Seurat, Your Single Cell RNA-seq data Analyst-Single-Cell Analysis Tool

Example

Filtering out cells with low gene expression or high mitochondrial content to improve data quality.

Scenario

Before analyzing a new dataset, a researcher uses Seurat to remove cells that are likely to be dead or damaged, ensuring that downstream analyses are based on healthy, transcriptionally active cells.

Example

Harmonizing datasets from different sources to remove batch effects, followed by normalization to account for differences in sequencing depth.

Scenario

A team of researchers combines scRNA-seq datasets from multiple studies to analyze the immune response across different diseases, using Seurat to ensure comparability between datasets.

Example

Using unsupervised clustering algorithms to group cells based on gene expression similarities and annotating clusters with known marker genes to identify cell types.

Scenario

In an effort to discover new cell types in the human brain, scientists employ Seurat to cluster cells and identify unique transcriptional profiles that indicate previously unrecognized neuronal subtypes.

Example

Comparing gene expression levels between clusters or conditions to identify genes that characterize cell populations or responses to stimuli.

Scenario

Investigating the effect of a drug on cancer cells, a researcher uses Seurat to pinpoint genes that are differentially expressed in treated versus untreated cells, highlighting potential therapeutic targets.

Example

Creating plots such as feature plots, violin plots, and UMAP or t-SNE plots for exploratory data analysis and presentation of results.

Scenario

To visualize the cellular diversity within a sample, a user generates UMAP plots with Seurat, revealing distinct cell populations and their gene expression patterns.