MultiMAP: a novel approach for integrating and visualizing multiomic single-cell and spatial data
Multimodal data are rapidly growing in many fields, including single-cell biology. Emerging single-cell technologies are providing high-resolution measurements of different data types. Integrating these measurements will open avenues for more comprehensive studies of cellular identity, cell–cell interactions, developmental dynamics, and tissue structure.
This talk introduces a new approach for the integration of single-cell multiomics. Compared to other methods, MultiMAP is extremely fast and leverages the entire data set, enabling researchers to take full advantage of multiomic data. With scRNA-seq and scATAC-seq data generated simultaneously from the same single cells, we use MultiMAP to study transcription-factor (TF) expression and TF binding site accessibility during T-cell differentiation.
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What will you learn?
- How multiomic single-cell studies can provide high-resolution measurements for diverse research questions
- How MultiMAP, a new computational approach, can quickly integrate different data types
- How joint single-cell epigenomic and transcriptomic data can offer new insights in T-cell differentiation (Reference: https://www.biorxiv.org/content/10.1101/2021.02.16.431421v2)
Who may this interest?
- Research Institutions
- Translational researchers
Mika Sarkin Jain
University of Cambridge & Wellcome Sanger Institute (UK)
Mika Sarkin Jain is a PhD student in Physics at the University of Cambridge. He is a member of the Cavendish Laboratory and the Wellcome Sanger Institute and is supervised by Sarah Teichmann. Mika focuses on developing machine-learning techniques for high-dimensional data and the application of these techniques to understand heterogeneous and time-dynamic biological systems. Mika received an MPhil in Physics from Cambridge, an MS in Computer Science and BS in Physics, both from Stanford. Mika is supported by a Gates Cambridge Scholarship.