"Causal inference for live cell imaging and single-cell multi-omics data (2022-07-ISAMBERT_HERSEN)" project details

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General information

Application closed

2022-07-ISAMBERT_HERSEN

Machine and Deep learning; Causal inference; Information theory; Live cell imaging; Single cell multi-omics

Causal inference for live cell imaging and single-cell multi-omics data

Director(s) and team

Hervé Isambert & Pascal Hersen

Reconstruction, Analysis and Evolution of Biological Networks

Abstract

Live cell imaging microscopy and next generation sequencing technologies, now routinely used in cell biology labs, produce massive amounts of time-lapse images and gene expression data at single cell resolution. However, this wealth of state-of-the-art biological data remain largely under-explored due to the lack of unsupervised methods and tools to analyze them without preconceived hypothesis. This highlights the need to develop new Machine Learning and Artificial Intelligence strategies to better exploit the richness and complexity of the information contained in time-resolved cell biology data.   The Isambert lab recently developed novel causal inference methods and tools (https://miic.curie.fr) to learn cause-effect relationships in a variety of biological or clinical datasets, from single-cell transcriptomic and genomic alteration data (Verny et al 2017, Sella et al 2018) to medical records of patients (Cabeli et al 2020). These machine learning methods combine multivariate information analysis with interpretable graphical models (Li et al 2019) and outperform other methods on a broad range of benchmarks, achieving better results with only ten to hundred times fewer samples.   The objective of the present PhD project is to extend these causal inference methods to analyze time-resolved cell biology data, for which the information about cellular dynamics can facilitate the discovery of novel cause-effect functional processes. These novel causal inference methods for time series data will then be applied to analyze two types of high-through put time-resolved cell biology data: 1- time-lapse images of cellular systems (Marinkovic et al 2019) from the Hersen lab (Institut Curie) to analyze cell cycle progression and apoptosis in tumor-on-chip devices, in collaboration with the Martinelli lab (University of Rome Tor Vergata, Italy), and 2- single-cell multi-omics data on a cellular therapy against multiple sclerosis in collaboration with the Fillatreau lab (Institut Necker).

Requirements to apply for the PhD thesis project

Applicants should have a strong background in machine learning or computer science and a keen interest to analyze complex heterogeneous data of biological and medical interests. Applicants should be proficient in programming and willing to interact with scientists from different disciplines, from data scientists to medical doctors. Applicants are expected to show a clear capacity for independent and creative thinking. Experience on causal inference analysis is a plus but not required as long as the applicant has a strong motivation to learn.