BDSC Seminar : Kernel-based perturbation testing for single-cell data

Prs Raphael Gottardo and Marianna Rapsomaniki cordially invite you to attend a seminar organized by the Biomedical Data Science Center. Their guest,

Dr Franck Picard

will give a talk entitled 

Kernel-based perturbation testing for single-cell data 

Tuesday, February 3rd, 2026 - 11h00-12h00
Biopôle - CLE-B301 

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.