8.3 Add cell type annotations to SparseExpressionSet object
Based on the supervised and unsupervised methods, we have annotated the cell types for each cluster. To add the cell type annotation information into the sparse eset object:
celltype_map <- c(`1`="CD4TN", `2`="CD4TCM", `3`="CD8TN", `4`="NK", `5`="B", `6`="Monocyte", `7`="CD4Treg")
pbmc14k_log2cpm.eset$cell_type <- as.character(celltype_map[pbmc14k_log2cpm.eset$clusterID])
head(pData(pbmc14k_log2cpm.eset))## trueLabel_full trueLabel projectID nUMI nFeature pctMito
## CACTTTGACGCAAT CD14+ Monocyte Monocyte PBMC14k 764 354 0.01832461
## GTTACGGAAACGAA CD14+ Monocyte Monocyte PBMC14k 956 442 0.01569038
## CACTTATGAGTCGT CD14+ Monocyte Monocyte PBMC14k 629 323 0.02066773
## GCATGTGATTCTGT CD14+ Monocyte Monocyte PBMC14k 875 427 0.02628571
## TAGAATACGTATCG CD14+ Monocyte Monocyte PBMC14k 1060 445 0.03207547
## CAAGAAGACCCTCA CD14+ Monocyte Monocyte PBMC14k 849 384 0.01531213
## pctSpikeIn CellID UMAP_1 UMAP_2 clusterID cell_type
## CACTTTGACGCAAT 0 CACTTTGACGCAAT 14.91650 13.04096 6 Monocyte
## GTTACGGAAACGAA 0 GTTACGGAAACGAA 14.57031 10.27093 6 Monocyte
## CACTTATGAGTCGT 0 CACTTATGAGTCGT 14.28869 13.61674 6 Monocyte
## GCATGTGATTCTGT 0 GCATGTGATTCTGT 14.12546 13.36319 6 Monocyte
## TAGAATACGTATCG 0 TAGAATACGTATCG 14.91227 11.19407 6 Monocyte
## CAAGAAGACCCTCA 0 CAAGAAGACCCTCA 15.34154 12.25821 6 MonocyteThe draw_barplot() function can visualize the cell composition of self-defined groups. We can use it to show the purity of MICA clusters:
## Violin plot of marker genes across clusters
draw_barplot(input_eset = pbmc14k_log2cpm.eset, group_by = "cell_type", color_by = "trueLabel_full", xlabel.angle = 45)
Don’t forget to save the SparseEset object after the cell type annotation added.