Skip to contents

Calculate cell type scores and assign cell types

calculate-cell-type-scores-and-assign-cell-types.Rmd
library(hitype)
#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
#> which was just loaded, were retired in October 2023.
#> Please refer to R-spatial evolution reports for details, especially
#> https://r-spatial.org/r/2023/05/15/evolution4.html.
#> It may be desirable to make the sf package available;
#> package maintainers should consider adding sf to Suggests:.

Using RunHitype on Seurat object

Prepare the seurat object:

See also https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#setup-the-seurat-object

suppressWarnings(SeuratData::InstallData("pbmc3k"))
pbmc <- pbmc3k.SeuratData::pbmc3k
pbmc[["percent.mt"]] <- Seurat::PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- Seurat::NormalizeData(pbmc)
pbmc <- Seurat::FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
pbmc <- Seurat::ScaleData(pbmc, features = rownames(pbmc))
pbmc <- Seurat::RunPCA(pbmc, features = Seurat::VariableFeatures(object = pbmc))
pbmc <- Seurat::FindNeighbors(pbmc, dims = 1:10)
pbmc <- Seurat::FindClusters(pbmc, resolution = 0.5)
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 2638
#> Number of edges: 95927
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8728
#> Number of communities: 9
#> Elapsed time: 0 seconds
pbmc <- Seurat::RunUMAP(pbmc, dims = 1:10)
library(hitype)

# Load gene sets
gs <- gs_prepare(hitypedb_pbmc3k)

# Assign cell types
obj <- RunHitype(pbmc, gs)

Seurat::DimPlot(obj, group.by = "hitype", label = TRUE, label.box = TRUE) +
  Seurat::NoLegend()

Compared to the manual marked cell types:

Seurat manual marked cell types

See also https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#assigning-cell-type-identity-to-clusters

Using hitype_score and hitype_assign on Seurat directly 

scores <- hitype_score(pbmc@assays$RNA@scale.data, gs, scaled = TRUE)
cell_types <- hitype_assign(pbmc$seurat_clusters, scores, gs)
summary(cell_types)
#> # A tibble: 9 × 4
#>   Level Cluster CellType     Score
#>   <int> <fct>   <chr>        <dbl>
#> 1     1 0       Naive CD4+ T 0.104
#> 2     1 1       CD14+ Mono   0.162
#> 3     1 2       Memory CD4+  0.114
#> 4     1 3       B            0.139
#> 5     1 4       CD8+ T       0.190
#> 6     1 5       FCFR3A+ Mono 0.366
#> 7     1 6       NK           0.256
#> 8     1 7       DC           0.559
#> 9     1 8       Platelet     0.973

compare to the manual marked cell types:

Cluster ID Markers Cell Type
0 IL7R, CCR7 Naive CD4+ T
1 CD14, LYZ CD14+ Mono
2 IL7R, S100A4 Memory CD4+
3 MS4A1 B
4 CD8A CD8+ T
5 FCGR3A, MS4A7 FCGR3A+ Mono
6 GNLY, NKG7 NK
7 FCER1A, CST3 DC
8 PPBP Platelet

See: https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#assigning-cell-type-identity-to-clusters

Exploring the result of hitype_assign

The result of hitype_assign is a data.frame with the following columns:

  • Level: the level of the cell type in the hierarchy
  • Cluster: the cluster ID
  • CellType: the cell type name
  • Score: the score of the cell type
head(cell_types)
#> # A tibble: 1 × 4
#>   Level Cluster CellType     Score
#>   <int> <fct>   <chr>        <dbl>
#> 1     1 0       Naive CD4+ T 0.104