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The effects of applying cell-suppression and perturbation to aggregated genetic data

The effects of applying cell-suppression and perturbation to aggregated genetic data.

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The effects of applying cell-suppression and perturbation to aggregated genetic data

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  1. The effects of applying cell-suppression andperturbation to aggregated genetic data Athos Antoniades, John Keane, Aristos Aristodimou, Christa Philipou, Andreas Constantinou, Christos Georgousopoulos, Federica Tozzi, Kyriacos Kyriacou, Andreas Hadjisavas, Maria Loizidou, Christiana Demitriou and Constantinos Pattichis

  2. Introduction • Why Share Data? • What are the current legal and ethical limitations? • How have scientists shared medical data so far? • Key Problems • Perturbation • Cell Suppression

  3. The Problem • Why share data: • Replication Testing • Statistical Power • Multiple Testing Problem • Legal and Ethical Issues • AnonymizationvsPseudoanonimization • Limitations derived from consent form signed by subjects • Other, regional, study, or subject specific issues.

  4. How have scientists shared medical data Contingency Table and Data Cube example

  5. 16 year old widow Problem A paper that analyzes data from a specific study reports:

  6. 16 year old widow Problem A paper that analyzes data from a specific study reports:

  7. 16 year old widow Problem A paper that analyzes data from a specific study reports:

  8. Categorization Differences Paper 2 that analyzes data from the same study reports: Paper 1 that analyzes data from a specific study reports:

  9. Perturbation and Cell Suppression Perturbation (+-1) and Cell Suppression (<5) Original Data

  10. Evaluation • Most common parameters tested • Perturbation:[0], [-1,1], [-3,3], [-5,5], [-10,10] • Cell Supression: <0, <=1, <=3,<=5,<=10 • Standard main effect test usingChi Square • Pearson’s Correlation Coefficient used to evaluate deviation of each parameter combination to original results. • A-priory defined threshold for Pearson’s correlation coefficient <=0.95.

  11. Evaluating Parameters with a matrix of graphs

  12. Conclusion and Future Work • We were able to identify for this dataset, the maximum noise that can be added to the data without significantly affecting the outcomes. • Results only relevant to MASTOS, all other datasets need to repeat the analytical approach described. • Further investigation is necessary to identify the minimum parameter settings to satisfy legal and ethical requirements.

  13. Who to Contact • Athos Antoniades • University of Cyprus • email: athos@cs.ucy.ac.cy

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