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• W3021526713 abstract "The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches." @default.
• W3021526713 created "2020-05-13" @default.
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• W3021526713 date "2020-04-28" @default.
• W3021526713 modified "2023-10-14" @default.
• W3021526713 title "Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data" @default.
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• W3021526713 doi "https://doi.org/10.3389/fcell.2020.00234" @default.
• W3021526713 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7198724" @default.
• W3021526713 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32411698" @default.
• W3021526713 hasPublicationYear "2020" @default.
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