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.

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.
Book of short papers SIS 2020
762
767
Nguyen Thi Kim Hue, Monica Chiogna, and Davide Risso
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/779977
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact