Employing scientists and developers of both computational and biological backgrounds, the Gottardo lab conceives data science solutions to enhance our understanding of complex diseases and their cures. We collaborate with excellent local, national and international research teams to understand vaccines and their efficacy (notably for HIV, tuberculosis and malaria), and deciphering the mechanisms of action of experimental cancer treatments such as new CAR T cells or checkpoint-blockade immunotherapies.
The Gottardo lab has pioneered the computational analysis of flow cytometry data in the R programing language, notably by developing and maintaining the core Bioconductor infrastructure cytoverse. These computational tools allow an automated, reproducible and unbiased statistical analysis, which is critical for large scale prospective projects such as clinical trials. Leveraging this expertise in high-throughput, high-dimensional single cell analyses, the lab expanded its areas of interest to new and transformative technologies such as single-cell RNA sequencing, DNA-barcoded antibodies (CITE-seq, AbSeq), spatial transcriptomics, and single-cell epigenomics (scATACseq). The lab also develops and maintains web-based user interfaces for employing these analysis techniques on publicly available biological datasets, with a focus on open data and reproducible analysis.