Selected Publications

RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.
We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.
ideal is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.
In bioRxiv, 2020

Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking. We developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components. pcaExplorer is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/pcaExplorer/), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.
In BMC Bioinformatics, 2019

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.
In F1000Research, 2018

Recent Publications

The full publications list is available on Google Scholar and on ORCID.

Here are some detailed information on a couple of my latest contributions:

RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the …

Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in …

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. …

Projects

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Interactive and reproducible research

Developing tools enabling interactivity and reproducibility, for better analyses.

Plateletopedia

Understanding platelet transcriptomics, one dataset after the other, with different bioinformatics perspectives.

Software

R packages

I am the maintainer or co-developer of the following R packages:

A package for interacting with RNA-seq principal components, based on objects from the DESeq2 framework

A package for performing Interactive Differential Expression AnaLysis - DE made accessible and reproducible!

A package for exploring interactively any SummarizedExperiment object, with an amazing support for reproducible research by meta-generating the required code. You can find more on iSEE at the GitHub Organization page for it - https://github.com/iSEE.

A package for tracking and analyzing flowing blood cells in time lapse microscopy images

A package for accessing the datasets of the Human Cell Atlas in R/Bioconductor

A package for enjoying transcriptomic data and analysis responsibly - like sipping a cocktail, where expression matrix, DE results, enrichment results are all seamlessly combined

An interactive interface for exploring design matrices in R (co-developed with Charlotte Soneson)

An package for comparing sessionInfos in a smooth way (co-developed with Charlotte Soneson)

An package for the simulation of recurrent event data in the total time model (co-developed with Katharina Ingel and Antje Jahn)

Other resources

I contributed to the OSCA online book (“Orchestrating Single-Cell Analysis with Bioconductor”), with some content related to “Interactive Interfaces and Sharing” (based on the work with iSEE)

I am the curator of the awesome-expression-browser list, filled with software and resources for exploring and visualizing (browsing) expression data

I designed and developed together with Denise Scherzinger TREND-DB, a Shiny application for exploring interactively Transcriptome 3’end diversification (TREND) data, as a companion to the original publication of Ogorodnikov et al.

Teaching

🚧 WORK IN PROGRESS 🚧

Contact