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Probabilistic index models (PIM) for differential gene expression analysis

Probabilistic index models (PIM) for differential gene expression analysis. Application to single cell RNA-sequencing data. Alemu Takele Assefa 1 , Jo Vandesompele 2,3,4 , Olivier Thas 1,3,5,6

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Probabilistic index models (PIM) for differential gene expression analysis

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  1. Probabilistic index models (PIM) for differential gene expression analysis Application to single cell RNA-sequencing data Alemu Takele Assefa1, Jo Vandesompele2,3,4, Olivier Thas 1,3,5,6 1Department of Data Analysis and Mathematical Modeling, Ghent University, Belgium; 2Department of Biomolecular Medicine, Ghent University, Belgium; 3Cancer Research Institute Ghent, Ghent University, Belgium; 4Center for Medical Genetics, Ghent University, Belgium; 5National Institute for Applied Statistics Research, University of Wollongong, Australia; 6I-BioStat, Hasselt University, Belgium December 5 2018 CRIG single cell mini-symposium

  2. PIM offers a flexible and robust approach for testing differential gene expression (DGE). PIM is a regression framework, • generalizes rank based tests, • can be used for simple and complex experimental designs, • e.g. multi-group comparison, • PIM requires no distributional assumption, • adaptable to various gene expression units

  3. PIM augments DGE with informative effect size parameters • Effect size in terms of probabilistic index • Straightforward for interpretation • Ranking genes • accounts for different sources of variation • e.g. sequencing depth, batch effect,

  4. PIM has competitive performance to the parametric tools Simulation studies

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