Extract shiny clustering data for full dataset

Create .Rdata file from Seurat object for clustering module of shiny server (for .Rdata)

This example demonstrates how to convert a Seurat object into an .Rdata file for use in a clustering module of a Shiny server. The script extracts normalized expression data as a dense matrix, along with t-SNE embeddings and cell cluster identities, and merges them into a single data frame. It also includes the full metadata associated with each cell. The code uses the Seurat and dplyr APIs, specifically accessing the `@data`, `@dr$tsne@cell.embeddings`, and `@ident` slots, and saves the resulting objects with the base R `save` function.

85% match

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

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.

Create an Rds file for the full heart maturation dataset

This example demonstrates how to convert a Seurat single-cell RNA sequencing object into a compact RDS file for a Shiny visualization app. It shows the creation of a custom S4 class that bundles tSNE coordinates, normalized expression data, cell metadata, and the top 50 differentially expressed markers per cluster. Using the dplyr and tidyr libraries for data manipulation, the script extracts the tSNE embeddings and cluster identities from the Seurat object, then merges them with metadata. The resulting clustering_module object is saved via saveRDS, enabling efficient downstream interactive exploration of heart maturation data.

Create shiny cluster modules for renamed datasets

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.

Create .Rds object with components needed for clustering module of heartmat shiny server (for .Rds)

This R script builds a clustering module object for the Shiny server by processing a Seurat object, creating a data frame that combines tSNE coordinates with cluster identities. It demonstrates data preparation for interactive visualization by extracting normalized expression data, metadata, and a marker list filtered to the top 20 markers per cluster by average log fold change. The code uses the dplyr and tidyr libraries for data manipulation, and relies on Seurat’s slots for tSNE embeddings, expression matrices, and metadata, ultimately saving the assembled S4 object as an .Rds file for downstream Shiny app use.

Create clustering module for shiny server for all timepoints

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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Create a cellview Rds object from a seurat expression object (updated for Seurat version 2) [MOUSE]

This example demonstrates how to prepare a Seurat single-cell expression object for use with the CellView visualization tool, specifically for mouse data. It shows the workflow of rerunning t-SNE with three dimensions, converting gene symbols to Ensembl IDs using `bitr` from clusterProfiler, and constructing the necessary `log2cpm`, `featuredata`, and `tsne.data` matrices. The code uses Seurat’s `RunTSNE`, the `Matrix` and `dplyr` packages for data manipulation, and ultimately saves the combined object as an Rds file for downstream visualization.

70% match

Make a nice ClusterTree from the initial ClusterTree plot from Seurat

This example shows a cluster tree visualization derived from a Seurat single-cell analysis, displaying the hierarchical relationships between cell clusters as a phylogenetic-style tree. The plot renders a ladderized dendrogram where nodes are labeled with grey circles, making the branching structure easier to interpret than the default Seurat output. The code uses Seurat’s `BuildClusterTree` function to compute the tree, then plots it with base R’s `plot` and `nodelabels` functions. The visualization applies `ladderize` to reorder branches for clarity and sets custom graphical parameters like `edge.width` and `label.offset` to improve readability. The data source is the Seurat object’s cluster tree, saved as a PNG file.