journal.pone.0114749.pdf (1.41 MB)
FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis
journal contribution
posted on 2023-06-08, 19:26 authored by Alex D Herbert, Antony CarrAntony Carr, Eva HoffmannAccurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.
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Publication status
- Published
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- Published version
Journal
PLoS ONEISSN
1932-6203Publisher
Public Library of ScienceExternal DOI
Issue
12Volume
9Article number
e114749Department affiliated with
- Sussex Centre for Genome Damage Stability Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2015-01-08First Open Access (FOA) Date
2015-01-08First Compliant Deposit (FCD) Date
2015-01-08Usage metrics
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