hitype

Hierarchical and high-resolution cell-type identification for single-cell RNA-seq data inspired by ScType.

Features

• Compatibility with ScType
• Hierarchical and high-resolution cell-type identification
• Train weights for your markers with a reference dataset
• Speed optimization
• Support as an R package with unit tests

Installation

You can install the development version of hitype like so:

if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")
}
devtools::install_github("pwwang/hitype")

Quick start

Prepare the dataset

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

Click to expand

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)

Use as a Seurat extension

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:

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

Use as standalone functions

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

You may see that we have exactly the same assignment in the Seurat tutorial:

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

Documentation

https://pwwang.github.io/hitype/