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

Denis SchapiroHartland W JacksonSwetha Raghuraman Jana R Fischer Vito R T ZanotelliDaniel SchulzCharlotte GiesenRaúl CatenaZsuzsanna VargaBernd Bodenmiller

Published in: Nature methods (2017)

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.

Keyphrases

single cell
rna seq
high throughput
gene expression
cell therapy
clinical practice
stem cells
mass spectrometry
machine learning
optical coherence tomography
convolutional neural network
young adults