A5093891402- Single-cell and spatial transcriptomics
- Immune cells in cancer
- Gene expression and cancer classification
- Extracellular vesicles in disease
- Molecular Biology Techniques and Applications
Swiss Cancer Center Léman
2024
SIB Swiss Institute of Bioinformatics
2024
Ludwig Cancer Research
2024
University of Lausanne
2024
Abstract The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions cells from complex tissues can be phenotypically profiled across multiple modalities. scaling computational methods to analyze such data is a constant challenge and tools need regularly updated, if not redesigned, cope with ever-growing numbers cells. Over the last few years, metacells have been introduced reduce size complexity while preserving...
Abstract Summary Spatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. As of today, thousands genes can be detected across hundreds spots. Akin standard single-cell RNA-Seq data, spatial transcriptomic data are very sparse due the limited amount RNA within each spot. Building upon metacell concept, we present a workflow, called SuperSpot, combine adjacent transcriptionally similar spots into “metaspots”....
Spatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. With current technologies such as VisiumHD, thousands of genes can be detected across millions spots (also called cells or bins depending on the technologies). Building upon metacell concept, we present a workflow, SuperSpot, combine adjacent transcriptomically similar into "metaspots". The process involves representing nodes in graph with edges...