Comments (8)
I don't think this is what we want. GPs are great for data in which there is a natural spatial relationship between the collected data, but that relationship must be learned. We are dealing with a very different case---we know what the relationship is, through the dissociation constant equations and mass conservation laws. Utilizing a GP of the sort in those examples would not only "forget" that information, but it doesn't allow us to propagate any uncertainty in which points are outliers into the posterior.
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Instead, I think we should use an approach like this, where there is a prior on the fraction of outliers and the outlier distribution has a mean and variance that is inferred (and marginalized out) during MCMC sampling:
http://www.astroml.org/book_figures/chapter8/fig_outlier_rejection.html
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But first, before we even talk about models, we absolutely need to collect some examples of the outliers and look at them to see what it tells us about the nature of the data.
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Just making a note here that this is something we should keep at the front of our minds.
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Agreed! Would be great to compile a list of data with outliers to find a strategy that works!
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Here is an example of two almost identical datasets with and without an outlier:
With outlier:
https://github.com/choderalab/fluorescence-assay-manuscript/blob/fig_sketches/analysis/bayes/DMSO-backfill/delG_Bosutinib-AB-2016-07-31%2020:10.png
https://github.com/choderalab/fluorescence-assay-manuscript/blob/fig_sketches/analysis/bayes/DMSO-backfill/Bosutinib-AB-2016-07-31%2020:10.json
Without outlier:
https://github.com/choderalab/fluorescence-assay-manuscript/blob/fig_sketches/analysis/bayes/DMSO-backfill/delG_Bosutinib-IJ-2016-07-31%2020:13.png
https://github.com/choderalab/fluorescence-assay-manuscript/blob/fig_sketches/analysis/bayes/DMSO-backfill/Bosutinib-IJ-2016-07-31%2020:13.json
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Awesome! This is exactly what we need to make this work! Thanks!
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@jchodera has an idea about Bayesian outlier detection that he is interested in implementing.
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Related Issues (20)
- Could we just set a slightly more restrictive prior on F_PL? HOT 1
- Missing dependencies HOT 4
- Cut a new assaytools release HOT 2
- Fix win install HOT 1
- Make better error message when using wrong path in quickmodel inputs file.
- Travis build failing - libgfortran related? HOT 5
- Deprecate assaytools-dev package HOT 3
- Travis is still broken HOT 1
- Clean up old branches
- Make Docs clearer for General Binding Model
- Integrate scripts/ into installed codebase and use entry-points instead
- Analysis for the single-well titration experiment HOT 2
- getting error in assaytools parser.py when running quickmodel HOT 9
- xml2png python 3.6 travis tests are failing HOT 1
- security alert for pyyaml dependency HOT 2
- Adding Quickmodel script the option to use informed F_PL priors HOT 1
- New release? HOT 1
- Add new `GeneralBindingModel` API that accepts/returns concentrations instead of log concentrations
- Getting print out of 'Skipping analysis of rows: []' for each protein from parser.py when running quickmodel HOT 3
- how to omit a well from analysis?
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