tendencies in the magnitude of variations, and the distributions of P-values were rather complex and varied; these characteristics are inconsistent with the premises of the compensation methodologies. Additionally, the appropriateness of the proposed multiplicities is reconsidered. When we observed differences in the transcriptome, the family-wise error rate should not be considered, since analyses at higher levels would not be influenced by a few false positives among the huge numbers of true information. Likely, concerns for a false discovery rate are not suitable for the point null hypotheses on expression levels, since the rate of true null hypotheses should be rare in contradiction to the premise of the methodology. Although compensation methods have been recommended to deal with the problem of multiplicity, the compensations are actually inappropriate for many of the applications of transcriptome analyses. Compensations are not only unnecessary, but will increase the occurrence of false negative errors, and arbitrary adjustment of the threshold damages the objectivity of the tests. Rather, the results of parametric tests should be evaluated directly. This SuperSeries is composed of the SubSeries listed below.

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Statistical tests for measures of colocalization in biological microscopy.

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Statistical tests for measures of colocalization in biological microscopy.

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