17 Solutions

17.1 Discussion: The Seurat Object in R

## To look at our seurat object
str(pbmc)

## To access the meta.data slot
pbmc@meta.data

## meta.data contains cell metadata identified by cell barcode, currently there is nFeatures and nCounts

## the actual count data can be found by which is what we had in `pbmc.data` lots of accessors here!

pbmc@assays$RNA@counts
# or
pbmc$RNA@counts
# or
pbmc[["RNA"]]@counts

## this is the data object in pbmc.data but is now stored within the seurat object
pbmc@assays$RNA@counts[c("CD3D","TCL1A","MS4A1"), 1:30]

# The PBMC dataset is a gene-expression dataset and is stored in an assay called `RNA`. What other types of assays could we have stored in a Seurat object if we had a different type of dataset?
# We could have a ATAC assay if we had done single cell ATAC
# We could have a HTO assay if we'd done cell hashtagging
# We could have a CSP assay if we captured cell surface protein data

17.2 Challenge: The meta.data slot in the Seurat object

# Show QC metrics for the first 5 cells
head(pbmc@meta.data, 5)

17.3 Challenge: Ribosomal gene expression as a QC metric

pbmc$percent.riboL <- PercentageFeatureSet(pbmc, pattern = "^RPL")
pbmc$percent.riboS <- PercentageFeatureSet(pbmc, pattern = "^RPS")

plot1 <- FeatureScatter(pbmc, feature1 = "percent.riboS", feature2 = "percent.riboL")
plot1

The large and small ribosomal subunit genes are correlated within cell.

What about with mitochondria and gene, feature counts?

plot2 <- FeatureScatter(pbmc, feature1 = "percent.riboL", feature2 = "percent.mt")
plot2

There are cells with low ribosome and low mitochondrial gene percentages, and some outliers too (low ribo, high mt).

These are the cells you may want to exclude.

To highlight cells with very low percentage of ribosomal genes, create a new column in the meta.data table and with FeatureScatter make a plot of the RNA count and mitochondrial percentage with the cells with very low ribosomal gene perentage.

pbmc$lowRiboL <- pbmc$percent.riboL <= 5
plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "lowRiboL")
plot1

17.4 Challenge: Labelling Genes of Interest

# earlier we created a variable genes plot
plot1 <- VariableFeaturePlot(pbmc)

# create a vector of genes of interest
goi <- c("IL8", "IDH2", "CXCL3")

# now we add the labels we want
plot3 <- LabelPoints(plot = plot1, points = goi, repel = TRUE)

plot3

17.5 Challenge: Try different cluster settings

Setting the resolution to 0.05 produces less clusters:

pbmc2 <- FindClusters(pbmc, resolution = 0.05)
DimPlot(pbmc2, reduction = 'pca', dims=c(1,2))
DimPlot(pbmc2, reduction = 'umap')

Using only the first two principal components, the clusters look muddled in the UMAP:

pbmc3 <- FindNeighbors(pbmc, dims = 1:2)
pbmc3 <- FindClusters(pbmc3, resolution = 0.5)
DimPlot(pbmc3, reduction = 'pca', dims=c(1,2))
DimPlot(pbmc3, reduction = 'umap')

17.6 Challenge: Reference Based Annotation

See if you can annotate the data with the fine labels from the Monoco reference dataset and whether it improves the cell type annotation resolution.

monaco <- celldex::MonacoImmuneData()
monaco.pred.fine <- SingleR::SingleR(
  test = sce, ref = monaco, labels = monaco$label.fine)

pbmc$monaco_fine <- monaco.pred.fine$labels
DimPlot(pbmc, reduction='umap', group.by='monaco_fine')

• The T cell & monocyte groups will have more specific cell groups. Discuss the difference between labelling an entire cluster and individual annotating cells - do you believe that every cell in a cluster belongs to the group or do you believe that you have a mix of cell types?

Do you lose any groups?

• The platelet cells that were previously identified are no longer being annotated as platelets. The Monoco reference doesn’t contain any information about platelets and so SingleR doesn’t label them as such. This is a weakness of reference based methods - they are only as good as your reference