Create shiny cluster modules for renamed datasets

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Create shiny cluster modules for renamed datasets

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Last edited Apr 24, 2018

Created on Apr 24, 2018

This example demonstrates how to process and store single-cell RNA sequencing data across multiple developmental time points, creating Shiny-ready cluster modules for each stage. The code defines an S4 class, `clustering_module`, that bundles t-SNE embeddings, normalized expression data, metadata, and a marker list for each time point from E14.5 to P7. It loads Seurat objects, extracts the t-SNE coordinates and cell identities, filters markers by expression difference using `dplyr`’s `top_n`, and saves each time point as a compressed RDS file. The workflow uses `Seurat` for data handling and `tidyr` for reshaping, preparing interactive visualizations for the Shiny server.

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