High-dimensional analysis of the murine myeloid cell system

By B. Becher, A. Schlitzer, Jinmiao Chen, F. Mair, Hermi Sumatoh, K. Teng, Donovan Low, C. Ruedl, Paola Riccardi-Castagnoli, Michael Poidinger and 3 more. 2014

350 citations recorded by the source corpus.

Subfields: Immunology

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In Immunology

Citations in the corpus

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, methodology): “In addition, t-SNE maps are used to categorize single cell data into relevant biological populations for downstream quanti ﬁ cation, achievable through expert-guided ﬁ ltering (gating) 4 or unsupervised clustering of the map 5 – 7 .”
Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (2016, methodology): “One such method, called t -distributed stochastic neighbor embedding ( t -SNE) (9), performs exceptionally well on mass cytometry data (10, 12, 14). t -SNE performs pair-wise comparisons of all events and maps them in a low dimensional space, optimally arranging similar events nearby and dissimilar…”
Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline (2016, methodology): “Firstly, cytofkit provides state-of-the-art clustering methods including DensVM [16], FlowSOM [23] and PhenoGraph [10], as well as an in-house newly developed algorithm named ClusterX for automatic detection of cell subpopulations.”
Mass cytometry of follicular lymphoma tumors reveals intrinsic heterogeneity in proteins including HLA-DR and a deficit in non-malignant plasmablast and germinal center B cell populations (2017, background): “5) to cluster cells into cell subsets (16,24,28).”
Computational flow cytometry: helping to make sense of high-dimensional immunology data (2016, methodology): “This approach is used by ACCENSE 65 and DensVM 66 .”
Approximated and User Steerable tSNE for Progressive Visual Analytics (2015, methodology): “used to show a tSNE embedding [5], [6], [7], [9] because of their ability to visualize features at different scales.”
Linear tSNE optimization for the Web (2018, methodology): “The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm [27] has been accepted as the state of the art for nonlinear dimensionality reduction applied to visual analysis of high-dimensional space in several application areas, such as life sciences [2], [4], [15] and machine learning model un”
Hierarchical Stochastic Neighbor Embedding (2016, methodology): “In the last decade, the application of non-linear dimensionality reduction techniques on real-world data led to new ﬁndings as complex real-world phenomena lead to non-convex structures that resides in a high-dimensional space [ADT ∗ 13, BSC ∗ 14].”
Low-dose IL-2 selectively activates subsets of CD4+ Tregs and NK cells. (2016, background): “tional markers in all major lymphocyte subsets at single-cell resolution (43, 53).”
High‐Dimensional Single‐Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease (2018, positive): “We created a high-dimensional single-cell proteome atlas of immune populations using high parametric mass cytometry (Bandura et al., 2009; Becher et al., 2014) combinedwith location information from immunohistochemistry (IHC) and verified by 22-color fluorescence cytometry and reporter and…”