( Lower) The same image after discretization (clustering) and Canny edge detection-based demarcation of cluster boundaries. ( Upper) The molecularly distinct regions found by t-SNE are separated in the t-SNE space, yielding transitional boundaries in the image that can be highlighted using the Canny edge detector. ( C) Illustration of the discretization process of the spatially mapped t-SNE. ( B) In the t-SNE image, each pixel is colored according to its location in the 3D t-SNE space using L*a*b* color coordinates, revealing a patchwork of subpopulations throughout the tumors. ( A) The t-SNE scatterplot reveals clear structural separations based on molecular heterogeneity. Nonlinear clustering of tumor cell-specific MSI data from 63 patients with gastric cancer. ( 10) introduced t-SNE to the MSI field, demonstrating its superiority over linear multivariate methods for demarcating regions of tissues with different mass spectral signatures.įig. Ji ( 9) used it to study the relationship between gene expression and neuroanatomy in the developing mouse brain, demonstrating that the developmental neuroanatomy is preserved in transcriptome data. ( 8) used it to visualize the spatial organization of gene expression across the mammalian brain. t-SNE has been applied to high-dimensionality imaging data and has been shown to outperform other dimensionality-reduction techniques in several life-science applications. A technique known as t-distributed stochastic neighbor embedding (t-SNE) has rapidly established itself as a method of choice for summarizing high-dimensionality datasets owing to its ability to overcome the “crowding problem,” in which some of the higher-dimensional data similarities cannot be faithfully represented in a single map ( 7). Nonlinear multivariate methods can preserve both local detail and the global data structure in a lower-dimensional representation by emphasizing similarities between data points.
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