Seurat Specialist-AI-powered Seurat data analysis.

Example

The user has a raw scRNA-seq dataset and needs to filter out low-quality cells based on mitochondrial gene expression or read count. Seurat Specialist provides R code for filtering and normalizing the dataset using Seurat functions such as `subset()` and `NormalizeData()`.

Scenario

A researcher has just sequenced a large scRNA-seq dataset but finds that some cells have extremely low quality due to high mitochondrial gene expression. Seurat Specialist assists them by explaining how to remove these cells, normalize the dataset, and scale the data, improving downstream analysis results.

Example

Seurat Specialist offers guidance on running Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and clustering algorithms such as Louvain or Leiden, with specific code snippets like `RunPCA()`, `RunUMAP()`, and `FindClusters()`.

Scenario

A bioinformatics team is working to visualize and group single-cell data based on gene expression patterns. Seurat Specialist assists them by showing how to perform dimensionality reduction and cluster the cells, explaining key parameters that impact resolution and clustering accuracy.

Example

Using functions like `FindMarkers()` or `FindAllMarkers()`, Seurat Specialist assists in identifying genes that are differentially expressed between clusters, guiding users through the interpretation of results, including volcano plots or heatmaps.

Scenario

A biologist seeks to understand which genes are differentially expressed between distinct cell populations in their dataset. Seurat Specialist provides a step-by-step guide on running differential expression tests, explains how to filter meaningful markers, and demonstrates how to visualize the results.