Cell cycle assignment for single cell data using scran and cyclone with gene symbols instead of ensembl IDs

Cell cycle assignment for single cells (using Dropbead), starting from a Seurat object and adding cell cycle to Metadata of Seurat object

This example assigns cell cycle phases to single cells and adds the result as metadata to a Seurat object. It shows each cell’s dominant cell cycle phase (e.g., G1, S, G2/M) based on per-cell scores, with the phase label stored as new metadata. The code uses the Dropbead package’s `assignCellCyclesPhases` function, which processes an expression matrix within a `SingleSpeciesSample` object. It then selects the highest-scoring phase per cell via `max.col` and attaches the resulting data frame to the Seurat object using `AddMetaData`. The pipeline relies on a supplied Excel file of cell cycle genes.

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Translate human to mouse gene symbols for cell cycling genes using biomart and save results for faster reading next time

This example demonstrates a workflow for converting human gene symbols to mouse orthologs for cell cycle genes, saving the results for faster future access. It shows the conversion of two gene lists—S-phase and G2/M-phase markers—from the Seurat `cc.genes` dataset into mouse equivalents. The code uses the `biomaRt` package to query Ensembl databases, specifically calling `useMart` for human and mouse datasets and `getLDS` to map HGNC symbols to MGI symbols. After conversion, the results are written to tab-separated text files for reuse, avoiding repeated live queries.

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Creating quick SCE object from Seurat expression data

This example demonstrates how to convert a Seurat expression dataset into a SingleCellExperiment (SCE) object for use with scater’s analytical and plotting functions. It shows the creation of an `AnnotatedDataFrame` from Seurat’s metadata, followed by constructing a new SCE object via `newSCESet()` with expression data from `expression_seurat@data` and quality-control metrics calculated using `calculateQCMetrics()`. The code relies on the scater and Seurat libraries, along with the `AnnotatedDataFrame` class from Bioconductor, to bridge the two single-cell analysis frameworks.

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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 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.

Use Seurat to find specific markers of each cell type (modified from Dan Skelly)

This example shows a dot plot of the top five specific marker genes for each cell type in a single-cell RNA-sequencing dataset, with dot size and color encoding expression level and percentage of cells expressing each gene. The visualization relies on Seurat’s `FindMarkers` function with a ROC test to perform all pairwise comparisons between cell types, filtering for markers expressed in fewer than 50% of cells in other clusters and retaining those with a mean AUC above 0.65. The code uses tidyverse functions like `mutate`, `filter`, `summarize`, and `group_by` to process the marker data, and Seurat’s `DotPlot` to render the final figure. Marker lists are written to TSV files, and the plot is saved as a PNG.

Use Seurat to find specific markers of each cell type

This example demonstrates how to identify specific marker genes for each cell type in a Seurat single-cell RNA-seq analysis. It shows a workflow that compares every cell type against all others using the `FindMarkers` function with a ROC test, then filters the results to retain only markers expressed in fewer than 50% of cells in all other populations. The code uses the `tidyverse` and `Seurat` libraries to perform pairwise comparisons, calculate the difference in expression percentages, and summarize the average AUC values. The final output is a curated list of the top 10 markers per cell type, emphasizing those with high discriminatory power.

Extract shiny clustering data for full dataset

This example prepares a clustered single-cell dataset for interactive visualization by extracting tSNE coordinates, cluster identities, normalized expression, and marker genes from a Seurat object. It shows how the full dataset’s clustering structure is assembled into a custom S4 object for downstream use. The code uses R with `dplyr` and `tidyr` to filter the top 50 markers per cluster by `avg_logFC`, and merges tSNE embeddings with cell identities. It saves the processed data as an RDS file for a Shiny app, relying on Seurat’s data structures and base R matrix operations.