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t-sne plots  (MathWorks Inc)


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    MathWorks Inc t-sne plots
    T Sne Plots, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/t-sne+plots/pmc12088720-150-22-28
    Average 90 stars, based on 1 article reviews
    t-sne plots - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers.
    Article Snippet: Then, for the three Balanced-X datasets, we used the top-20 features to visualize t-SNE clusters. t-SNE plots were generated using MATLAB’s ‘tsne’ function with the Barnes–Hut algorithm keeping the Barnes–Hut trade-off parameter at 0.5 to increase processing speed for large datasets.

    Article Title: Unsupervised clustering of single-lead electrocardiograms associates with prevalent and incident heart failure in coronary artery disease
    Article Snippet: Subsequently, a representative median heartbeat was calculated from each cluster to characterize the ECG morphology representing each cluster ( ). t-SNE and median heartbeats plots were generated using MATLAB R2022b (The MathWorks Inc.).

    Article Title: CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues
    Article Snippet: For the t-SNE plots in this manuscript the default MATLAB options were used, including: Euclidian distance, perplexity of 30, theta, of 0.5, and exaggeration of 4.

    Article Title: Using Large Datasets to Understand Nanotechnology
    Article Snippet: Algorithms to visualize t-SNE plots have been adapted for use in multiple languages, including R, python, and MATLAB, making the technique easy to use.



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    Image Search Results


    Automatic analysis of the 2P microscopy images of infected cells using SARS-CoV-2 variants D614G and B.1.1.7. A Purple contours indicate the areas automatically defined as particles during this analysis. B , C , E , F Spider charts showing the effects of B variant, C virus titer, E dye concentration, and F photomultiplier tube (PMT) relative voltage on the range-normalized values of seven different image parameters obtained from 2P microscopy images. The error bars for the spider charts can be found in Supplementary Fig. 7. D The image parameter called “relative signal area” shows a significant increase at virus titers higher than TCID 50 10 –3 mL −1 . G The image parameter called “image mean intensity” for the two studied variants at high virus titer (TCID 50 > 10 –3 mL −1 ) at 0.05 μM and 0.5 μM dye concentration. H t -Distributed stochastic neighbor embedding (t-SNE) 2D plot obtained from all the seven image parameters recorded at 0.5 µM dye concentration shows three clusters depending on the virus variant and titer. Inset shows that the three groups, namely no or low infection, infection with D614G variant, and infection with B.1.1.7 variant are clearly separated along the first dimension. I Classification of the images corresponding to different variants at various virus titer in three clusters by seven-dimensional Gaussian mixture model clustering (error bars show standard deviation; significance levels as * p ≤ 0.1; ** p ≤ 0.05; *** p ≤ 0.01.)

    Journal: Cellular & Molecular Biology Letters

    Article Title: Monitoring correlates of SARS-CoV-2 infection in cell culture using a two-photon-active calcium-sensitive dye

    doi: 10.1186/s11658-024-00619-0

    Figure Lengend Snippet: Automatic analysis of the 2P microscopy images of infected cells using SARS-CoV-2 variants D614G and B.1.1.7. A Purple contours indicate the areas automatically defined as particles during this analysis. B , C , E , F Spider charts showing the effects of B variant, C virus titer, E dye concentration, and F photomultiplier tube (PMT) relative voltage on the range-normalized values of seven different image parameters obtained from 2P microscopy images. The error bars for the spider charts can be found in Supplementary Fig. 7. D The image parameter called “relative signal area” shows a significant increase at virus titers higher than TCID 50 10 –3 mL −1 . G The image parameter called “image mean intensity” for the two studied variants at high virus titer (TCID 50 > 10 –3 mL −1 ) at 0.05 μM and 0.5 μM dye concentration. H t -Distributed stochastic neighbor embedding (t-SNE) 2D plot obtained from all the seven image parameters recorded at 0.5 µM dye concentration shows three clusters depending on the virus variant and titer. Inset shows that the three groups, namely no or low infection, infection with D614G variant, and infection with B.1.1.7 variant are clearly separated along the first dimension. I Classification of the images corresponding to different variants at various virus titer in three clusters by seven-dimensional Gaussian mixture model clustering (error bars show standard deviation; significance levels as * p ≤ 0.1; ** p ≤ 0.05; *** p ≤ 0.01.)

    Article Snippet: Seven parameters characterizing the detected particles on the images were defined for each image: (i) relative signal area, (ii) image mean intensity, (iii) mean of the threshold area, (iv) maximum particle intensity, (v) average particle size, (vi) particle percentage area, and (vii) particle mean intensity. t -Distributed stochastic neighbor embedding (t-SNE) 2D plot was obtained in MATLAB using the built-in “tsne” function with default random number generation, a perplexity value of 10, and exaggeration value of 50 on all seven parameters of images acquired with 0.5 μM dye concentration (Supplementary Fig. 12).

    Techniques: Microscopy, Infection, Variant Assay, Virus, Concentration Assay, Standard Deviation