scholarly article | Q13442814 |
P356 | DOI | 10.1038/NRI.2016.56 |
P698 | PubMed publication ID | 27320317 |
P50 | author | Bart Lambrecht | Q18402373 |
Sofie Van Gassen | Q56489658 | ||
P2093 | author name string | Yvan Saeys | |
P2860 | cites work | Bioconductor: open software development for computational biology and bioinformatics | Q21194861 |
Data reduction for spectral clustering to analyze high throughput flow cytometry data | Q21284349 | ||
EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes | Q24604581 | ||
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 | Q24605194 | ||
MIFlowCyt: the minimum information about a Flow Cytometry Experiment | Q24644748 | ||
Algorithmic Tools for Mining High-Dimensional Cytometry Data | Q26799355 | ||
Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding | Q27302270 | ||
COMPASS identifies T-cell subsets correlated with clinical outcomes | Q27314516 | ||
Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium | Q28601097 | ||
GenePattern 2.0 | Q29614171 | ||
viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia | Q30628090 | ||
Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data | Q30729963 | ||
OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis | Q30845743 | ||
Systems biology. Conditional density-based analysis of T cell signaling in single-cell data | Q30863890 | ||
flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. | Q30868322 | ||
Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape | Q30878726 | ||
FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data | Q30883687 | ||
New approaches to fluorescence compensation and visualization of FACS data | Q30916909 | ||
Human B-cell and progenitor stages as determined by probability state modeling of multidimensional cytometry data | Q30926044 | ||
Data analysis as a source of variability of the HLA-peptide multimer assay: from manual gating to automated recognition of cell clusters | Q30927297 | ||
ISAC's Gating-ML 2.0 data exchange standard for gating description | Q30951775 | ||
A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. | Q36921764 | ||
Multispectral imaging of hematopoietic cells: where flow meets morphology | Q37186175 | ||
Standardization and optimization of multiparameter intracellular cytokine staining | Q37211056 | ||
Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE). | Q37475009 | ||
Spectral Flow Cytometry | Q38071970 | ||
Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells | Q38264685 | ||
IMMUNOLOGY. An interactive reference framework for modeling a dynamic immune system | Q38385523 | ||
SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design | Q38433447 | ||
FloReMi: Flow density survival regression using minimal feature redundancy | Q38977557 | ||
Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development | Q40297899 | ||
Standardized Multi-Color Flow Cytometry and Computational Biomarker Discovery | Q40360044 | ||
Probability state modeling theory | Q40900332 | ||
HISTORY OF SCIENCE. Flow cytometry strikes gold | Q40911150 | ||
immunoClust--An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets | Q41104497 | ||
Automated flow cytometric analysis across large numbers of samples and cell types | Q41597793 | ||
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells | Q41861103 | ||
ISAC's classification results file format | Q41954438 | ||
Beyond the age of cellular discovery. | Q45955864 | ||
High-dimensional analysis of the murine myeloid cell system. | Q45956087 | ||
Cell population identification using fluorescence-minus-one controls with a one-class classifying algorithm | Q47734228 | ||
Probability state modeling of memory CD8+ T-cell differentiation | Q50886114 | ||
A proposal for unified flow cytometer parameter naming | Q50922165 | ||
ICEFormat—the image cytometry experiment format | Q51312591 | ||
Semi-automated and standardized cytometric procedures for multi-panel and multi-parametric whole blood immunophenotyping | Q57271729 | ||
Subjectivity and flow cytometric variability | Q58040274 | ||
Publication of optimized multicolor immunofluorescence panels | Q62577367 | ||
Quality assurance for polychromatic flow cytometry | Q62577374 | ||
Seventeen-colour flow cytometry: unravelling the immune system | Q62577376 | ||
Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data | Q30952528 | ||
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis | Q30975629 | ||
Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure | Q30987350 | ||
The end of gating? An introduction to automated analysis of high dimensional cytometry data. | Q31019645 | ||
Wishbone identifies bifurcating developmental trajectories from single-cell data | Q31089177 | ||
Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data. | Q31105166 | ||
Data quality assessment of ungated flow cytometry data in high throughput experiments | Q31105822 | ||
Automated gating of flow cytometry data via robust model-based clustering | Q31148045 | ||
flowCore: a Bioconductor package for high throughput flow cytometry | Q33428799 | ||
Automated high-dimensional flow cytometric data analysis | Q33445872 | ||
Per-channel basis normalization methods for flow cytometry data | Q33515614 | ||
Data File Standard for Flow Cytometry, version FCS 3.1. | Q33516620 | ||
Merging mixture components for cell population identification in flow cytometry | Q33557440 | ||
Rapid cell population identification in flow cytometry data | Q33779332 | ||
Flow cytometry data standards | Q33840165 | ||
Automated identification of stratifying signatures in cellular subpopulations | Q33854094 | ||
Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma | Q33875380 | ||
flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data | Q33923634 | ||
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE | Q34037159 | ||
Electronic Separation of Biological Cells by Volume | Q34241313 | ||
Standardizing immunophenotyping for the Human Immunology Project | Q34255224 | ||
B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma | Q34270046 | ||
flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding | Q34272797 | ||
A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects | Q34671240 | ||
Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis | Q34672485 | ||
Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples | Q34845052 | ||
Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry | Q34992218 | ||
Chromosome measurement and sorting by flow systems | Q35074749 | ||
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry | Q35105312 | ||
A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry | Q35458144 | ||
BayesFlow: latent modeling of flow cytometry cell populations | Q35891316 | ||
Competitive SWIFT cluster templates enhance detection of aging changes | Q36533352 | ||
Thinking outside the gate: single-cell assessments in multiple dimensions | Q36777433 | ||
P433 | issue | 7 | |
P921 | main subject | immunology | Q101929 |
flow cytometry | Q1141429 | ||
sensor data | Q45940104 | ||
P304 | page(s) | 449-462 | |
P577 | publication date | 2016-06-20 | |
P13046 | publication type of scholarly work | review article | Q7318358 |
P1433 | published in | Nature Reviews Immunology | Q43355 |
P1476 | title | Computational flow cytometry: helping to make sense of high-dimensional immunology data | |
P478 | volume | 16 |