OscoNet: inferring oscillatory gene networks.

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 well-calibrated 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.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Cutillo L,Boukouvalas A,Marinopoulou E,Papalopulu N,Rattray M

doi

10.1186/s12859-020-03561-y

subject

Has Abstract

pub_date

2020-08-21 00:00:00

pages

351

issue

Suppl 10

issn

1471-2105

pii

10.1186/s12859-020-03561-y

journal_volume

21

pub_type

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