[{"data":1,"prerenderedAt":27},["ShallowReactive",2],{"resource-dimensionality-reduction":3},{"slug":4,"type":5,"title":6,"date":7,"state":8,"image":9,"video":10,"searchWords":11,"excerpt":22,"keyPoints":23,"content":26},"dimensionality-reduction","courses","[14] Dimensionality Reduction","2026-01-14","hidden","https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1517694712202-14dd9538aa97?w=800&q=80","\u002Fcourses\u002FInstructional Videos\u002F[14] Dimensionality reduction.mp4",[12,13,14,15,16,17,18,19,20,21],"dimensionality reduction","umap","tsne","tutorial","course","mantaplex","visualization","clustering","high dimensional","analysis","Visualize high-dimensional cell data using dimensionality reduction techniques like UMAP and t-SNE.",[24,25],"Generate UMAP of single-cell data","Export UMAP coordinates","\n      \u003Cp>Dimensionality reduction helps reveal patterns in complex, high-dimensional single-cell data.\u003C\u002Fp>\n      \u003Ch2>Reduction Algorithms\u003C\u002Fh2>\n      \u003Cp>Learn when to use UMAP vs t-SNE for different types of data exploration.\u003C\u002Fp>\n      \u003Ch2>Visualization\u003C\u002Fh2>\n      \u003Cp>Create interactive 2D plots that reveal cell population structure and relationships.\u003C\u002Fp>\n      \u003Ch2>Interpretation\u003C\u002Fh2>\n      \u003Cp>Understand how to interpret dimensionality reduction plots and identify meaningful clusters.\u003C\u002Fp>\n    ",1782808299120]