2015
DOI: 10.1016/j.ymeth.2015.05.008
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Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

Abstract: The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both… Show more

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Cited by 125 publications
(154 citation statements)
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References 58 publications
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“…SPADE and hierarchical clustering of SPADE clusters and markers are proving to be useful analytical approaches for identifying correlations of marker expression (27,92), and which was confirmed with ACCENSE. As previously reported (25), CD21, a complement receptor involved in B cell Ag processing (93), showed patterns of coexpression with CD62L.…”
Section: Discussionmentioning
confidence: 65%
“…SPADE and hierarchical clustering of SPADE clusters and markers are proving to be useful analytical approaches for identifying correlations of marker expression (27,92), and which was confirmed with ACCENSE. As previously reported (25), CD21, a complement receptor involved in B cell Ag processing (93), showed patterns of coexpression with CD62L.…”
Section: Discussionmentioning
confidence: 65%
“…Biomarkers that, first, identify AA patients from HDs and, second, identify at time of diagnosis who are less likely to respond to IST, have as yet not been identified. It is now possible to comprehensively characterize rare, complex populations of cells with minimal bias 15,32 using CyTOF to measure the expression level of more than 40 parameters at the singlecell level. [32][33][34] The complexity of Treg subsets in HDs has been previously demonstrated by mass cytometry on sorted Tregs 35 ; however, their biological importance has not been investigated.…”
Section: Discussionmentioning
confidence: 99%
“…Acquired data were normalized based on normalization beads (Ce 140, Eu151, Eu153, Ho165, and Lu175). 15 Automated clustering was performed on a subset of 800 000 cells sampled from all individuals. The number of cells sampled from each individual was proportional to the total number of cells in that sample.…”
Section: Antibodies and Cell Stainingmentioning
confidence: 99%
“…Going forward, combinations of high-dimensional single-cell approaches, such as mass cytometry, and machine-learning computational analysis tools are expected to systematically reveal and characterize clinically relevant alterations in cancer cell signaling. [66][67][68] …”
Section: Discussionmentioning
confidence: 99%