OscoNet: inferring oscillatory gene networks

Luisa Cutillo, Alexis Boukouvalas, Elli Marinopoulou, Nancy Papalopulu, Magnus Rattray

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)351
JournalBMC Bioinformatics
Issue numberSuppl 10
Publication statusPublished - 21 Aug 2020


  • Cell Cycle
  • Circadian Clocks/genetics
  • Gene Expression Regulation
  • Gene Regulatory Networks
  • Human Embryonic Stem Cells/metabolism
  • Humans
  • Oligonucleotide Array Sequence Analysis
  • RNA, Messenger/genetics
  • Software
  • Statistics, Nonparametric
  • Time Factors


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