A deep profiler's guide to cytometry.

By Sean Bendall, Garry P. Nolan, Mario Roederer, Pratip K. Chattopadhyay. Trends in immunology, 2012

651 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d2a3045d44336fd56fb16587d796a69d9dbd7bb7

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Citations in the corpus

CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES (2013, methodology): “In cases where algorithms with high scores were more frequent, the ensemble clustering performed better and was less sensitive to the exclusion of several of the algorithms (Challenges 1 and 3).”
Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (2016, background): “Hierarchical subgating based on biaxial plots offers a straight-forward approach to analyze ﬂow cytometry data, but this becomes impractical when interpreting the immense detail generated from mass cytometry (8) or other high-dimensional approaches.”
Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline (2016, background): “limitations of spectral overlap in flow cytometry, and allow for simultaneous analysis of more than 40 markers per cell [3,4].”
The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability (2016, methodology): “In support of these projects, the CL is being used both as a controlled vocabulary of cell types for use as metadata, and as part of an analytical pipeline for analyzing high-dimensional flow cytometry and mass cytometry data (e.g. CyTOF) [69] submitted to the ImmPort data repository.”
Mapping the Diversity of Follicular Helper T Cells in Human Blood and Tonsils Using High-Dimensional Mass Cytometry Analysis. (2015, background): “Mass cytometry (also known as cytometry by time-of-flight, or CyTOF) combines the advantages of both flow cytometry and mass spectrometry by utilizing antibodies conjugated to metal isotopes (Bendall et al., 2012; Newell and Davis, 2014).”
Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types (2017, background): “Future increases in acquisition rate and dimensionality in mass-and ﬂ ow cytometry are expected to amplify this problem signi ﬁ cantly 11,12 .”
Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data (2016, background): “However, the number of parameters is ultimately limited by technical issues such as spectral overlap and autofluorescence (3).”
Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE) (2013, background): “In contrast, mass cytometry allows up to 42 parameters to be quantified on individual cells using metal-chelated probes without any significant signal overlap, thus resolving cellular phenotypes at an unprecedented level of detail (4).”
OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment (2018, background): “These advancements have largely been fueled by technical developments in the field of flow cytometry (7) and single-cell RNA-sequencing (sc-RNAseq) (8).”