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histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

by Raghuraman, Swetha , Zanotelli, Vito R T , Schulz, Daniel , Schapiro, Denis , Bodenmiller, Bernd , Giesen, Charlotte , Varga, Zsuzsanna , Jackson, Hartland W , Catena, Raúl , Fischer, Jana R

in 13 / 14 / 14/1 / 631/1647/245 / 631/553/794 / 82 / 82/58 / Algorithms / Bioinformatics / Biological Microscopy / Biological Techniques / Biology / Biomedical Engineering/Biotechnology / Breast cancer / brief-communication / Cell Communication - physiology / Cell interactions / Computer applications / Cytometry / Disease / Ecosystem biology / Flow Cytometry - methods / Genetic aspects / Genotype & phenotype / Histology / Identification and classification / Image Interpretation, Computer-Assisted - methods / Life Sciences / Medical research / Microenvironments / Molecular Imaging - methods / Multiplexing / Phenotypes / Physiological aspects / Proteome - metabolism / Proteomics / Social networks / Software / Tissue analysis / Tissue Array Analysis - methods / User-Computer Interface / Visualization

2017

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histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

by Raghuraman, Swetha , Zanotelli, Vito R T , Schulz, Daniel , Schapiro, Denis , Bodenmiller, Bernd , Giesen, Charlotte , Varga, Zsuzsanna , Jackson, Hartland W , Catena, Raúl , Fischer, Jana R

2017

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histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

by Raghuraman, Swetha , Zanotelli, Vito R T , Schulz, Daniel , Schapiro, Denis , Bodenmiller, Bernd , Giesen, Charlotte , Varga, Zsuzsanna , Jackson, Hartland W , Catena, Raúl , Fischer, Jana R

2017

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Journal Article

histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

Raghuraman, Swetha,

Zanotelli, Vito R T,

Schulz, Daniel,

Schapiro, Denis,

Bodenmiller, Bernd,

Giesen, Charlotte,

Varga, Zsuzsanna,

Jackson, Hartland W,

Catena, Raúl,

Fischer, Jana R

2017

Overview

The histology topography cytometry analysis toolbox (histoCAT) enables quantitative analysis and exploration of highly multiplexed imaging data for better understanding of individual cells in the context of tissue architecture. Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell–cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

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Publisher

Nature Publishing Group US,Nature Publishing Group

Subject

/ 14

/ 14/1

/ 631/1647/245

/ 631/553/794

/ 82

/ 82/58

/ Algorithms