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 overabundance of zeros. Here we present a general framework for learning the structure of a graph from single-cell RNA-seq data, based on the zero-inflated negative binomial distribution. We demonstrate with simulations that our approach is able to retrieve the structure of a graph in a variety of settings, and we show the utility of the approach on real data.
Funding Statement
DR was supported by “Programma per Giovani Ricercatori Rita Levi Montalcini,” granted by the Italian Ministry of Education and University Research, and by the National Cancer Institute of the National Institutes of Health (U24CA180996).
TKHN was supported by the project of excellence “Statistical methods and models for complex data” awarded to the Department of Statistical Sciences, University of Padova by the Italian Ministry for Education and University Research.
KVDB was a postdoctoral fellow of the Belgian American Educational Foundation (BAEF) and was supported by the Research Foundation Flanders (FWO), grant 1246220N.
This work was supported in part by CZF2019-002443 (DR and TKHN) from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation.
Acknowledgments
The authors would like to thank Diya Das, Rebecca Chance and John Ngai for providing access to the data and for help with the biological interpretation of the results.
Additional affiliation for Davide Risso: Padua Center for Network Medicine, University of Padova.
Koen Van den Berge’s current address: Janssen R&D, Beerse, Belgium.
Citation
Thi Kim Hue Nguyen. Koen van den Berge. Monica Chiogna. Davide Risso. "Structure learning for zero-inflated counts with an application to single-cell RNA sequencing data." Ann. Appl. Stat. 17 (3) 2555 - 2573, September 2023. https://doi.org/10.1214/23-AOAS1732
Information
