A1163
Title: Structure learning of graphical models for count data, with applications to single-cell RNA sequencing
Authors: Thi Kim Hue Nguyen - University of Padova (Italy) [presenting]
Davide Risso - University of Padua (Italy)
Monica Chiogna - University of Bologna (Italy)
Koen Van Den Berge - Ghent University (Belgium)
Abstract: The problem of estimating the structure of a graph from observed data is of growing interest in the context of high-throughput genomic data and single-cell RNA sequencing in particular. These, however, are challenging applications since the data consist of high-dimensional counts with high variance and over-abundance of zeros. Here, general frameworks for learning the structure of a graph from single-cell RNA-seq data are presented. It is demonstrated with simulations that the approaches can retrieve the structure of a graph in a variety of settings, and it is shown the utility of the approach on real data.
