t-distributed stochastic neighbor embedding (t-sne) matlab r2020a  (MathWorks Inc)

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

Journal: Frontiers in Molecular Neuroscience

Article Title: Type 2 cytokines sensitize human sensory neurons to itch-associated stimuli

doi: 10.3389/fnmol.2023.1258823

Figure Lengend Snippet: (A) t-SNE with Louvain clustering of DRG neurons from reanalyzed single nuclei RNA sequencing . (B) Expression density of select genes on the t-SNE plot. (C) Average expression level and frequency of selected marker genes of established neuronal subsets within each cluster. (D) Average expression level and frequency of genes selected during marker. Analysis of putative pruriceptors clusters C6, C9, and C11 from BioTuring “ find markers ” function. (E) Average expression level and frequency of select genes involved in type 2 cytokine receptor signaling within each cluster. (F) t-SNE plots depicting the expression density of type 2 cytokine receptors IL-4Rα and IL-13Rα1. (G) Enrichment scores of type 2 cytokine-associated signaling genes highlighted in blue in panel (E) . AUCell score for each neuron is represented in the indicated color scale for each cell in the scatter plot (top) and as an average for each cluster (bottom). DRG, dorsal root ganglion; IL, interleukin; t-SNE, t-distributed stochastic neighbor embedding.

Article Snippet: Raw data from were reanalyzed and visualized with 2D t-distributed stochastic neighbor embedding (t-SNE) with Louvain clustering (resolution = 1) in BioTuring Talk2Data browser.

Techniques: RNA Sequencing, Expressing, Marker

Journal: Cerebral Cortex (New York, NY)

Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

doi: 10.1093/cercor/bhac523

Figure Lengend Snippet: Segmentation of Ca 2+ imaging data reveals spatially similar ICs across time segments and scales. A) Schematic showing how data is parsed into equal length time segments (set of images show data time segments) for different timescales which are run through the JADE ICA algorithm independently to obtain new ICA solutions (lower images; each color represents an IC). B) Example template map based on an entire mouse’s dataset and time segment ICA map at the day timescale (left; colors show individual ICs) and Jaccard index matrix showing matching (high value) and nonmatching (low value) ICs between a time segment and template ICA map (circles). C) Examples of overlap between matching and nonmatching pairs of template and time segment ICs for circles shown in the Jaccard matrix of B (overlap shown in dark red; individual ICs blue or yellow). D) Three examples of template matching ICs (Jaccard index ≥0.5) for each of the four timescales (columns/green bars). Scale bars: 1 mm.

Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

Techniques: Imaging

Journal: Cerebral Cortex (New York, NY)

Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

doi: 10.1093/cercor/bhac523

Figure Lengend Snippet: Spatial ICA of wide-field Ca 2+ imaging data produces spatially independent brain regions. A) Schematic showing the ICA workflow of concatenating data for each animal chronologically across days and trials (days are signified by different colored borders; trials are signified by overlapping images) and sending the combined dataset through the JADE ICA algorithm. B) Example template map (ground-truth to which all other ICA solutions are compared) of spatial ICs produced from running ICA on one mouse’s combined dataset (each different colored region is a single IC; scale bar: 1 mm). White lines denote major regions of the Allen Common Coordinate Framework (CCF; see ). C) Example matrix of Jaccard indices comparing the template map to itself (low off-diagonal Jaccard indices indicate good spatial separation; zero values are shown as white indicating no IC overlap). D) Frequency histograms showing the distribution of off-diagonal Jaccard indices (nonself matches) when comparing the template map to itself for each animal (bin-widths = 0.05).

Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

Techniques: Imaging, Produced

Journal: Cerebral Cortex (New York, NY)

Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

doi: 10.1093/cercor/bhac523

Figure Lengend Snippet: Cortex-wide maps cover similar areas across timescales. A) Brain maps from all six experimental subjects showing cumulative cortical coverage of template matching ICs across all time-windows within each of the four timescales examined (color scale shows the number of timescales where an area of cortex was covered by a spatial IC). Scale bar: 1 mm. B) Jaccard index matrices for each of the six experimental subjects showing a high degree of overlapping cortical coverage between timescales (off-diagonal comparisons).

Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

Techniques: