How to measure the quality of 2D gel image analysis data
Improve your discovery potential by understanding how correct your data really is
When you go through your 2D gel image analysis results, do you sometimes notice that a lot of spots presented in the results have not been detected or matched correctly?
Ever wondered how that affects your overall discovery potential?
You probably won't be surprised to learn that the more mistakes occur in your image analysis data, the less likely you are to discover all differentially expressed proteins. True expression changes can be masked by mistakes and valuable resources wasted on validating false hits.
How can we avoid this?
Being aware of the quality of your data is an important step in optimizing the overal outcome of your 2D gel experiments.
After all, only once you are able to measure something are you also able to quantifyably improve it.
Correctness must come before reproducibility
Especially in light of the on-going efforts to ensure reproducibility of 2D gel proteomics data, it is vital to first look at how correct and complete the obtained data is.
An emphasis on data quality before reproducibility ensures that we don't just end up reliably reproducing erroneous data.
Luckily, the importance of 2D gel data correctness and completeness is being brought more to the forefront again and is being discussed at international meetings and conferences. Together with a push for more reproducible data, the future for 2D gel based proteomics indeed looks promising.
Combined Correctness as a metric for 2D gel data quality
At Ludesi we have been researching into data quality metrics for 2D gel image analysis for many years now, as part of providing an industry leading 2D gel image analysis service.
In cooperation with some of the leading researchers in the field, this work has culminated in the establishement of the Combined Correctness metric.
An inter-lab study including 30 different real-life 2D gel projects has shown that Combined Correctness is strongly correlated to the false positive rate in 2D gel image analysis data, and hence the overal discovery potential.
One of the most important things about it is that it can be applied across all current software platforms, making it a universally applicable metric for 2D gel image analysis data quality.
Find out how Combined Correctness works
Download the FREE tool to measure your own Combined Correctness
The Combined Correctness metric has been developed in collaboration with the following research groups:
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JHU Bayview Proteomics Center, Johns Hopkins University |
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Vertex Pharmaceuticals Inc. |
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DSM Nutritional Products Ltd |
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Scheie Eye Institute, FM Kirby Center for Molecular Ophthalmology, University of Pennsylvania |
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Institute of Animal Physiology and Genetics, Academy of Sciences of the Czech Republic |
Example of high quality 2D gel image analysis:
Example of low quality 2D gel image analysis:
