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MosaicIA: an ImageJ/Fiji plugin for spatial pattern and interaction analysis.

Shivanandan A, Radenovic A, Sbalzarini IF - BMC Bioinformatics (2013)

Bottom Line: If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another.Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets.The presented showcases illustrate the usage of the software.

View Article: PubMed Central - HTML - PubMed

Affiliation: MOSAIC Group, Center of Systems Biology Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr 108, 01307 Dresden, Germany. ivos@mpi-cbg.de.

ABSTRACT

Background: Analyzing spatial distributions of objects in images is a fundamental task in many biological studies. The relative arrangement of a set of objects with respect to another set of objects contains information about potential interactions between the two sets of objects. If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another. Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets. This can be used to generalize co-localization analysis to spatial interaction analysis. However, no user-friendly software for this type of analysis was available so far.

Results: We present an ImageJ/Fiji plugin that implements the complete workflow of spatial pattern and interaction analysis for spot-like objects. The plugin detects objects in images, infers the interaction potential that is most likely to explain the observed pattern, and provides statistical tests for whether an inferred interaction is significant given the number of objects detected in the images and the size of the space within which they can distribute. We benchmark and demonstrate the present software using examples from confocal and PALM single-molecule microscopy.

Conclusions: The present software greatly simplifies spatial interaction analysis for point patterns, and makes it available to the large user community of ImageJ and Fiji. The presented showcases illustrate the usage of the software.

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Related in: MedlinePlus

Results of applying the plugin to virus–endosome data from confocal microscopy. (a) Image X of the red channel showing adenovirus serotype 2 (Ad2) tagged with ATTO-647. (b) Image Y of the green channel showing Rab5-EGFP, a marker for endosomes. The results from object detection using MosaicIA are shown as overlaid red circles. Only a single 2D image is used here, and no z-stack. (c,d) Distance distributions obtained after fitting the data with a Plummer and step potential model, respectively. (e,f) The corresponding estimated interaction potentials. The Plummer potential leads to a 4-fold lower fitting error than the step potential.
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Figure 4: Results of applying the plugin to virus–endosome data from confocal microscopy. (a) Image X of the red channel showing adenovirus serotype 2 (Ad2) tagged with ATTO-647. (b) Image Y of the green channel showing Rab5-EGFP, a marker for endosomes. The results from object detection using MosaicIA are shown as overlaid red circles. Only a single 2D image is used here, and no z-stack. (c,d) Distance distributions obtained after fitting the data with a Plummer and step potential model, respectively. (e,f) The corresponding estimated interaction potentials. The Plummer potential leads to a 4-fold lower fitting error than the step potential.

Mentions: The results are shown in Figure 4. Figure 4a shows the image X of the virus particles after object detection in the plugin. The image Y of the endosomes after object detection is shown in Figure 4b. Figure 4c shows the observed NN distance distribution (blue curve), the expected distribution if viruses were distributed at random and independently of the endosomes, i.e., the context (red curve), and the best fit (green curve) with the Plummer potential shown in Figure 4e. Figure 4d shows the results when using the step potential shown in Figure 4f, corresponding to a context-corrected object-based co-localization count. The residual fitting error when using the Plummer potential is about 4-fold lower than when using the step potential, even though the latter also corrects for the context. The improvement stems from using continuous distance information. The error when using classical co-localization analysis without correcting for the context would be even larger.


MosaicIA: an ImageJ/Fiji plugin for spatial pattern and interaction analysis.

Shivanandan A, Radenovic A, Sbalzarini IF - BMC Bioinformatics (2013)

Results of applying the plugin to virus–endosome data from confocal microscopy. (a) Image X of the red channel showing adenovirus serotype 2 (Ad2) tagged with ATTO-647. (b) Image Y of the green channel showing Rab5-EGFP, a marker for endosomes. The results from object detection using MosaicIA are shown as overlaid red circles. Only a single 2D image is used here, and no z-stack. (c,d) Distance distributions obtained after fitting the data with a Plummer and step potential model, respectively. (e,f) The corresponding estimated interaction potentials. The Plummer potential leads to a 4-fold lower fitting error than the step potential.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219334&req=5

Figure 4: Results of applying the plugin to virus–endosome data from confocal microscopy. (a) Image X of the red channel showing adenovirus serotype 2 (Ad2) tagged with ATTO-647. (b) Image Y of the green channel showing Rab5-EGFP, a marker for endosomes. The results from object detection using MosaicIA are shown as overlaid red circles. Only a single 2D image is used here, and no z-stack. (c,d) Distance distributions obtained after fitting the data with a Plummer and step potential model, respectively. (e,f) The corresponding estimated interaction potentials. The Plummer potential leads to a 4-fold lower fitting error than the step potential.
Mentions: The results are shown in Figure 4. Figure 4a shows the image X of the virus particles after object detection in the plugin. The image Y of the endosomes after object detection is shown in Figure 4b. Figure 4c shows the observed NN distance distribution (blue curve), the expected distribution if viruses were distributed at random and independently of the endosomes, i.e., the context (red curve), and the best fit (green curve) with the Plummer potential shown in Figure 4e. Figure 4d shows the results when using the step potential shown in Figure 4f, corresponding to a context-corrected object-based co-localization count. The residual fitting error when using the Plummer potential is about 4-fold lower than when using the step potential, even though the latter also corrects for the context. The improvement stems from using continuous distance information. The error when using classical co-localization analysis without correcting for the context would be even larger.

Bottom Line: If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another.Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets.The presented showcases illustrate the usage of the software.

View Article: PubMed Central - HTML - PubMed

Affiliation: MOSAIC Group, Center of Systems Biology Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr 108, 01307 Dresden, Germany. ivos@mpi-cbg.de.

ABSTRACT

Background: Analyzing spatial distributions of objects in images is a fundamental task in many biological studies. The relative arrangement of a set of objects with respect to another set of objects contains information about potential interactions between the two sets of objects. If they do not "feel" each other's presence, their spatial distributions are expected to be independent of one another. Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets. This can be used to generalize co-localization analysis to spatial interaction analysis. However, no user-friendly software for this type of analysis was available so far.

Results: We present an ImageJ/Fiji plugin that implements the complete workflow of spatial pattern and interaction analysis for spot-like objects. The plugin detects objects in images, infers the interaction potential that is most likely to explain the observed pattern, and provides statistical tests for whether an inferred interaction is significant given the number of objects detected in the images and the size of the space within which they can distribute. We benchmark and demonstrate the present software using examples from confocal and PALM single-molecule microscopy.

Conclusions: The present software greatly simplifies spatial interaction analysis for point patterns, and makes it available to the large user community of ImageJ and Fiji. The presented showcases illustrate the usage of the software.

Show MeSH
Related in: MedlinePlus