Research
The Phipson laboratory develops novel bioinformatics methods and undertakes collaborative projects with scientists both within the Institute and internationally. Our work combines statistical innovation with biological insight to reveal patterns in complex genomic data, with a strong emphasis on understanding normal development and disease. Much of our research is motivated by advances in single-cell and spatial technologies, where sophisticated modeling is essential for extracting meaningful biological conclusions.
Our research spans multiple areas of computational biology and bioinformatics:
Single-Cell Genomics: We focus on developing statistical methods tailored for single-cell sequencing data. Our work emphasizes rigorous approaches for analyzing cell type composition across conditions, assessing variability between biological replicates, and ensuring that experimental designs are statistically sound.
Spatial Transcriptomics: We create methods to integrate gene expression data with spatial context, enabling the study of cellular organization within tissues. This allows us to uncover spatially resolved patterns that provide new insights into tissue development, disease progression, and cellular interactions.
Cytometry Analysis: We design approaches for large-scale cytometry datasets, enabling efficient summarization of high-dimensional measurements across millions of cells. These methods allow researchers to capture cellular heterogeneity while reducing computational burden.
Collaborative Research: We work closely with experimental biologists to apply our methods to real-world problems in developmental biology, cancer, and immunology. These collaborations ensure our methodological innovations directly contribute to biological discovery and clinical impact.
All our novel bioinformatics methods are implemented as publicly available open-source software through the Bioconductor project, ensuring reproducibility and accessibility for the research community.