Leiv Rønneberg

• Heimersheim, Stefan; Rønneberg, Leiv; Linton, Henry; Pagani, Filippo & Fialkov, Anastasia (2024). FlexKnot and Gaussian Process for 21 cm global signal analysis and foreground separation. Monthly notices of the Royal Astronomical Society. ISSN 0035-8711. 527(4), p. 11404–11421. doi: 10.1093/mnras/stad3936. Full text in Research Archive Show summary

  The cosmological 21 cm signal is one of the most promising avenues to study the Epoch of Reionization. One class of experiments aiming to detect this signal is global signal experiments measuring the sky-averaged 21 cm brightness temperature as a function of frequency. A crucial step in the interpretation and analysis of such measurements is separating foreground contributions from the remainder of the signal, requiring accurate models for both components. Current models for the signal (non-foreground) component, which may contain cosmological and systematic contributions, are incomplete and unable to capture the full signal. We propose two new methods for extracting this component from the data: First, we employ a foreground-orthogonal Gaussian Process to extract the part of the signal that cannot be explained by the foregrounds. Secondly, we use a FlexKnot parametrization to model the full signal component in a free-form manner, not assuming any particular shape or functional form. This method uses Bayesian model selection to find the simplest signal that can explain the data. We test our methods on both, synthetic data and publicly available EDGES low-band data. We find that the Gaussian Process can clearly capture the foreground-orthogonal signal component of both data sets. The FlexKnot method correctly recovers the full shape of the input signal used in the synthetic data and yields a multimodal distribution of different signal shapes that can explain the EDGES observations.

• Rønneberg, Leiv; Kirk, Paul D. W. & Zucknick, Manuela (2023). Dose–response prediction for in-vitro drug combination datasets: a probabilistic approach. BMC Bioinformatics. 24. doi: 10.1186/s12859-023-05256-6. Full text in Research Archive Show summary

  In this paper we propose PIICM, a probabilistic framework for dose–response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose–response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose–response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose–response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.

• Hanes, Robert; Ayuda-Duran, Maria del Pilar; Rønneberg, Leiv; Nakken, Sigve; Hovig, Eivind & Zucknick, Manuela [Show all 7 contributors for this article] (2022). screenwerk: a modular tool for the design and analysis of drug combination screens. Bioinformatics. ISSN 1367-4803. 39(1). doi: 10.1093/bioinformatics/btac840. Full text in Research Archive Show summary

  Abstract Motivation There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data and generate data files that can be analyzed by synergy-finding bioinformatics applications. Results screenwerk is an open-source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control and analyze, report and visualize the results of drug sensitivity screens. Availability and implementation The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy. Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no Supplementary information Supplementary data are available at Bioinformatics online.

• Rønneberg, Leiv; Cremaschi, Andrea; Hanes, Robert; Enserink, Jorrit & Zucknick, Manuela (2021). bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments. Briefings in Bioinformatics. ISSN 1467-5463. 22(6), p. 1–12. doi: 10.1093/bib/bbab251. Full text in Research Archive Show summary

  Abstract The effect of cancer therapies is often tested pre-clinically via in vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). Although the model formulation makes use of the Bliss independence assumption, we note that the posterior estimates of the dose–response surface can also be used to extract synergy scores based on other reference models, which we illustrate for the Highest Single Agent model. The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the open-source Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.

• Mai, The Tien; Rønneberg, Leiv; Zhao, Zhi; Zucknick, Manuela & Corander, Jukka (2020). Learning Cancer Drug Sensitivities in Large-Scale Screens from Multi-omics Data with Local Low-Rank Structure. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12313, p. 67–79. doi: 10.1007/978-3-030-63061-4_7. Full text in Research Archive
• Lyngstad, Torkild Hovde; Härkönen, Juho & Rønneberg, Leiv Tore Salte (2020). Nationalistic bias in sport performance evaluations: An example from the ski jumping world cup. European Journal for Sport and Society (EJSS). ISSN 1613-8171. 17(3), p. 250–264. doi: 10.1080/16138171.2020.1792628. Full text in Research Archive Show summary

  Illegitimate biases of non-independent judges represent a threat to the legitimacy of international sport competitions. Judges’ nationalities represent one source of bias, as they consciously or subconsciously may prefer athletes from their own nation. Such biases may affect the outcomes of competitions. The literature offers no complete consensus on the magnitude, origin and stability of such biases. In this paper, we shed light on these problems, using international ski jumping as an example. We draw on data from the FIS World Cup competitions in the 2006–2008 and 2015–2016 seasons and estimate a series of fixed-effects models to test hypotheses on nationalistic biases. Our results reaffirm suspicions of nationalistic bias in major ski jumping competitions, but also show their magnitude is too little to be of major relevance to competition outcomes. The biases vary between nations, but do not change markedly over the study period

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