Statistical and visual differentiation of subcellular imaging

Hamilton, Nicholas A., Wang, Jack T. H., Kerr, Markus C. and Teasdale, Rohan. D. (2009) Statistical and visual differentiation of subcellular imaging. BMC Bioinformatics, 10 94.1-94.12. doi:10.1186/1471-2105-10-94

Author Hamilton, Nicholas A.
Wang, Jack T. H.
Kerr, Markus C.
Teasdale, Rohan. D.
Title Statistical and visual differentiation of subcellular imaging
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2009-03
Sub-type Article (original research)
DOI 10.1186/1471-2105-10-94
Open Access Status DOI
Volume 10
Start page 94.1
End page 94.12
Total pages 12
Editor Melissa Norton
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2010
Language eng
Subject C1
970106 Expanding Knowledge in the Biological Sciences
060102 Bioinformatics
Formatted abstract
Background Automated microscopy technologies have led to a rapid growth in imaging data on a scale comparable to that of the genomic revolution. High throughput screens are now being performed to determine the localisation of all of proteins in a proteome. Closer to the bench, large image sets of proteins in treated and untreated cells are being captured on a daily basis to determine function and interactions. Hence there is a need for new methodologies and protocols to test for difference in subcellular imaging both to remove bias and enable throughput. Here we introduce a novel method of statistical testing, and supporting software, to give a rigorous test for difference in imaging. We also outline the key questions and steps in establishing an analysis pipeline.

The methodology is tested on a high throughput set of images of 10 subcellular localisations, and it is shown that the localisations may be distinguished to a statistically significant degree with as few as 12 images of each. Further, subtle changes in a protein's distribution between nocodazole treated and control experiments are shown to be detectable. The effect of outlier images is also examined and it is shown that while the significance of the test may be reduced by outliers this may be compensated for by utilising more images. Finally, the test is compared to previous work and shown to be more sensitive in detecting difference. The methodology has been implemented within the iCluster system for visualising and clustering bio-image sets.

The aim here is to establish a methodology and protocol for testing for difference in subcellular imaging, and to provide tools to do so. While iCluster is applicable to moderate (<1000) size image sets, the statistical test is simple to implement and will readily be adapted to high throughput pipelines to provide more sensitive discrimination of difference.
Keyword Images
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 94

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
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Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 15 times in Scopus Article | Citations
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Created: Thu, 03 Sep 2009, 08:03:14 EST by Mr Andrew Martlew on behalf of Institute for Molecular Bioscience