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Extract data for clustering module from all time points (For .Rdata)

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FFloWuenne
Last edited Apr 17, 2018
Created on Apr 16, 2018

This example demonstrates a data-extraction workflow that prepares Seurat clustering results from multiple developmental time points for a Shiny server application. It loads seven Seurat objects (E14.5 through P6) and, for each, extracts normalized expression matrices, t-SNE embeddings, cluster identities, and metadata. The script explicitly uses the `dplyr` and `tidyr` packages for data manipulation, converting sparse expression matrices to dense data frames and merging cell embeddings with identity assignments. The resulting data frames are saved as `.RData` files per time point, preserving the structure needed for interactive clustering visualization. The code relies on Seurat’s slot accessors (`@data`, `@dr$tsne`, `@ident`, `@meta.data`) to gather all relevant components.

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