Kernel-based perturbation testing for single-cell data - Dr. Franck Picard
Event details
| Date | 03.02.2026 |
| Hour | 11:00 › 12:00 |
| Speaker | Dr. Franck Picard, ENS-Lyon |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Since joining the Biopôle campus, the Biomedical Data Science Center (BDSC) is delighted to kickoff their series of scientific talks. In their first talk, Dr. Franck Picard, ENS-Lyon, will share insights into Kernel-based perturbation testing for single-cell data. The presentation is intended for an interdisciplinary audience, and they warmly encourage students and participants from diverse backgrounds and career stages to join the discussion.
- Date: Tuesday, February 3rd, 2026
- Time: 11.00 – 12.00
- Location: Biopôle CLE B301
Title
Kernel-based perturbation testing for single-cell data
Summary
Advances in single-cell sequencing have enabled high-dimensional profiling of individual cells, giving rise to single-cell data science and new statistical challenges. A key task is the comparative analysis of single-cell datasets across conditions, tissues, or perturbations, where traditional gene-wise differential expression methods often fail to capture complex, non-linear distributional differences. Perturbation experiments further amplify this challenge by introducing structured, high-dimensional responses that are poorly modeled by linear approaches. We propose a kernel-based framework for differential analysis of single-cell data that enables non-linear, distribution-level comparisons by embedding data into a reproducing kernel Hilbert space. Our method quantifies differences between cellular populations through distances between mean embeddings and supports formal hypothesis testing in complex experimental designs, including perturbation studies via linear models in RKHS. The approach is robust to high dimensionality, sparsity, and noise, and is implemented in the Python package kaov, which provides visualization and interpretation tools. By offering a flexible, distribution-free alternative to classical methods, kernel-based testing facilitates the detection of subtle but biologically meaningful changes in single-cell data, enabling deeper insights into cellular regulation, disease mechanisms, and precision medicine.
Bio
Franck Picard is a CNRS researcher in statistical learning at ENS Lyon, France, and principal investigator of the SCAI group (AI for Single-Cell Data Analysis). His research lies at the interface of statistical learning and high-throughput biology, with expertise in high-dimensional statistics, functional data analysis, and point processes.
Links
Practical information
- General public
- Free
Organizer
Contact
- Profs. Raphael Gottardo and Marianna Rapsomaniki