Kevin Rue-Albrecht

Kevin Rue-Albrecht

Postdoctoral Researcher

University of Oxford


I am a computational biologist at the University of Oxford.

My research interests in computational biology include software engineering best practices, DevOps, single-cell genomics, and interactive data visualization. I particularly enjoy using and contributing R packages part of the Bioconductor project. A list of software packages that I maintain or contributed to is available on the Software page.

My academic research primarily explores the host immune response to infectious diseases, inflammation, and self-antigens.


  • Computational Biology
  • Software Development
  • Bioconductor packages
  • Single-cell genomics


  • PhD in Computational Infection Biology, 2015

    University College Dublin

  • MSc in Biological Engineering, 2011

    Polytech Nice - Université Nice Sophia Antipolis

  • BSc in Biology, Chemistry, Physics and Earth Sciences, 2008

    CPGE BCPST Véto - Lycée Jean Rostand




Software carpentry






Infectious diseases




Postdoctoral Researcher - Computational Genomics

University of Oxford

Sep 2017 – Present Oxford

Responsibilities include:

  • Study the role of thymic epithelial cells (TEC) in shaping the T vell receptor (TCR) repertoire (consortium funded by Wellcome Trust Strategic Award)
  • Study contest-specific changes in immune cell abundance and phenotype that underlie chronic inflammation (collaboration with the Powrie group)
  • Write and maintain software pipelines and programs for data analysis

Postdoctoral Researcher - Computational Biology

University of Oxford

Dec 2016 – Sep 2017 Oxford

Responsibilities include:

  • Management, quality control, and integration of pan-genomic datasets encompassingn RNA-seq, ChIP-seq, DNAse/FAIRE/ATAC-seq, Capture-C, microarrays, whole-genome sequencing and targeted re-sequencing
  • Study transcriptional and epigenetic responses to hypoxia and their relation to cancer biology
  • Experimental design, pipeline development, and publication

Research Associate - Bioinformatics / Biostatistics

Imperial College London

Sep 2015 – Nov 2016 London

Responsibilities include:

  • Analyze genetic, proteomic and metabolomics data and associated phenotype data
  • Integrate and analysis of large-scale, complex, multi-sources medical data and multi-variate data
  • Generate new knowledge on idiopathic pulmonary arterial hypertension (IPAH) disease aetiology, underlying biochemical pathways and mechanisms, to improve patient stratification, preventive strategies and treatments


Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed.
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Python for Genomic Data Science

This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.
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Command Line Tools for Genomic Data Science

Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
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Bioconductor for Genomic Data Science

Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.
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Statistics for Genomic Data Science

An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
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Network Analysis in Systems Biology

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction.
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Recent Posts


Hello world

Create R environments for teaching

In this post, I’ll describe a use of renv to manage a collection of environments for a training course, in a single Git repository. I will cover some caveats at the end of the post, so that you can decide whether this set up suits your own needs, but for now - before I give myself a chance to confuse you - I’ll just get straight into my process.


Motivation There are currently 6,096 packages on Bioconductor, broken down as follows: Repository Packages data-annotation 2,693 data-experiment 855 software 2,516 workflows 32 In an effort to motivate myself to keep an eye out for interesting packages - both new and old - I have used the BiocPkgTools package to develop a website updated daily and feature packages selected randomly from each repository.

Making a map of COVID-19 cases

Overview I have added an interactive world map to my COVID-19 website using the leaflet package. Using my existing data preprocessing setup, it was incredibly easy to set up the interactive map!

Transiton from Travis CI to GitHub Actions

Overview The recent introduction of GitHub Actions makes a lot of our Travis CI build configurations redundant. In particular, the examples actions include: R-CMD-check using rcmdcheck::rcmdcheck() to check the package.



COVID-19 Website

My website tracking the evolution of the COVID-19 pandemic.

Reading list

A list of items that I have read or intend to read.

Twitter conference coverage

My website tracking tweets associated with scientific conferences.

Recent & Upcoming Talks

iSEE - Bioconductor 2020 Workshop Presentation

Overview of the iSEE package and functionality

iSEE - Bioconductor 2020 Flash Presentation

Flash presentation of the iSEE package and functionality