Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Robert Krueger, Johanna Beyer, Won-Dong Jang, Nam Wook Kim, Artem Sokolov, Peter K. Sorger, Hanspeter Pfister

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.
Original languageEnglish (US)
Pages (from-to)227-237
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
StatePublished - Jan 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-02-08
Acknowledged KAUST grant number(s): OSR-2015-CCF-2533
Acknowledgements: We thank Rumana Rashid, Shannon Coy, Sandro Santagata, Anniina Farkkila, and Julia Casado for their valuable input and feedback. We thank Benjamin Izar, Jia-Ren Lin and YuAn Chen for data and advice on the manuscript, and Denis Schapiro, Clarence Yapp, Jeremy
Muhlich for image stitching and segmentation. This work is supported by the Ludwig Center at Harvard Medical School, by NCI Grant U54-CA225088, by King Abdullah University of Science and Technology (KAUST) and the KAUST Office of Sponsored Research (OSR) award OSR-2015-CCF-2533-0.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data'. Together they form a unique fingerprint.

Cite this