<scp>ISAC</scp>'s <scp>G</scp>ating‐<scp>ML</scp> 2.0 data exchange standard for gating description

Špidlen, Josef; Moore, Wayne A.; Force, Isac Data Standards Task; Brinkman, Ryan R.

doi:10.1002/cyto.a.22690

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…Raw and processed data can also be easily downloaded, which can be used to reproduce the analyses locally or perform novel analyses. Automated and manual gate definition can also be exported in Gating-ML 25 , an open standard extensible markup language for describing flow cytometry gating, as well as CLR format .…”

Section: Methodsmentioning

confidence: 99%

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Finak

Langweiler

Jaimes

et al. 2016

Sci Rep

245

235

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Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.

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Section: Methodsmentioning

confidence: 99%

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Finak

Langweiler

Jaimes

et al. 2016

Sci Rep

245

235

View full text Add to dashboard Cite

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“…“intact/lymphocyte/CD3−/CD14−/CD19+/CD38+”). These two fields in ImmPort are not meant to provide a fully reproducible description of how experiments were conducted, which is captured in other standards such as MIFlowCyt [5] or Gating-ML [6], but rather are intended to reflect how immunologists typically describe targeted cell populations in journal articles and allow one to query for experiments in which cell populations of interest were studied.…”

Section: Introductionmentioning

confidence: 99%

Reporting and connecting cell type names and gating definitions through ontologies

Overton¹,

Vita

Dunn³

et al. 2019

BMC Bioinformatics

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Background Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. ‘T cells’), and the description of the marker pattern utilized (e.g. CD14−, CD3+). Results We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two. Conclusions We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.

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“…The Gating‐ML standard was developed to tackle this problem . It is designed to describe the gates that define different cell populations and the hierarchical relationships between them.…”

Section: Introductionmentioning

confidence: 99%

CytoML for cross‐platform cytometry data sharing

2018

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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.

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

Cited by 14 publications

References 8 publications

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Reporting and connecting cell type names and gating definitions through ontologies

CytoML for cross‐platform cytometry data sharing

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