3 Coarse-graining of large single-cell data into metacells using SuperCell

Here, we use the pbmc4k dataset as presented in Batch correction.

3.1 RunSuperCell

The RunSuperCell() function is a wrapper function to run SuperCell.

sce_sc <- RunSuperCell(pbmc4k, gamma = 5)
sce_sc

## class: SingleCellExperiment 
## dim: 33694 746 
## metadata(3): hvgmethod hvgcols SuperCell
## assays(2): counts logcounts
## rownames(33694): RP11-34P13.3 FAM138A ... AC213203.1
##   FAM231B
## rowData names(9): ENSEMBL_ID Symbol_TENx ... p.value
##   FDR
## colnames: NULL
## colData names(1): size
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

The output of RunSuperCell() is a SingleCellExperiment object that stores gene expression matrix of metacells.

We can use it for downstram analysis.

post_process <- function(sce) {
    sce <- NormalizeData(sce)
    sce <- FindVariableFeatures(sce)
    sce <- ScaleData(sce)
    sce <- runPCA(sce, subset_row = VariableFeatures(sce), exprs_values = "scaled")
    
    sce <- FindNeighbors(sce, dims = 1:10)
    sce <- FindClusters(sce)
    sce <- RunUMAP(sce)
    return(sce)
}

sce_sc <- post_process(sce_sc)

library(ggsc)
sc_dim(sce_sc, reduction="UMAP") + sc_dim_geom_label()

3.2 Estimate SuperCell purity

pbmc4k2 <- post_process(pbmc4k)

SC <- metadata(sce_sc)$SuperCell

purity <- SuperCell::supercell_purity(pbmc4k2$label, SC$membership, method = 'entropy')

head(purity)

## 1 2 3 4 5 6 
## 0 0 0 0 0 0

summary(purity)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.00000 0.00000 0.03568 0.00000 0.69315