Title |
Incorporating prior knowledge improves detection of differences in bacterial growth rate
|
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Published in |
BMC Systems Biology, September 2015
|
DOI | 10.1186/s12918-015-0204-9 |
Pubmed ID | |
Authors |
Lydia M Rickett, Nick Pullen, Matthew Hartley, Cyril Zipfel, Sophien Kamoun, József Baranyi, Richard J. Morris |
Abstract |
Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation. We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate. We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 22% |
Argentina | 1 | 11% |
Sudan | 1 | 11% |
Canada | 1 | 11% |
Tunisia | 1 | 11% |
Germany | 1 | 11% |
United States | 1 | 11% |
Unknown | 1 | 11% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 44% |
Members of the public | 4 | 44% |
Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 59 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 13 | 22% |
Researcher | 12 | 20% |
Student > Ph. D. Student | 9 | 15% |
Student > Master | 5 | 8% |
Professor > Associate Professor | 3 | 5% |
Other | 8 | 14% |
Unknown | 9 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 20 | 34% |
Biochemistry, Genetics and Molecular Biology | 5 | 8% |
Engineering | 4 | 7% |
Medicine and Dentistry | 3 | 5% |
Immunology and Microbiology | 3 | 5% |
Other | 11 | 19% |
Unknown | 13 | 22% |