Skip to main content
SimilarExport ZIP
100%

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

✓ Published0🌍 Public
FFloWuenne
Last edited Jun 27, 2017
Created on Jun 27, 2017

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.

AI-generated description

Similar vizzes

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
76% 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
61% match
Loading thumbnail…

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.

FFloWuenne
60% match
Loading thumbnail…

Make a table of the number of cells for each group in the seurat object

The example builds a data frame of cell counts for each cluster in a Seurat object, broken down by treatment group. It first shows raw cell numbers per group per cluster, then adds normalized proportions relative to each group’s total cell count, making group compositions comparable despite unequal sample sizes. The code uses `Seurat` functions like `SubsetData` and the `@ident` and `@meta.data` slots, alongside `dplyr`’s `select` and `nrow` to count group memberships. It iterates over clusters, appending rows via `rbind`, and finally writes the result to a tab-separated file with `write.table`. The output table directly reveals per-cluster group distributions and their normalized percentages, aiding differential abundance analysis.

FFloWuenne
58% match
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
57% match
Loading thumbnail…

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.

FFloWuenne
57% match
Loading thumbnail…

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.

FFloWuenne
56% match
Loading thumbnail…

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.

FFloWuenne
55% match