Speaker: Ivan G. Costa, Institute for Computational Genomics, RWTH Aachen
Abstract: Single cell and spatial sequencing allow measuring full transcriptomes or epigenomes of all cells in a tissue. When applied to disease cohorts, several single cell or spatial experiments across distinct patients are available. One open computational problem is how to compare experiments at a sample level, as a sample is represented by a distribution of cells. We will describe in this talk the use of the optimal transport framework as an approach to obtain distances between distributions of cells. This is used for sample level analysis of disease relevant single cell and spatial transcriptomics data to find clusters or trajectories of patients. Another relevant challenge comes from the multi-modal properties of the data, as single cells can be measured in regard to distinct molecular features (transcriptomes and epigenome) or histology data. This requires algorithms for estimation of joint embeddings, which capture information from all available modalities. Finally, we propose statistical methods to interpret results, i.e., to find cell populations and genes related to the detected sample level clusters and trajectories..
Short Biography:Ivan G. Costa is Professor for Computational Genomics at the RWTH Aachen. After graduating in computer science in 2003 at the Federal University of Pernambuco (Brazil), he joined the Max Planck Institute for Molecular Genetics (Germany) to pursue doctoral studies in bioinformatics. In 2017, he established a research group on computational genomics at the RWTH Aachen Medical Faculty. His research interest involves statistical machine learning approaches to dissect transcriptional and regulatory programs controlling cellular changes in cell differentiation and in the onset of diseases. He currently focuses on computational methods to understand how cellular microenvironment changes cause or support disease processes by integrative analysis of transcriptional, chromatin and spatial status of single cells.
Abstract: Single cell and spatial sequencing allow measuring full transcriptomes or epigenomes of all cells in a tissue. When applied to disease cohorts, several single cell or spatial experiments across distinct patients are available. One open computational problem is how to compare experiments at a sample level, as a sample is represented by a distribution of cells. We will describe in this talk the use of the optimal transport framework as an approach to obtain distances between distributions of cells. This is used for sample level analysis of disease relevant single cell and spatial transcriptomics data to find clusters or trajectories of patients. Another relevant challenge comes from the multi-modal properties of the data, as single cells can be measured in regard to distinct molecular features (transcriptomes and epigenome) or histology data. This requires algorithms for estimation of joint embeddings, which capture information from all available modalities. Finally, we propose statistical methods to interpret results, i.e., to find cell populations and genes related to the detected sample level clusters and trajectories..
Short Biography:Ivan G. Costa is Professor for Computational Genomics at the RWTH Aachen. After graduating in computer science in 2003 at the Federal University of Pernambuco (Brazil), he joined the Max Planck Institute for Molecular Genetics (Germany) to pursue doctoral studies in bioinformatics. In 2017, he established a research group on computational genomics at the RWTH Aachen Medical Faculty. His research interest involves statistical machine learning approaches to dissect transcriptional and regulatory programs controlling cellular changes in cell differentiation and in the onset of diseases. He currently focuses on computational methods to understand how cellular microenvironment changes cause or support disease processes by integrative analysis of transcriptional, chromatin and spatial status of single cells.
- Organizer: The Data Science and Statistics (DSS) group at IMADA
- Address: Campusvej 55, 5230 Odense M
- Contact Email: campello@imada.sdu.dk
- The seminar is in IMADA conference room (Ø18-509-2): https://clients.mapsindoors.com/sdu/573f26e4bc1f571b08094312/details/563cb9be423b7d0540c9a9aa
- Add to your calendar: https://eom.sdu.dk:443/events/ical/0b32652a-bffb-4bda-87dd-41d256994395