arXiv subjects

Charlotte Soneson

Publications and source records attributed to Charlotte Soneson.

6 recordsLinked to original sources

arXiv2026

Omnibenchmark: transparent, reproducible, extensible and standardized orchestration of solo and collaborative benchmarks

Benchmarking involves designing, running and disseminating rigorous performance assessments of methods, most often for data analysis and software tools, but the process can also be applied to experimental systems. Ideally, a benchmarking system is used to facilitate the benchmarking process by providing a structured entrypoint to design, coordinate, execute, and store standardized benchmarks. We describe a novel benchmarking system, Omnibenchmark, that facilitates benchmark formalization and execution in both solo and community efforts. Omnibenchmark provides a flexible benchmark plan syntax (i.e., a configuration YAML file), dynamic workflow generation based on Snakemake, S3-compatible storage handling, and reproducible software environments using environment modules, Apptainer or Conda. Such a setup provides an unprecedented flexibility such that existing benchmark designs can be forked and extended, run separately or collaboratively, giving versioned and standardized result outputs and therefore much-needed transparency to the analysis and interpretation of benchmark results. Tutorials and installation instructions are available from https://omnibenchmark.org.

q-bio.OT↗

arXiv2025

Building a continuous benchmarking ecosystem in bioinformatics

Benchmarking, which involves collecting reference datasets and demonstrating method performances, is a requirement for the development of new computational tools, but also becomes a domain of its own to achieve neutral comparisons of methods. Although a lot has been written about how to design and conduct benchmark studies, this Perspective sheds light on a wish list for a computational platform to orchestrate benchmark studies. We discuss various ideas for organizing reproducible software environments, formally defining benchmarks, orchestrating standardized workflows, and how they interface with computing infrastructure.

q-bio.OT↗

arXiv2025

Learning and teaching biological data science in the Bioconductor community

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.

cs.CY↗

arXiv2019

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.

q-bio.QM↗

arXiv2011

A method for visual identification of small sample subgroups and potential biomarkers

In order to find previously unknown subgroups in biomedical data and generate testable hypotheses, visually guided exploratory analysis can be of tremendous importance. In this paper we propose a new dissimilarity measure that can be used within the Multidimensional Scaling framework to obtain a joint low-dimensional representation of both the samples and variables of a multivariate data set, thereby providing an alternative to conventional biplots. In comparison with biplots, the representations obtained by our approach are particularly useful for exploratory analysis of data sets where there are small groups of variables sharing unusually high or low values for a small group of samples.

stat.AP↗

arXiv2011

A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities

Analysis of multivariate data sets from e.g. microarray studies frequently results in lists of genes which are associated with some response of interest. The biological interpretation is often complicated by the statistical instability of the obtained gene lists with respect to sampling variations, which may partly be due to the functional redundancy among genes, implying that multiple genes can play exchangeable roles in the cell. In this paper we use the concept of exchangeability of random variables to model this functional redundancy and thereby account for the instability attributable to sampling variations. We present a flexible framework to incorporate the exchangeability into the representation of lists. The proposed framework supports straightforward robust comparison between any two lists. It can also be used to generate new, more stable gene rankings incorporating more information from the experimental data. Using a microarray data set from lung cancer patients we show that the proposed method provides more robust gene rankings than existing methods with respect to sampling variations, without compromising the biological significance.

stat.AP↗