Biography

Samuel Granjeaud is a research engineer in computational biology at Inserm. He is working at CRCM’s Integrative BioInformatics facility. CIBI is carrying out various omics analyses for experiments of CRCM’s teams, such as NGS technologies (mutations, ChIPseq, RNAseq), proteomics and metabolomics, cytometry, single cell RNAseq… While these technologies generate massive amounts of data on cell behaviour and functioning, interpreting these data and making them readily comprehensible is a current challenge. CIBI is applying state-of-the-art pipelines and data mining techniques to address specific questions in biology and clinics concerning cancerology and immunotherapy. Samuel is mostly working in the proteomics and cytometry fields.

Samuel has a computer science background and a thesis in material science. He switched to biology at the early ages of the transcriptomic era. He is experienced in data analysis, numerical programming, database development, omics database querying, Web and Linux administration. He has developed an expertise in statistics and classical machine learning techniques such biomarker selection, as well as their extensions to high-dimensional, small sample settings, and imbalanced data. He published many articles in omics fields as well as three Laboratory Information Management Systems.

Since a few years, Samuel is working on the analysis of the high content flow and mass cytometry datasets. He is testing, automating and developing analysis tools for cytometry in order to ease the work of experimenters. He contributes to flowCore, FlowSOM and cytofkit packages. He participated to workshops and congress on cytometry data analysis, and trained scientists at the wet bench to analyse datasets on their own. With Hervé Luche, Antonio Cosma and the support of the Association Française de Cytométrie (AFC), he initiated the Computational Cytometry Club (3C) to improve harmonization of multidimensional analysis across laboratories in France. His current goals are to validate the analysis of complex datasets, to disseminate the good practices and to increase the adoption of new analytical tools via the web site impact (Intergative MultiPArametric Cytometry Tools).