Agile workflow for interactive analysis of mass cytometry data.

Julia Casado,Antti Häkkinen,Johanna Hynninen,Olli Carpén,Katja Kaipio,Sampsa Hautaniemi,Oskari Lehtonen,Ville Rantanen,Elenora Petrucci,Luca Pasquini,Anniina Färkkilä,Mauro Biffoni,Sakari Hietanen,Mathelier Anthony

doi:10.1093/bioinformatics/btaa946

Abstract

MotivationSingle-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge.ResultsWe present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples.Availabilityand implementationThe method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

Single-cell technologies such as Cytometry by Time-Of-Flight (CyTOF), multiplexed imaging, or single cell RNA sequencing have enabled characterizating tumor-microenvironment compositions and cell populations at a single-cell resolution (Galli et al, 2019)
Common CyTOF analysis steps have steadily reached a quasi-standard workflow that involves manual gating with FlowJoTM or other 2D scatter plot tools followed by dimensionality reduction with t-SNE (Van Der Maaten and Hinton, 2008) and unsupervised clustering
We demonstrate the utility of Cyto with two CyTOF datasets

Summary

Introduction

Single-cell technologies such as Cytometry by Time-Of-Flight (CyTOF), multiplexed imaging, or single cell RNA sequencing have enabled characterizating tumor-microenvironment compositions and cell populations at a single-cell resolution (Galli et al, 2019). Common CyTOF analysis steps have steadily reached a quasi-standard workflow that involves manual gating with FlowJoTM or other 2D scatter plot tools followed by dimensionality reduction with t-SNE (Van Der Maaten and Hinton, 2008) and unsupervised clustering. These analyses are executed with different software or platforms, which maes the resutls prone to errors and biases. Cytofkit (Chen et al, 2016), integrate methods available only within the R ecosystem and no parallelization support due to R limitations, which is suboptimal when analyzing very large data sets.

Methods

Results

Conclusion