lipidomeR is a new tool for lipidome-wide visualization of statistical associations.
The tool creates integrative heatmaps of patterns in the entire measured lipidome. The package can be installed in R with the command 'install.packages("lipidomeR")'. See lipidomeR.org for reproducible examples, the manuscript preprint for guidance on the systematic interpretion of the results, and CRAN for documentation of the tool.
The fecal metabolome.
In this publication, we assess, which lipids and metabolites have relevant between-individual variation in feces. Furthermore, we report a data-driven analysis of associations between these small molecules.
I am a data scientist with a background in machine learning and computational sciences, and a specific focus in digital human health.
While based in Stockholm, I work as a post-doctoral researcher at Steno Diabetes Center Copenhagen (SDCC), which is the largest center of diabetes treatment in Scandinavia. I am affiliated with the Systems Medicine research group, where I work as a bioinformatics and biostatistics specialist to process, analyze, visualize and understand complex omics data from the small molecule profiling platforms managed by the team.
During my career, I have acquired an in-depth knowledge of computational methods for data-driven analysis as well as a broad understanding of application areas in health and clinical research. Lately, the focus of my work has been in the development and application of computational methods for studying biomarkers of diabetes, its development, treatment and complications.
In my current position, I have set up extensive computational pipelines for a streamlined processing, analysis and visualization of data from the team’s omics profiling platforms. While I continue to develop these solutions, I collaborate with medical professionals, chemists and biostatisticians to help them understand complex research data and to uncover novel insights from measured molecular profiles in large clinical studies.
Since 2018, I have been working with Dr. Cristina Legido-Quigley, who leads the interdisciplinary Systems Medicine team. I work in the role of a senior bioinformatician and I complement the chemistry and laboratory expertise in the team by instructing its members with modern data analysis approaches.
Between 2016 and 2017, I worked with Prof. Lars Ove Dragsted and M.D. Prof. Peter Rossing, with whom I continue to collaborate in some of my largest collaborative projects -- the PROFIL study and the PROTON project. I started my post-doctoral career in 2015 by working with Dr. Matej Orešič on lipidomic profiling projects in mental disease and in the development of type 2 diabetes.
I completed a PhD (Doctor of Science in Technology) from Aalto University and Helsinki Institute for Information Technology in 2014. For my doctoral research, I was privileged to work with Academy Prof. Samuel Kaski on Bayesian models for data integration across measurement domains, organisms and time points, in what is nowadays known as the Probabilistic Machine Learning research group.
As part of the research work for my PhD, I also had a great opportunity to make a visit to University of Glasgow to work with Dr. Simon Rogers on non-parametric Bayesian modeling of mass-spectral measurements.
Prior to my doctoral studies, I completed a MSc. (Tech.) degree in bioinformation technology from Helsinki University of Technology in 2009, where my Thesis on dimensionality-reducing modeling of covariates’ effects on molecular levels was instructed by Dr. Ilkka Huopaniemi. My research career started in 2006, when I got on opportunity to work for two summers as an undergraduate intern in a computational physics group led by Prof. Jukka Tulkki. During the time, I wrote a BSc. (Tech.) Thesis on quantum photonics at what was then known as the Laboratory of Computational Engineering at Helsinki University of Technology.