GateFinder: projection-based gating strategy optimization for flow and mass cytometry.
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ABSTRACT:
SUBMITTER: Aghaeepour N
PROVIDER: S-EPMC6247943 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The lack of software interoperability with respect to gating due to lack of a standardized mechanism for data exchange has traditionally been a bottleneck, preventing reproducibility of flow cytometry (FCM) data analysis and the usage of multiple analytical tools. To facilitate interoperability among FCM data analysis tools, members of the International Society for the Advancement of Cytometry (ISAC) Data Standards Task Force (DSTF) have developed an XML-based mechanism to formally describe gates (Gating-ML). Gating-ML, an open specification for encoding gating, data transformations and compensation, has been adopted by the ISAC DSTF as a Candidate Recommendation. Gating-ML can facilitate exchange of gating descriptions the same way that FCS facilitated for exchange of raw FCM data. Its adoption will open new collaborative opportunities as well as possibilities for advanced analyses and methods development. The ISAC DSTF is satisfied that the standard addresses the requirements for a gating exchange standard.
| S-EPMC2585156 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-09-26 | GSE120446 | GEO
Project description:Motivation:Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies. Results:We introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that DeepCyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured across several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data. Availability and implementation:Our codes and data are publicly available at https://github.com/KlugerLab/deepcytof.git. Contact:yuval.kluger@yale.edu. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5860171 | biostudies-literature
Project description:New techniques for single-cell analysis have led to insights into hematopoiesis and the immune system, but the ability of these techniques to cross-validate and reproducibly identify the biological variation in diverse human samples is currently unproven. We therefore performed a comprehensive assessment of human bone marrow cells using both single-cell RNA sequencing and multiparameter flow cytometry from twenty healthy adult human donors across a broad age range. These data characterize variation between healthy donors as well as age-associated changes in cell population frequencies. Direct comparison of techniques revealed discrepancy in the quantification of T lymphocyte and natural killer cell populations. Orthogonal validation of immunophenotyping using mass cytometry demonstrated good correlation with flow cytometry. Technical replicates using single-cell RNA sequencing matched robustly, while biological replicates showed variation. Given the increasing use of single-cell technologies in translational research, this resource serves as an important reference dataset and highlights opportunities for further refinement. [Funding source] Project Number: 1ZIAHL006163-05 Contact PI / Project Leader: HOURIGAN, CHRISTOPHER Title: DETECTION, PREVENTION AND TREATMENT OF ACUTE MYELOID LEUKEMIA (AML) RELAPSE. Awardee Organization: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE
2018-09-21 | GSE120221 | GEO
Project description:Bulk RNA Sequencing of Healthy Bone Marrow Mononuclear Cells
2018-09-26 | GSE120444 | GEO
Project description:Analysis of high-dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets. We present three proof-of-concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non-redundant marker set to identify a target cell population.
| S-EPMC3726344 | biostudies-literature
Project description:OBJECTIVE:Human monocyte subsets are defined as classical (CD14++CD16-), intermediate (CD14++CD16+), and nonclassical (CD14+CD16+). Alterations in monocyte subset frequencies are associated with clinical outcomes, including cardiovascular disease, in which circulating intermediate monocytes independently predict cardiovascular events. However, delineating mechanisms of monocyte function is hampered by inconsistent results among studies. APPROACH AND RESULTS:We use cytometry by time-of-flight mass cytometry to profile human monocytes using a panel of 36 cell surface markers. Using the dimensionality reduction approach visual interactive stochastic neighbor embedding (viSNE), we define monocytes by incorporating all cell surface markers simultaneously. Using viSNE, we find that although classical monocytes are defined with high purity using CD14 and CD16, intermediate and nonclassical monocytes defined using CD14 and CD16 alone are frequently contaminated, with average intermediate and nonclassical monocyte purity of ?86.0% and 87.2%, respectively. To improve the monocyte purity, we devised a new gating scheme that takes advantage of the shared coexpression of cell surface markers on each subset. In addition to CD14 and CD16, CCR2, CD36, HLA-DR, and CD11c are the most informative markers that discriminate among the 3 monocyte populations. Using these additional markers as filters, our revised gating scheme increases the purity of both intermediate and nonclassical monocyte subsets to 98.8% and 99.1%, respectively. We demonstrate the use of this new gating scheme using conventional flow cytometry of peripheral blood mononuclear cells from subjects with cardiovascular disease. CONCLUSIONS:Using cytometry by time-of-flight mass cytometry, we have identified a small panel of surface markers that can significantly improve monocyte subset identification and purity in flow cytometry. Such a revised gating scheme will be useful for clinical studies of monocyte function in human cardiovascular disease.
| S-EPMC5828170 | biostudies-literature
Project description:BackgroundThere are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments.ResultsTo circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 106 data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.ConclusionsMisty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.
| S-EPMC2967560 | biostudies-literature
Project description:SummaryflowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript.Availability and implementationR source code freely available through BioConductor (http://master.bioconductor.org/packages/devel/bioc/html/flowDensity.html.). Data available from FlowRepository.org (dataset FR-FCM-ZZBW).Contactrbrinkman@bccrc.caSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4325545 | biostudies-literature
Project description:BackgroundData obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating.ResultsWe propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow .ConclusionsoptimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.
| S-EPMC7590740 | biostudies-literature