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Create clustering module for shiny server for all timepoints

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FFloWuenne
Last edited Apr 20, 2018
Created on Apr 17, 2018

This example builds a clustering module for a Shiny server across multiple developmental time points. It processes Seurat objects to extract t-SNE embeddings, cluster identities, normalized expression data, and metadata, then combines them into a custom S4 object via the `setClass` function. The code filters marker lists by the top 50 markers per cluster using `dplyr`’s `top_n` on pct difference. Using base R matrix conversions and `merge`, it consolidates cell embeddings with identities, saving each time point as an RDS file for later visualization.

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