On the public citation graph
Rapid single-cell cytometry data visualization with EmbedSOM
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cyto.a.22625openalex-snapshot
FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data
inferredcites →https://doi.org/10.1002/cyto.a.23030openalex-snapshot
Comparison of clustering methods for high‐dimensional single‐cell flow and mass cytometry data
inferredcites →https://doi.org/10.1007/s10044-014-0367-9openalex-snapshot
Topology-oriented self-organizing maps: a survey
inferredcites →https://doi.org/10.1007/s11063-014-9383-4openalex-snapshot
GPU Accelerated Self-Organizing Map for High Dimensional Data
inferredcites →https://doi.org/10.1016/j.cell.2015.05.047openalex-snapshot
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
inferredcites →https://doi.org/10.1016/j.celrep.2017.12.057openalex-snapshot
Identification of Cardiomyocyte-Fated Progenitors from Human-Induced Pluripotent Stem Cells Marked with CD82
inferredcites →https://doi.org/10.1016/j.csbj.2018.10.004openalex-snapshot
Cytofast : A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations
inferredcites →https://doi.org/10.1038/nbt.4314openalex-snapshot
Dimensionality reduction for visualizing single-cell data using UMAP
inferredcites →https://doi.org/10.1038/nm.4466openalex-snapshot
High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy
inferredcites →https://doi.org/10.1038/nri.2016.56openalex-snapshot
Computational flow cytometry: helping to make sense of high-dimensional immunology data
inferredcites →https://doi.org/10.1038/s41467-017-01689-9openalex-snapshot
Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types
inferredcites →https://doi.org/10.1038/s41467-019-13055-yopenalex-snapshot
Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
inferredcites →https://doi.org/10.1038/s41587-019-0071-9openalex-snapshot
A comparison of single-cell trajectory inference methods
inferredcites →https://doi.org/10.1038/s42003-019-0415-5openalex-snapshot
diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering
inferredcites →https://doi.org/10.1084/jem.20171934openalex-snapshot
Mass cytometry reveals innate lymphoid cell differentiation pathways in the human fetal intestine
inferredcites →https://doi.org/10.1101/273862openalex-snapshot
Comparative Analysis of Linear and Nonlinear Dimension Reduction Techniques on Mass Cytometry Data
inferredcites →https://doi.org/10.1109/34.1000238openalex-snapshot
Hierarchical GTM: constructing localized nonlinear projection manifolds in a principled way
inferredcites →https://doi.org/10.1109/cahpc.2018.8645912openalex-snapshot
T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data
inferredcites →https://doi.org/10.1109/tvcg.2016.2570755openalex-snapshot
Approximated and User Steerable tSNE for Progressive Visual Analytics
inferredcites →https://doi.org/10.1111/cgf.12878openalex-snapshot
Hierarchical Stochastic Neighbor Embedding
inferredcites →https://doi.org/10.1126/sciimmunol.aan2946openalex-snapshot
An immune clock of human pregnancy
inferredcites →https://doi.org/10.1186/s13059-019-1663-xopenalex-snapshot
PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells
inferredcites →https://doi.org/10.12688/f1000research.15666.2openalex-snapshot
A systematic performance evaluation of clustering methods for single-cell RNA-seq data
inferredcites →https://doi.org/10.4049/jimmunol.1701494openalex-snapshot
A Beginner’s Guide to Analyzing and Visualizing Mass Cytometry Data
inferredcites →https://doi.org/10.5555/2627435.2697068openalex-snapshot
Accelerating t-SNE using tree-based algorithms
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.csbj.2020.03.024openalex-snapshot
Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry
inferred← citeshttps://doi.org/10.1038/s41467-019-13055-yopenalex-snapshot
Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
inferred← citeshttps://doi.org/10.1093/bioadv/vbab026openalex-snapshot
Dimensional reduction of phenotypes from 53 000 mouse models reveals a diverse landscape of gene function
inferred← citeshttps://doi.org/10.12688/f1000research.21642.1openalex-snapshot
Generalized EmbedSOM on quadtree-structured self-organizing maps
inferred← citeshttps://doi.org/10.3390/app11146241openalex-snapshot
One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study
inferred← citeshttps://doi.org/10.3390/vaccines8010138openalex-snapshot
From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies
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