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, 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.
Thi Kim Hue Nguyen, Koen Van den Berge, Monica Chiogna, Davide Risso (2023). Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data. THE ANNALS OF APPLIED STATISTICS, 17(3 (September)), 2555-2573 [10.1214/23-AOAS1732].
Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data
Monica Chiogna;
2023
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, 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.File | Dimensione | Formato | |
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