ISAC's classification results file format

CytoML for cross-platform cytometry data sharing

CytoML is an R/Bioconductor package that enables cross‐platform import, export, and sharing of gated cytometry data. It currently supports Cytobank, FlowJo, Diva, and R, allowing users to import gated cytometry data from commercial platforms into R. Once data are available in R, the data can be further manipulated. For example it can be combined with other computational and analytic approaches, and the results can be exported to FlowJo or Cytobank to be explored by researchers using those platforms. We demonstrate how CytoML and related R packages can be used as a tool to import, modify and export several samples stained with the T cell panel from the FlowCAP IV Lyoplate data set. Once imported, the gating is modified using computational approaches, and exported for visualization in Cytobank and FlowJo. We further show how CytoML can be used to import gated data from a publicly accessible mass cytometry experiment from Cytobank. CytoML is the only tool that allows such sharing of gated cytometry data between researchers working across different platforms, and it will serve as a useful tool for validating and verifying the reproducibility of analyses. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: (1) distinguishing initial populations, (2) revealing cell subsets, and (3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.

ISAC's Gating-ML 2.0 data exchange standard for gating description

The lack of software interoperability with respect to gating has traditionally been a bottleneck preventing the use of multiple analytical tools and reproducibility of flow cytometry data analysis by independent parties. To address this issue, ISAC developed Gating-ML, a computer file format to encode and interchange gates. Gating-ML 1.5 was adopted and published as an ISAC Candidate Recommendation in 2008. Feedback during the probationary period from implementors, including major commercial software companies, instrument vendors, and the wider community, has led to a streamlined Gating-ML 2.0. Gating-ML has been significantly simplified and therefore easier to support by software tools. To aid developers, free, open source reference implementations, compliance tests, and detailed examples are provided to stimulate further commercial adoption. ISAC has approved Gating-ML as a standard ready for deployment in the public domain and encourages its support within the community as it is at a mature stage of development having undergone extensive review and testing, under both theoretical and practical conditions.