Biological processes involve complex interactions between genes. Such interactions may be represented employing a conditional independence graph. Here, we propose the use of a PC-stable algorithm to estimate the neighbourhood of each node in the graph, thus learning its structure. We apply our algorithm to two single- cell RNA sequencing datasets, demonstrating that we can identify important genes as hub nodes in the networks.
Nguyen Thi Kim Hue, M.C. (2020). Graphical models for count data: an application to single-cell RNA sequencing. Pearson.
Graphical models for count data: an application to single-cell RNA sequencing
Monica Chiogna;
2020
Abstract
Biological processes involve complex interactions between genes. Such interactions may be represented employing a conditional independence graph. Here, we propose the use of a PC-stable algorithm to estimate the neighbourhood of each node in the graph, thus learning its structure. We apply our algorithm to two single- cell RNA sequencing datasets, demonstrating that we can identify important genes as hub nodes in the networks.File in questo prodotto:
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