<i>CytoML</i> for cross‐platform cytometry data sharing

Finak, Greg; Jiang, Wenxin; Gottardo, Raphaël

doi:10.1002/cyto.a.23663

Cited by 36 publications

(31 citation statements)

References 33 publications

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“…To avoid biases by manual analysis of high complex flow, data software tools are available that work partly operator independent. This stresses also the importance of the reproducibility in complex, (semi)‐automated data analysis . O'Neill and Brinkman have suggested that certain data besides compensation, gating details and mathematical algorithms, should be shared for reproducible FCM bioinformatics .…”

Section: Data Handling Evaluation Storage and Repositoriesmentioning

confidence: 99%

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

et al. 2019

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These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer‐reviewed by leading experts in the field, making this an essential research companion.

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Section: Data Handling Evaluation Storage and Repositoriesmentioning

confidence: 99%

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

et al. 2019

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“…We optimized performance of hierarchical gating templates implemented in OpenCyto iteratively to increase the Pearson correlation value r > 0.80 prior to evaluating the remaining ~10000 HRS samples which still underwent a manual review in FlowAnnotator. This was facilitated by the use of CytoML 6 , which allows for the importing and exporting of gated cytometry data between commercial platforms (Cytobank, FlowJo, and Diva) and the R statistical programming language. FlowAnnotator facilitated combination of automated and manual data analysis and enabled successful large-scale implementation of a hybrid method in a large population study.…”

Section: Discussionmentioning

confidence: 99%

Validation of a hybrid approach to standardize immunophenotyping analysis in large population studies: The Health and Retirement Study

Hunter-Schlichting

Lane²,

Cole³

et al. 2020

Sci Rep

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traditional manual gating strategies are often time-intensive, place a high burden on the analyzer, and are susceptible to bias between analyzers. Several automated gating methods have shown to exceed performance of manual gating for a limited number of cell subsets. However, many of the automated algorithms still require significant manual interventions or have yet to demonstrate their utility in large datasets. therefore, we developed an approach that utilizes a previously published automated algorithm (opencyto framework) with a manually created hierarchically cell gating template implemented, along with a custom developed visualization software (flowAnnotator) to rapidly and efficiently analyze immunophenotyping data in large population studies. This approach allows pre-defining populations that can be analyzed solely by automated analysis and incorporating manual refinement for smaller downstream populations. We validated this method with traditional manual gating strategies for 24 subsets of T cells, B cells, NK cells, monocytes and dendritic cells in 931 participants from the Health and Retirement Study (HRS). our results show a high degree of correlation (r ≥ 0.80) for 18 (78%) of the 24 cell subsets. For the remaining subsets, the correlation was low (<0.80) primarily because of the low numbers of events recorded in these subsets. The mean difference in the absolute counts between the hybrid method and manual gating strategy of these cell subsets showed results that were very similar to the traditional manual gating method. We describe a practical method for standardization of immunophenotyping methods in large scale population studies that provides a rapid, accurate and reproducible alternative to labor intensive manual gating strategies. Flow cytometry (FCM) provides a high dimensional quantitative measure for single cell analysis of the immune system. Manual gating using analyzer-defined boundaries or "gates" to identify cell populations of interest is commonly analyzed using proprietary software (e.g. FlowJo Tree Star Inc version 10). In the context of large-scale epidemiological investigations involving thousands of samples, this time consuming and labor-intensive process depends on the skill of the analyst, thereby introducing subjectivity that can increase variability among analysts and limit reproducibility of flow cytometry assays. Recent technological advancements in computational methods help reduce the subjectivity intrinsic to manual gating for multi-dimensional flow cytometry data and promote standardization of immunophenotyping analysis in large population studies. Several automated analyses tools such as OpenCyto 1 and FLOCK 2,3 and hybrid tools such as DAFi 4 and FlowGM 5 have been validated in recent years with good concordance when compared to manually gated datasets. Additional tools such as CytoML 6 allow data to be shared across platforms, which makes a streamlined analysis using both automated and manual analysis possible. Recently, the Flow Cytometry

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“…ISAC recommendations on specific criteria for recording and reporting information about a flow cytometry experiment were first published more than 10 years ago as MIFlowCyt, the "Minimum Information about a Flow Cytometry Experiment" (19). Further support was provided by publications of protocols outlining the steps involved in data deposition, sharing, and annotation (44) as well as by the development of software packages that facilitate data exchange and integration between computational systems and enable cross-platform import, export, and sharing of gated cytometry data (45). This was complemented by FlowRepository, a database containing predominantly published and peer-reviewed flow cytometry experiments allowing query and download of deposited data (43).…”

Section: Existing Flow Cytometry Resources and Guidelines On Preparinmentioning

confidence: 99%

“…This was complemented by FlowRepository, a database containing predominantly published and peer-reviewed flow cytometry experiments allowing query and download of deposited data (43). Further support was provided by publications of protocols outlining the steps involved in data deposition, sharing, and annotation (44) as well as by the development of software packages that facilitate data exchange and integration between computational systems and enable cross-platform import, export, and sharing of gated cytometry data (45). More recently, it was proposed that flow cytometry data produced in clinical studies should also be deposited in FlowRepository, but issues such as compliance with data privacy policies hamper such considerations (46).…”

Section: Existing Flow Cytometry Resources and Guidelines On Preparinmentioning

confidence: 99%

MiSet RFC Standards: Defining a Universal Minimum Set of Standards Required for Reproducibility and Rigor in Research Flow Cytometry Experiments

et al. 2019

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Poor adherence to best practices, insufficient training, and pressure to produce data quickly may lead to publications of suboptimal biomedical research flow cytometry data, which contributes to the body of irreproducible research findings. In addition, documentation of compliance with best flow cytometry practices for submission, visualization, and publication of flow cytometry data is currently endorsed by very few scientific journals, which is particularly concerning as numerous peer-reviewed flow cytometry publications emphasize instrumentation, experimental design, and data analysis as important sources of variability. Guidelines and resources for adequate reporting, annotation and deposition of flow cytometry experiments are provided by MIFlowCyt and the FlowRepository database, and comprehensive expert recommendations covering principles and techniques of field-specific flow cytometry applications have been published. To facilitate the integration of quality-defining parameters into manuscript and grant submission and publication requirements across biomedical fields that rely on the use of flow-cytometry-based techniques, a single comprehensive yet easily and universally applicable document is needed. To produce such a list of gold-standard parameters that assess whether a research flow cytometry experiment has been planned, conducted, interpreted, and reported at the highest standard, a new initiative defining the minimum set of standards a robust and rigorous research flow experiment must fulfill (MiSet RFC Standards) was proposed at CYTO 2019. MiSet RFC Standards will integrate and simplify existing resources to provide a universal benchmark a flow cytometry experiment can easily be measured against. The goal of MiSET RFC Standards is its integration into peerreview and publication procedures through partnership with stakeholders, journals and publishers in biomedical and translational research. This article introduces the aims and anticipated timeline and discusses strategies for interdisciplinary consensus and implementation. A single-resource broadly applicable guideline will harmonize standards across different fields of biomedical research and lead to publication of more robust research findings.

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CytoML for cross‐platform cytometry data sharing

Cited by 36 publications

References 33 publications

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

Validation of a hybrid approach to standardize immunophenotyping analysis in large population studies: The Health and Retirement Study

MiSet RFC Standards: Defining a Universal Minimum Set of Standards Required for Reproducibility and Rigor in Research Flow Cytometry Experiments

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