About us

We are located at the Schulich School of Medicine & Dentistry, Western University in London, Ontario.

The Schulich School of Medicine & Dentistry fosters an interdisciplinary approach to research and teaching that is enabled by state-of-the-art facilities and a common mission to innovate, integrate and translate breakthrough biomedical knowledge.

Members will experience a rich multidisciplinary environment that spans from basic science towards human health, from biology to the quantitative sciences.

We maintain both a wet (sample processing, development of single-cell profiling platforms, experimental validations) and dry lab (high-throughput data analyses).

Trainees are given hands-on experience with very modern areas of quantitative life science research including single-cell genomics, computational biology, and deep learning.

A research overview can be found here and our papers are listed here. If you are interested in joining our research, please check our current training and career opportunities.

The lab also plays both leadership and collaborative scientific roles in several large bioinformatics projects. Current close collaborators in these projects include:

  • Dr Eileeen Rakovtich, Sunnybrook Research Institute in the investigation of Ductal Carinoma In Situ (DCIS)
  • Dr Sarah Kimmins, McGill University in the investigation of male infertility and epigenetic inheritance

Dr Michael Hallett and his group have been an important partner of the lab for the past 10 years and we have consequently evolved into a joint lab. Besides the fact that Mike and Vanessa are also partners in life, this makes sense for the science we are pursuing. Although each lab has its own focus with independent projects, we combine my deep grounding in molecular biology and applied data science with Dr Hallett’s expertise in machine learning and computational biology. We are sharing wet-lab space and dry lab resources, conduct some joint lab meetings and social events.

Dumeaux & Hallett lab, 2018, Montreal

Lab values

Our top 3 values are:

  1. Enthusiasm. Science is fascinating and fun. Our lab will thrive on the excitement of the big picture of our research but also the day-to-day experiments, analyses and coding we do.

  2. Persistence. Research can get hard, and experiments will inevitably fail sometimes. There is always something to learn, and we will help each other out to adapt and find new ways to answer our research questions.

  3. Transparency. Communication is of the utmost importance. We will have honest discussions about experiments, data, results, and all aspects of day-to-day lab life. We will be open to giving and receiving feedback and work together to build a supportive environment.

Mentoring

I seek to build a stimulating and diverse lab environment and ultimately help trainees improve as scientists to translate biological questions into quantitative exploration and analysis and answer the questions they find most exciting.

I have been very active in the training of data science to several undergraduate, graduate, and postgraduate students. The students often have a strong background in molecular biology but required training in statistics, programming, and data science (10+ trainees through academia and consultancy). A few have had a strong background in programming or machine learning but conversely lacked knowledge in biology and health data sciences (3 MSc/graduate students).

I am happy to teach trainees new techniques in data science, programming (mostly R and python), molecular profiling and single-cell technologies. The lab uses chatting platforms like Slack with dedicated rooms for each project which facilitate greatly daily communications between team members (and collaborators). We will have regular one-on-one meetings in addition to group meetings to make sure you’re on track for your research project as well as career goals, whatever they may be. Another group session is spent working together through textbooks, online talks or recent papers. In particular, we have previously studied Murphy’s “Machine Learning”, James/Witten/Hastie/Tibshirani’s “An introductions to statistical learning”, Holmes & Huber’s “Modern Statistics for Modern Biology” books, Prof. Nando De Freitas’ youtube lectures and Howards’ online course “Deep learning for coders”.

References:

If you want to contact some of the recent PhD students I have trained in data science and bioinformatics:

Affiliations

Department of Anatomy and Cell Biology
Schulich School of Medicine and Dentistry, Western University