Articles with "cytometry data" as a keyword



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Learning Single‐Cell Distances from Cytometry Data

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Published in 2019 at "Cytometry Part A"

DOI: 10.1002/cyto.a.23792

Abstract: Recent years have seen an increased interest in employing data analysis techniques for the automated identification of cell populations in the field of cytometry. These techniques highly depend on the use of a distance metric,… read more here.

Keywords: single cell; cell; distance metric; cytometry data ... See more keywords
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PeacoQC: Peak‐based selection of high quality cytometry data

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Published in 2021 at "Cytometry Part A"

DOI: 10.1002/cyto.a.24501

Abstract: In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high‐dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes,… read more here.

Keywords: cytometry data; high quality; cytometry; quality control ... See more keywords
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Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach.

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Published in 2023 at "Proteomics"

DOI: 10.1002/pmic.202200290

Abstract: The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing… read more here.

Keywords: approach; machine learning; cytometry data; biomarker ... See more keywords
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Analysis of Mass Cytometry Data.

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Published in 2019 at "Methods in molecular biology"

DOI: 10.1007/978-1-4939-9454-0_17

Abstract: The CyTOF system produces single cell protein expression data similar to that from flow cytometry, but with an increased number of features measured. Traditionally, analysis of these data is carried out using manual gating, but… read more here.

Keywords: mass cytometry; data analysis; analysis; analysis mass ... See more keywords
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Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data.

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Published in 2022 at "Current opinion in biotechnology"

DOI: 10.1016/j.copbio.2022.102688

Abstract: Dynamic analysis of microbial composition is crucial for understanding community functioning and detecting dysbiosis. Compositional information is mostly obtained through sequencing of taxonomic markers or whole meta-genomes, which may be productively complemented by real-time quantitative… read more here.

Keywords: cytometry data; multiparametric flow; machine; flow cytometry ... See more keywords
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Testing for differential abundance in mass cytometry data

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Published in 2017 at "Nature methods"

DOI: 10.1038/nmeth.4295

Abstract: When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to… read more here.

Keywords: differential abundance; abundance; cytometry data; mass cytometry ... See more keywords
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ShinySOM: graphical SOM-based analysis of single-cell cytometry data

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Published in 2020 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btaa091

Abstract: Abstract Summary ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data… read more here.

Keywords: analysis; cytometry data; som based; shinysom graphical ... See more keywords
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ggCyto: next generation open-source visualization software for cytometry

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Published in 2018 at "Bioinformatics"

DOI: 10.1093/bioinformatics/bty441

Abstract: Motivation Open source software for computational cytometry has gained in popularity over the past few years. Efforts such as FlowCAP, the Lyoplate and Euroflow projects have highlighted the importance of efforts to standardize both experimental… read more here.

Keywords: open source; bioconductor; cytometry data; cytometry ... See more keywords
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ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

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Published in 2021 at "IEEE Transactions on Visualization and Computer Graphics"

DOI: 10.1109/tvcg.2019.2931299

Abstract: Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial… read more here.

Keywords: cytometry data; mass cytometry; analysis; imaging mass ... See more keywords
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CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data

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Published in 2021 at "BMC Bioinformatics"

DOI: 10.1186/s12859-021-04054-2

Abstract: Background The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge.… read more here.

Keywords: mass cytometry; flow mass; cytometry data; cytotree bioconductor ... See more keywords
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A comparison framework and guideline of clustering methods for mass cytometry data

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Published in 2019 at "Genome Biology"

DOI: 10.1186/s13059-019-1917-7

Abstract: BackgroundWith the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification… read more here.

Keywords: mass cytometry; clustering methods; cytometry data; comparison framework ... See more keywords