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Probabilistic index models (PIM) for differential gene expression analysis

The Probabilistic Index Model (PIM) offers a robust, flexible approach for differential gene expression analysis in single-cell RNA-sequencing data. This regression framework generalizes rank-based tests, accommodates various experimental designs, and requires no distributional assumption. PIM enhances DGE by incorporating informative effect size parameters, simplifying interpretation, and considering different sources of variation. It delivers competitive performance compared to parametric tools in simulation studies.

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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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