OscoNet: inferring oscillatory gene networks
- PDF / 9,444,925 Bytes
- 19 Pages / 595 x 794 pts Page_size
- 103 Downloads / 240 Views
RESEARCH
Open Access
OscoNet: inferring oscillatory gene networks Luisa Cutillo2*† , Alexis Boukouvalas1† , Elli Marinopoulou1 , Nancy Papalopulu1 and Magnus Rattray1 From 13th Bioinformatics and Computational Biology Conference - BBCC 2018 Naples, Italy. 19-21 November 2018 *Correspondence: [email protected] Luisa Cutillo and Alexis Boukouvalas contributed equally to this work. † Luisa Cutillo and Alexis Boukouvalas are co-first authors. 2 University of Leeds, Leeds, UK Full list of author information is available at the end of the article
Abstract Background: Oscillatory genes, with periodic expression at the mRNA and/or protein level, have been shown to play a pivotal role in many biological contexts. However, with the exception of the circadian clock and cell cycle, only a few such genes are known. Detecting oscillatory genes from snapshot single-cell experiments is a challenging task due to the lack of time information. Oscope is a recently proposed method to identify co-oscillatory gene pairs using single-cell RNA-seq data. Although promising, the current implementation of Oscope does not provide a principled statistical criterion for selecting oscillatory genes. Results: We improve the optimisation scheme underlying Oscope and provide a wellcalibrated non-parametric hypothesis test to select oscillatory genes at a given FDR threshold. We evaluate performance on synthetic data and three real datasets and show that our approach is more sensitive than the original Oscope formulation, discovering larger sets of known oscillators while avoiding the need for less interpretable thresholds. We also describe how our proposed pseudo-time estimation method is more accurate in recovering the true cell order for each gene cluster while requiring substantially less computation time than the extended nearest insertion approach. Conclusions: OscoNet is a robust and versatile approach to detect oscillatory gene networks from snapshot single-cell data addressing many of the limitations of the original Oscope method. Keywords: Single-cell, Network analysis, Non-parametric hypothesis test
Background Oscillating genes are expressed in a periodic manner leading to alternating appearance and disappearance of the corresponding mRNA and protein. Oscillatory genes have been shown to play a pivotal role in many developmental processes, by enabling individual systems to implement diverse functions [1]. To identify oscillatory genes a combination of time-lapse microscopy techniques and fluorescent reporter genes is required, which
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indi
Data Loading...
