Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data

Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE

(2019) Axel Theorell et al.   PLoS One

Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data

(2019) George C. Linderman et al.   NATURE METHODS

Predicting cell populations in single cell mass cytometry data

(2019) Tamim Abdelaal et al.   CYTOMETRY PART A

Ultrafast clustering of single-cell flow cytometry data using FlowGrid

(2019) Xiaoxin Ye et al.   BMC Systems Biology

A comparison of single-cell trajectory inference methods

(2019) Wouter Saelens et al.   NATURE BIOTECHNOLOGY

Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology

(2019) Subarna Palit et al.   Frontiers in Immunology

Quantitative Comparison of Conventional and t-SNE-guided Gating Analyses

(2019) Shadi Toghi Eshghi et al.   Frontiers in Immunology

Immune monitoring using mass cytometry and related high-dimensional imaging approaches

(2019) Felix J. Hartmann et al.   Nature Reviews Rheumatology

flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry

(2018) Markus Lux et al.   BIOINFORMATICS

Background fluorescence and spreading error are major contributors of variability in high-dimensional flow cytometry data visualization by t-distributed stochastic neighboring embedding

(2018) Emilia Maria Cristina Mazza et al.   CYTOMETRY PART A

Computational approaches for high-throughput single-cell data analysis

(2018) Helena Todorov et al.   FEBS Journal

Gate to the Future: Computational Analysis of Immunophenotyping Data

(2018) Florian Mair   CYTOMETRY PART A

cytometree: A binary tree algorithm for automatic gating in cytometry analysis

(2018) Daniel Commenges et al.   CYTOMETRY PART A

Dimensionality reduction for visualizing single-cell data using UMAP

(2018) Etienne Becht et al.   NATURE BIOTECHNOLOGY

A Beginner’s Guide to Analyzing and Visualizing Mass Cytometry Data

(2017) Abigail K. Kimball et al.   JOURNAL OF IMMUNOLOGY

Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry

(2017) Troy J. Comi et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Characterizing cell subsets using marker enrichment modeling

(2017) Kirsten E Diggins et al.   NATURE METHODS

Testing for differential abundance in mass cytometry data

(2017) Aaron T L Lun et al.   NATURE METHODS

Sensitive detection of rare disease-associated cell subsets via representation learning

(2017) Eirini Arvaniti et al.   Nature Communications

Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

(2017) Ye Henry Li et al.   PLoS Computational Biology

densityCut: an efficient and versatile topological approach for automatic clustering of biological data

(2016) Jiarui Ding et al.   BIOINFORMATICS

Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection

(2016) Maziyar Baran Pouyan et al.   BMC Medical Genomics

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data

(2016) Lukas M. Weber et al.   CYTOMETRY PART A

Computational methods for trajectory inference from single-cell transcriptomics

(2016) Robrecht Cannoodt et al.   EUROPEAN JOURNAL OF IMMUNOLOGY

Automated mapping of phenotype space with single-cell data

(2016) Nikolay Samusik et al.   NATURE METHODS

Spectral Cytometry Has Unique Properties Allowing Multicolor Analysis of Cell Suspensions Isolated from Solid Tissues

(2016) Sandrine Schmutz et al.   PLoS One

Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline

(2016) Hao Chen et al.   PLoS Computational Biology

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis

(2015) Jacob H. Levine et al.   CELL

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data

(2015) Sofie Van Gassen et al.   CYTOMETRY PART A

immunoClust-An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets

(2015) Till Sörensen et al.   CYTOMETRY PART A

The end of gating? An introduction to automated analysis of high dimensional cytometry data

(2015) Florian Mair et al.   EUROPEAN JOURNAL OF IMMUNOLOGY

Algorithmic Tools for Mining High-Dimensional Cytometry Data

(2015) Cariad Chester et al.   JOURNAL OF IMMUNOLOGY

An Introduction to Automated Flow Cytometry Gating Tools and Their Implementation

(2015) Chris P. Verschoor et al.   Frontiers in Immunology

flowCL: ontology-based cell population labelling in flow cytometry

(2014) Mélanie Courtot et al.   BIOINFORMATICS

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, Part 2: Biological evaluation

(2014) Tim R. Mosmann et al.   CYTOMETRY PART A

High-dimensional analysis of the murine myeloid cell system

(2014) Burkhard Becher et al.   NATURE IMMUNOLOGY

Automated identification of stratifying signatures in cellular subpopulations

(2014) Robert V. Bruggner et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells

(2014) Benedict Anchang et al.   PLoS Computational Biology

OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis

(2014) Greg Finak et al.   PLoS Computational Biology

Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

(2013) K. Shekhar et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data

(2012) M. D. Linderman et al.   BIOINFORMATICS

flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding

(2012) Y. Ge et al.   BIOINFORMATICS

A deep profiler's guide to cytometry

(2012) Sean C. Bendall et al.   TRENDS IN IMMUNOLOGY

Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

(2011) Peng Qiu et al.   NATURE BIOTECHNOLOGY

A flow cytometry revolution

(2011) Allison Doerr   NATURE METHODS

Data reduction for spectral clustering to analyze high throughput flow cytometry data

(2010) Habil Zare et al.   BMC BIOINFORMATICS

Rapid cell population identification in flow cytometry data

(2010) Nima Aghaeepour et al.   CYTOMETRY PART A

Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data

(2010) Yu Qian et al.   CYTOMETRY PART B-CLINICAL CYTOMETRY

flowClust: a Bioconductor package for automated gating of flow cytometry data

(2009) Kenneth Lo et al.   BMC BIOINFORMATICS

Automated high-dimensional flow cytometric data analysis

(2009) S. Pyne et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA