Single-cell transcriptomics measures the activity of thousands of genes in individual cells, producing extremely high-dimensional datasets. To interpret these data, researchers typically project them into two or three dimensions for visualization. Widely used methods such as t-SNE and UMAP can reveal local structure but may distort global relationships or depend heavily on parameter choices.<\/p>\n\n\n\n
Led by Dmitry Kobak at the University of T\u00fcbingen, the project \u201cContrastive learning for dimensionality reduction and visualization of transcriptomic data\u201d<\/strong> (2021\u20132025) investigated whether contrastive learning\u2014a self-supervised machine-learning approach\u2014could improve dimensionality reduction and visualization for single-cell and other transcriptomic datasets. <\/p>\n\n\n\n Dimensionality reduction and visualization are central to analysing modern transcriptomic datasets. By grounding popular methods like t-SNE and UMAP in a unified contrastive-learning framework, the project helps researchers understand what these visualizations represent and how they depend on algorithmic choices. The resulting methods and guidelines can support more reliable interpretation of single-cell data in neuroscience, immunology, developmental biology, and related fields. <\/p>\n\n\n\nKey results<\/h2>\n\n\n\n
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Why it matters<\/h2>\n\n\n\n
This project produced the following peer-reviewed publications:\u00a0<\/h2>\n\n\n\n
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