Clustering and Annotation with Seurat

This vignette demonstrates how to perform clustering on a pre-processed Seurat object and then annotate the resulting clusters using CASSIA. We'll assume quality control steps have already been completed.

1. Installation and Setup

1.1 Required Packages

# Install required packages
install.packages(c("Seurat", "dplyr", "reticulate", "devtools"))

# Install CASSIA from GitHub
library(devtools)
devtools::install_github("ElliotXie/CASSIA/CASSIA_R")

# Load packages
library(Seurat)
library(dplyr)
library(CASSIA)

# Set up API key (needed for CASSIA)
setLLMApiKey("your_api_key", provider = "openrouter", persist = TRUE)  # or use anthropic/openai
R

2. Exploring Your Pre-processed Seurat Object

In this tutorial, we'll use a breast tissue dataset from the GTEX project as an example. This dataset provides a comprehensive reference for breast tissue cell types and can be downloaded from:

GTEX Breast Tissue Dataset

# Load the GTEX breast dataset
gtex_data <- readRDS("gtex_ref.rds")

# Take a look at the dataset metadata
gtex_data@meta.data %>% colnames()
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[1] "orig.ident" "nCount_RNA" "nFeature_RNA" "Broad.cell.type" "Granular.cell.type"

We can see that the dataset includes gold standard cell type labels in the Broad.cell.type and Granular.cell.type columns.

3. Dimensional Reduction and Clustering

3.1 PCA and UMAP

gtex_data <- NormalizeData(object = gtex_data)
gtex_data <- FindVariableFeatures(object = gtex_data)
gtex_data <- ScaleData(object = gtex_data)
gtex_data <- RunPCA(object = gtex_data)
gtex_data <- FindNeighbors(object = gtex_data, dims = 1:25)     # Use 25 PCs for clustering
gtex_data <- FindClusters(object = gtex_data,resolution = 0.4)  # Use resolution 0.4 for clustering
gtex_data <- RunUMAP(object = gtex_data, dims = 1:25)           # Use 25 PCs for UMAP
DimPlot(object = gtex_data, reduction = "umap")                # Visualize the UMAP
R

3.3 Finding Marker Genes

Idents(gtex_data)="RNA_snn_res.0.4" # This is the default cluster name if you use the default resolution of 0.4


all_markers <- FindAllMarkers(gtex_data, 
                              only.pos = TRUE) 

R

The marker filter is implemented internally in CASSIA, and the raw marker file is needed for the annotation boost agent too; thus, no filter is needed at this step.

4. Annotating Clusters with CASSIA

4.1 Basic Annotation

Now that we have our clusters and marker genes, we can use CASSIA for annotation:

results <- runCASSIA_pipeline(
    output_file_name = "gtex_breast_annotation",
    tissue = "Breast",
    species = "Human",
    marker = all_markers,
    max_workers = 6  # Depends on your CPU cores
)

R

The default provider is OpenRouter, and the default models are selected to optimize for annotation quality. To see how each new model performs, visit our benchmark website:

sc-llm-benchmark.com/methods/cassia

The output file is saved in a folder named after the tissue and species. Inside the folder, you will see the following files:

• gtex_breast_annotation_summary.csv: The summary of the annotation results
• gtex_breast_annotation_full.csv: The full annotation results with full conversation history and merged clusters at different levels
• gtex_breast_annotation_scored.csv: The scored annotation results
• gtex_breast_annotation_report.html: Genearted report of the annotation results, contains the router to all the clusters reports.

The gtex_breast_annotation_scored.csv file:

4.2 Incorporating Annotations into Seurat Object

After running CASSIA, you can integrate the annotations back into your Seurat object:

seurat_breast=add_cassia_to_seurat(
    seurat_obj = gtex_data, 
    cassia_results_path = "gtex_breast_annotation_scored.csv", # the path to the CASSIA results csv file
    cluster_col = "RNA_snn_res.0.4", # the column name of the cluster column used for Findallmarkers in the seurat object
    columns_to_include = "1") # default is to export only the merged column back to seurat, set to "2" if you want to export all the columns
R

4.3 Visualizing the Annotations

DimPlot(seurat_breast,group.by = "Broad.cell.type", label = TRUE, repel = TRUE)
DimPlot(seurat_breast,group.by = "Granular.cell.type", label = TRUE, repel = TRUE)
DimPlot(seurat_breast,group.by = "RNA_snn_res.0.4", label = TRUE, repel = TRUE)
DimPlot(seurat_breast,group.by = "CASSIA_merged_grouping_1", label = TRUE, repel = TRUE)
DimPlot(seurat_breast,group.by = "CASSIA_merged_grouping_2", label = TRUE, repel = TRUE)
DimPlot(seurat_breast,group.by = "CASSIA_merged_grouping_3", label = TRUE, repel = TRUE)

R

CASSIA will automatically summarize the annotation into three different detailed levels, from the most general to the most detailed, as shown in the UMAPs.

5. Next Steps

After completing clustering and annotation:

• Subset the major clusters and repeat the steps
• Use runCASSIA_annotationboost() for clusters with low annotation quality
• Try compareCelltypes() to distinguish between similar cell types
• Explore runCASSIA_subclusters() for more detailed analysis of specific populations
• Implement runCASSIA_batch_n_times() for uncertainty quantification

See the "Full Workflow Best Practices" vignette for more comprehensive quality control and analysis techniques.