Creating quick SCE object from Seurat expression data

100%

Creating quick SCE object from Seurat expression data

✓ Published0🌍 Public

Last edited Jul 31, 2017

Created on Jul 5, 2017

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.

AI-generated description

Similar vizzes

Loading thumbnail…

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.

FFloWuenne

64% match

Loading thumbnail…

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

This example demonstrates how to assign cell cycle phases to single-cell RNA sequencing data by converting the pretrained marker gene pairs in the scran package from Ensembl IDs to gene symbols. It shows the resulting phase assignments being added as metadata to a Seurat object for downstream analysis. The code uses the scran cyclone function, the org.Hs.eg.db database for ID conversion via bitr, and dplyr for data manipulation. The visualization implicitly relies on the Seurat object’s structure to store the new cell cycle labels, though no plot is generated in the provided script.

FFloWuenne

62% match

Loading thumbnail…

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.

FFloWuenne

61% match

Loading thumbnail…

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.

FFloWuenne

60% match

Loading thumbnail…

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.

FFloWuenne

60% match

Loading thumbnail…

Plot mean expression of a gene vs other parameters and highlight specific genes using ggrepel

This example uses the scater package to calculate quality metrics for single-cell RNA-seq data, then plots mean gene expression against percent dropout and percent total expression. The ggplot2 code highlights a user-defined set of genes of interest in red, while ggrepel’s `geom_text_repel` labels those genes to avoid overlapping points. Two separate scatterplots are generated from the same `gene_info` data frame, each saved as an SVG, showing how specific genes relate to global expression distributions. The analysis relies on the `newSCESet` and `calculateQCMetrics` functions from scater to derive the plotted metrics.

FFloWuenne

59% match

Loading thumbnail…

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.

FFloWuenne

57% match

Loading thumbnail…

Save raw UMI counts and metadata for each object

This example saves raw UMI counts and metadata for each timepoint in a single-cell heart maturation dataset. It iterates over six developmental stages, reading a Seurat object for each and extracting the raw UMI matrix and metadata. The code uses base R functions `as.matrix()` and `readRDS()`, then writes tab-separated files with `write.table()`. The visualization shows how raw count matrices are paired with their corresponding cell annotations, facilitating downstream differential expression analysis. The approach relies on Seurat’s stored raw data and metadata slots, producing separate DGE and metadata files per timepoint.

FFloWuenne

57% match