1 Introduction

The function AddModuleScore_UCell() allows operating directly on Seurat objects. UCell scores are calculated from raw counts or normalized data, and returned as metadata columns. The example below defines some simple signatures, and applies them on single-cell data stored in a Seurat object.

To see how this function differs from Seurat’s own AddModuleScore() (not based on per-cell ranks) see this vignette.

2 Get some testing data

For this demo, we will download a single-cell dataset of lung cancer (Zilionis et al. (2019) Immunity) through the scRNA-seq package. This dataset contains >170,000 single cells; for the sake of simplicity, in this demo will we focus on immune cells, according to the annotations by the authors, and downsample to 5000 cells.

library(scRNAseq)

lung <- ZilionisLungData()
immune <- lung$Used & lung$used_in_NSCLC_immune
lung <- lung[,immune]
lung <- lung[,1:5000]

exp.mat <- Matrix::Matrix(counts(lung),sparse = TRUE)
colnames(exp.mat) <- paste0(colnames(exp.mat), seq(1,ncol(exp.mat)))

3 Define gene signatures

Here we define some simple gene sets based on the “Human Cell Landscape” signatures Han et al. (2020) Nature. You may edit existing signatures, or add new one as elements in a list.

signatures <- list(
    Tcell = c("CD3D","CD3E","CD3G","CD2","TRAC"),
    Myeloid = c("CD14","LYZ","CSF1R","FCER1G","SPI1","LCK-"),
    NK = c("KLRD1","NCR1","NKG7","CD3D-","CD3E-"),
    Plasma_cell = c("MZB1","DERL3","CD19-")
)

4 Run UCell on Seurat object

library(UCell)
library(Seurat)
seurat.object <- CreateSeuratObject(counts = exp.mat, 
                                    project = "Zilionis_immune")
seurat.object <- AddModuleScore_UCell(seurat.object, 
                                      features=signatures, name=NULL)
head(seurat.object[[]])
##        orig.ident nCount_RNA nFeature_RNA Tcell   Myeloid NK Plasma_cell
## 1 Zilionis_immune       7516         2613     0 0.5227121  0  0.00000000
## 2 Zilionis_immune       5684         1981     0 0.5112892  0  0.00000000
## 3 Zilionis_immune       4558         1867     0 0.3584502  0  0.07540874
## 4 Zilionis_immune       2915         1308     0 0.1546426  0  0.00000000
## 5 Zilionis_immune       3576         1548     0 0.4629927  0  0.00000000
## 6 Zilionis_immune       2796         1270     0 0.5452238  0  0.00000000

Generate PCA and UMAP embeddings

seurat.object <- NormalizeData(seurat.object)
seurat.object <- FindVariableFeatures(seurat.object, 
                     selection.method = "vst", nfeatures = 500)
  
seurat.object <- ScaleData(seurat.object)
seurat.object <- RunPCA(seurat.object, npcs = 20, 
                        features=VariableFeatures(seurat.object)) 
seurat.object <- RunUMAP(seurat.object, reduction = "pca", 
                         dims = 1:20, seed.use=123)

Visualize UCell scores on low-dimensional representation (UMAP)

library(ggplot2)
library(patchwork)

FeaturePlot(seurat.object, reduction = "umap", features = names(signatures))