Journal: Cell reports

Article Title: Circuit dynamics of superficial and deep CA1 pyramidal cells and inhibitory cells in freely moving macaques

doi: 10.1016/j.celrep.2024.114519

Figure Lengend Snippet:

Article Snippet: For these UMAP procedures, we employed a custom UMAP function implemented in MATLAB.

Techniques: Software, Diagnostic Assay

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci of players in the FIFA 2021 dataset obtained by the UMAP with the distances: ( a ) d A r ; ( b ) d C a ; ( c ) d C o ; ( d ) d L o .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci obtained by the UMAP with the Canberra distance d C a for the FIFA 2021 dataset. The colormap is proportional to the attributes: ( a ) ln ( overall ) ; ( b ) ln ( value _ eur ) ; ( c ) ln ( wage _ eur ) ; ( d ) ln ( release _ clause _ eur ) .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci of players in the groups {Defenders, Centre Midfielders, Wingers, Strikers} the FIFA 2021 dataset obtained by the UMAP with the distances: ( a ) d A r ; ( b ) d C a ; ( c ) d C o ; ( d ) d L o .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D loci obtained by the UMAP with the Canberra distance for the FIFA 2021 dataset: ( a ) Goalkeepers; ( b ) Strikers. The colormap is proportional to the attribute ln ( value _ eur ) .

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques:

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable goalkeepers in the FIFA 2021 dataset. The reference is J. Oblak and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable defenders in the FIFA 2021 dataset. The reference is V. van Dijkand and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable midfielders in the FIFA 2021 dataset. The reference is K. De Bruyne and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable wingers in the FIFA 2021 dataset. The reference is Neymar Jr and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Entropy

Article Title: Uniform Manifold Approximation and Projection Analysis of Soccer Players

doi: 10.3390/e23070793

Figure Lengend Snippet: The 3D locus generated by the UMAP with the Canberra distance for the N = 100 most valuable strikers in the FIFA 2021 dataset. The reference is L. Messi and w = 10 . The size of the circular marks and the colormap are proportional to the attributes wage _ eur and value _ eur , respectively.

Article Snippet: For implementing the UMAP dimensionality reduction, clustering and visualization tool we used the Matlab UMAP code, version 2.1.3 , developed by Stephen Meehan et al. [ ].

Techniques: Generated

Journal: Cell reports

Article Title: CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

doi: 10.1016/j.celrep.2020.107523

Figure Lengend Snippet: (A) Confocal image and zoom-in image of a LN section from a C57BL/6 mouse, adoptively transferred with 10^6 naive OT-II CD4+ T cells and immunized with OVA plus alum. Overview image scale bar, 200 μm; zoom-in scale bar, 30 μm. Only select channels are shown. (B) Histo-cytometry plots of cell object MFI for different channels demonstrating the gating used for identification of the indicated immune cell populations. (C) Positional plot of cell data from (B) (area matches the zoom-in image in A). CytoMAP was used to calculate the number of cells in 30-μm-radius neighborhoods (denoted by the circles in the bottom left), which were raster scanned as denoted by the arrow. (D) Heatmap of the neighborhood composition (percentage of each cell phenotype per neighborhood) after SOM clustering. Individual clusters, or “regions,” aredenoted by the color bar at the top of the graph. Arrowheads at the bottom highlight specific neighborhoods. (E) Region color-coded positional plot of the neighborhoods from (D). (F) Pseudo-space plot with the neighborhoods sorted based on B cell composition (sorted to the left) and T cell composition (sorted to the right). (G) Dimensionality reduction plots of the neighborhoods in which the standardized numbers of cells and total MFI of all channels were used for the dimensionalityreduction. t-SNE, PCA, UMAP, and PHATE were all calculated for the same input neighborhoods, which are color coded based on region type from (D). For this experiment, an imaging volume of 0.03 mm 3 , 139,399 cells, and 11,328 neighborhoods were analyzed.

Article Snippet: We used the e4 MATLAB implementation of UMAP, with default parameters, provided by the Herzenberg Lab at Stanford University available for download at: https://www.mathworks.com/matlabcentral/fileexchange/71902-uniform-manifold-approximation-and-projection-umap .> We used the MATLAB implementation of PHATE, with default parameters, provided by Krishnaswamy Lab available for download at: https://github.com/KrishnaswamyLab/PHATE .

Techniques: Cytometry, Imaging