A5008114519- 3D Modeling in Geospatial Applications
- Semantic Web and Ontologies
- Cell Image Analysis Techniques
- Gene expression and cancer classification
- Single-cell and spatial transcriptomics
- Advanced Clustering Algorithms Research
- Data Management and Algorithms
- Model-Driven Software Engineering Techniques
- AI in cancer detection
- Image Processing Techniques and Applications
- RNA modifications and cancer
- Advanced Proteomics Techniques and Applications
- Glycosylation and Glycoproteins Research
- Genomic variations and chromosomal abnormalities
- Mathematical Biology Tumor Growth
BC Cancer Agency
2004-2023
University of British Columbia
2022-2023
The University of Texas Southwestern Medical Center
2004
The tissue architecture of classic Hodgkin Lymphoma (CHL) is ｕｎｉｑｕｅ among cancers and characterized by rare malignant Reed-Sternberg cells that co-evolve with a complex ecosystem immune in the tumor microenvironment (TME). lack comprehensive systems-level interrogation has hindered description disease heterogeneity clinically relevant molecular subtypes. Here, we employed an integrative, multimodal approach to characterize CHL tumors using cell sequencing, spatial transcriptomics imaging...
Abstract Motivation Recent advances in spatial proteomics technologies have enabled the profiling of dozens proteins thousands single cells situ. This has created opportunity to move beyond quantifying composition cell types tissue, and instead probe relationships between cells. However, most current methods for clustering data from these assays only consider expression values ignore context. Furthermore, existing approaches do not account prior information about expected populations a...
Abstract Motivation Single cell segmentation is critical in the processing of spatial omics data to accurately perform type identification and analyze expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training which are highly dependent user expertise. To ensure quality segmentation, current evaluation strategies quantify accuracy by assessing cellular masks through iterative inspection pathologists. While these each address either statistical...
Single cell segmentation is critical in the processing of spatial omics data to accurately perform type identification and analyze expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training which are highly dependent user expertise. To ensure quality segmentation, current evaluation strategies quantify accuracy by assessing cellular masks through iterative inspection pathologists. While these each address either statistical biological aspects there...
Abstract Emerging spatial proteomics technologies have created new opportunities to move beyond quantifying the composition of cell types in tissue and begin probing structure. However, current methods for analysing such data are designed non-spatial ignore information. We present SpatialSort, a spatially aware Bayesian clustering approach that allows incorporation prior biological knowledge. SpatialSort clusters cells by accounting affinities different neighbours space. Additionally,...