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Single-cell Analysis Assesment. Alperen Uysal, MD, PhDc. Ege University, Izmir, Turkey.

3. Cell Type Annotation:

library(SeuratDisk)
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library(Seurat)
## Loading required package: SeuratObject

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library(SingleR)
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With the code below, the seurat object, which was saved in the dimension reduction session, was loaded to the R environment.

# load the data
seurat.obj <- LoadH5Seurat("../output/reducted.h5Seurat")
## Validating h5Seurat file

## Initializing RNA with data

## Adding counts for RNA

## Adding scale.data for RNA

## Adding feature-level metadata for RNA

## Adding variable feature information for RNA

## Adding reduction pca

## Adding cell embeddings for pca

## Adding feature loadings for pca

## Adding miscellaneous information for pca

## Adding reduction umap

## Adding cell embeddings for umap

## Adding miscellaneous information for umap

## Adding graph RNA_nn

## Adding graph RNA_snn

## Adding command information

## Adding cell-level metadata

## Adding miscellaneous information

## Adding tool-specific results

SingleR Usage

To annotate single-cell RNA sequencing data with predicted cell types, SingleR package was used. SingleR is a reference-based method that labels single cells by comparing their expression profiles to a reference dataset of known cell types.

The following steps were performed:

Reference Data: The HumanPrimaryCellAtlasData function from the celldex package was used to load a reference dataset containing cell type annotations from primary human cells. This dataset provides the label.main column for high-level cell type labels.

ref <- celldex::HumanPrimaryCellAtlasData()
## see ?celldex and browseVignettes('celldex') for documentation

## loading from cache

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Converting Seurat Object: Our single-cell data, stored in a Seurat object (seurat.obj), was converted into a format compatible with SingleR using the as.SingleCellExperiment function.

Annotation: The SingleR function was employed to classify the cells in our dataset. It compared the gene expression profiles of our test dataset against the reference and assigned the most likely cell type label from the reference.

results <- SingleR(test = as.SingleCellExperiment(seurat.obj),
                      ref = ref,
                      labels = ref$label.main)

This approach leverages the curated knowledge in the Human Primary Cell Atlas to achieve high-confidence cell type annotations, enabling downstream biological analyses.

For more details, see the SingleR documentation.

After annotating cells with SingleR, we integrated the results into the Seurat object and visualized them using UMAP:

Adding Annotations: The cell type labels from SingleR (results$labels) were added to the Seurat object as a new metadata column, annotation_labels.

seurat.obj$annotation_labels <- results$labels

The DimPlot function was used to create a UMAP plot, grouping cells by their annotations. Labels were added directly to the plot for clarity.

DimPlot(seurat.obj,
        reduction = "umap",
        group.by = "annotation_labels",
        label = T)

This plot shows the distribution of annotated cell types across clusters.

The final seurat object was saved as annotated.rds to use in next steps.

saveRDS(seurat.obj, file = "../output/annotated.rds")