Seurat’s AddModuleScore function

Last updated: 2025-02-19

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File	Version	Author	Date	Message
Rmd	55128f2	Dave Tang	2025-02-19	Seurat’s add module score

A bastardisation of Walter Muskovic’s blog post Seurat’s AddModuleScore function (my apologies).

Load packages.

suppressPackageStartupMessages(library("Seurat"))
suppressPackageStartupMessages(library("ggplot2"))

Download the Peripheral Blood Mononuclear Cells (PBMCs) 2,700 cells dataset.

mkdir -p data/pbmc3k && cd data/pbmc3k
wget -c https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz
tar -xzf pbmc3k_filtered_gene_bc_matrices.tar.gz

Create Seurat object.

work_dir <- rprojroot::find_rstudio_root_file()
data_dir <- paste0(work_dir, "/data/pbmc3k/filtered_gene_bc_matrices/hg19/")
stopifnot(dir.exists(data_dir))
pbmc.data <- Read10X(
  data.dir = data_dir
)

seurat_obj <- CreateSeuratObject(
  counts = pbmc.data,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

debug_flag <- FALSE
seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", scale.factor = 1e4, verbose = debug_flag)
seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = 2000, verbose = debug_flag)
seurat_obj <- ScaleData(seurat_obj, verbose = debug_flag)
seurat_obj <- RunPCA(seurat_obj, verbose = debug_flag)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30, verbose = debug_flag)

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30, verbose = debug_flag)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5, verbose = debug_flag)

seurat_obj

An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 2 dimensional reductions calculated: pca, umap

UMAP.

DimPlot(seurat_obj, label = TRUE, repel = TRUE) + NoLegend()

Get top 20 genes enriched in cluster 4.

FindMarkers(seurat_obj, ident.1 = "4", verbose = FALSE) |>
  tibble::rownames_to_column(var = "gene_symbol") |>
  head(20) |>
  dplyr::pull(gene_symbol) -> cluster_4_markers

Add module score; it is very important that features are provided as a list.

AddModuleScore(
  seurat_obj,
  features = list(cluster_4_markers),
  name = "cluster_4_markers"
) -> seurat_obj

FeaturePlot(
  seurat_obj,
  features = "cluster_4_markers1",
  label = TRUE,
  repel = TRUE
)

Details of method in the supplementary materials:

The top 100 MITF-correlated genes across the entire set of malignant cells were defined as the MITF program, and their average relative expression as the MITF-program cell score.
The average expression of the top 100 genes that negatively correlate with the MITF program scores were defined as the AXL program and used to define AXL program cell score.
To decrease the effect that the quality and complexity of each cell’s data might have on its MITF/AXL scores we defined control gene-sets and their average relative expression as control scores, for both the MITF and AXL programs.
These control cell scores were subtracted from the respective MITF/AXL cell scores.
The control gene-sets were defined by first binning all analyzed genes into 25 bins of aggregate expression levels and then, for each gene in the MITF/AXL gene-set, randomly selecting 100 genes from the same expression bin as that gene.
In this way, a control gene-sets have a comparable distribution of expression levels to that of the MITF/AXL gene-set and the control gene set is 100-fold larger, such that its average expression is analogous to averaging over 100 randomly-selected gene-sets of the same size as the MITF/AXL gene-set.

Implementation in Seurat.

object <- seurat_obj
features <- list(cluster_4_markers)
pool <- rownames(seurat_obj)
nbin <- 24
ctrl <- 100
k <- FALSE
name = "cluster_4_markers"
seed = 1

# Find how many gene lists were provided. In this case just one.
cluster.length <- length(x = features)
cluster.length

[1] 1

# Pull the expression data from the provided Seurat object
# uses DefaultAssay()
# default is the data layer, by order
assay.data <- GetAssayData(object = object)
class(assay.data)

[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"

# For all genes, get the average expression across all cells (named vector)
data.avg <- Matrix::rowMeans(x = assay.data[pool, ])
length(data.avg)

[1] 13714

# Order genes from lowest average expression to highest average expression
data.avg <- data.avg[order(data.avg)]

# Use ggplot2's cut_number function to make n groups with (approximately) equal numbers of observations.
# The 'rnorm(n = length(data.avg))/1e+30' part adds a tiny bit of noise to the data, presumably to break ties.
# similar to base R's cut function
data.cut <- ggplot2::cut_number(
  x = data.avg + rnorm(n = length(data.avg))/1e+30,
  n = nbin,
  labels = FALSE,
  right = FALSE
)
table(data.cut)

data.cut
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
572 571 572 571 571 572 571 571 572 571 572 571 571 572 571 571 572 571 572 571 
 21  22  23  24 
571 572 571 572

# Set the names of the cuts as the gene names
names(x = data.cut) <- names(x = data.avg)
head(data.cut)

  CDC6 SHCBP1 VPREB1  ESCO2   LHFP    PBK 
     1      1      1      1      1      1

# Create an empty list the same length as the number of input gene sets. This will contain the names of the control genes
ctrl.use <- vector(mode = "list", length = cluster.length)
length(ctrl.use)

[1] 1

# For each of the input gene lists:
for (i in 1:cluster.length) {
  # Get the gene names from the input gene set as a character vector  
  features.use <- features[[i]]
  
  # Loop through the provided genes (1:num_genes) and for each gene, find ctrl (default=100) genes from the same expression bin (by looking in data.cut):
  for (j in 1:length(x = features.use)) {
    # Within this loop, 'data.cut[features.use[j]]' gives us the expression bin number. We then sample `ctrl` genes from that bin without replacement and add the gene names to ctrl.use.
    ctrl.use[[i]] <- c(
      ctrl.use[[i]],
      names(x = sample(
        x = data.cut[which(x = data.cut == data.cut[features.use[j]])],
        size = ctrl,
        replace = FALSE)
      )
    )
  }
}

# Have a quick look at what's in ctrl.use:
class(ctrl.use)

[1] "list"

length(ctrl.use)

[1] 1

class(ctrl.use[[1]])

[1] "character"

# There should be length(features.use)*ctrl genes (i.e. 20*100):
length(ctrl.use[[1]])

[1] 2000

Explanatory plot.

# Plot the bins that have been created to split genes based on their average expression
plot(data.avg, pch=16, ylab="Average expression across all cells", xlab="All genes, ranked")

for(i in unique(data.cut)){
  cut_pos <- which(data.cut==i)
  if(i%%2==0){
    rect(xleft = cut_pos[1], ybottom = min(data.avg), xright = cut_pos[length(cut_pos)], ytop = max(data.avg), col=scales::alpha("grey", 0.3))
  } else {
    rect(xleft = cut_pos[1], ybottom = min(data.avg), xright = cut_pos[length(cut_pos)], ytop = max(data.avg), col=scales::alpha("white", 0.3))
  }
}

# Add red points for selected control genes
points(which(names(data.avg)%in%ctrl.use[[1]]), data.avg[which(names(data.avg)%in%ctrl.use[[1]])], pch=16, col="red")

# Add blue points for genes in the input gene list
points(which(names(data.avg)%in%features[[1]]), data.avg[which(names(data.avg)%in%features[[1]])], pch=16, col="blue")

# Add a legend
legend(x = "topleft",
       legend = c("gene", "selected control gene", "gene in geneset"),
       col = c("black", "red", "blue"),
       pch = 16)

Note how control genes are only selected from the bins in which the genes in our input list fall.

# Remove any repeated gene names - even though we set replace=FALSE when we sampled genes from the same expression bin, there may be more than two genes in our input gene list that fall in the same expression bin, so we can end up sampling the same gene more than once.
ctrl.use <- lapply(X = ctrl.use, FUN = unique)


## Get control gene scores

# Create an empty matrix with dimensions;
  # number of rows equal to the number of gene sets (just one here)
  # number of columns equal to number of cells in input Seurat object
ctrl.scores <- matrix(data = numeric(length = 1L),
                      nrow = length(x = ctrl.use),
                      ncol = ncol(x = object))

# Loop through each provided gene set and add to the empty matrix the mean expression of the control genes in each cell
for (i in 1:length(ctrl.use)) {
  # Get control gene names as a vector  
  features.use <- ctrl.use[[i]]
  # For each cell, calculate the mean expression of *all* of the control genes 
  ctrl.scores[i, ] <- Matrix::colMeans(x = assay.data[features.use,])
}


## Get scores for input gene sets

# Similar to the above, create an empty matrix
features.scores <- matrix(data = numeric(length = 1L),
                          nrow = cluster.length,
                          ncol = ncol(x = object))

# Loop through input gene sets and calculate the mean expression of these genes for each cell
for (i in 1:cluster.length) {
    features.use <- features[[i]]
    data.use <- assay.data[features.use, , drop = FALSE]
    features.scores[i, ] <- Matrix::colMeans(x = data.use)
}

Now we have two matrices;

ctrl.scores - contains the mean expression of the control genes for each cell
features.scores - contains the mean expression of the genes in the input gene set for each cell

# Subtract the control scores from the feature scores - the idea is that if there is no enrichment of the genes in the geneset in a cell, then the result of this subtraction should be ~ 0
features.scores.use <- features.scores - ctrl.scores

# Name the result the "name" variable + whatever the position the geneset was in the input list, e.g. "Cluster1"
rownames(x = features.scores.use) <- paste0(name, 1:cluster.length)

# Change the matrix from wide to long
features.scores.use <- as.data.frame(x = t(x = features.scores.use))

# Give the rows of the matrix, the names of the cells
rownames(x = features.scores.use) <- colnames(x = object)

# Add the result as a metadata column to the input Seurat object 
object[[colnames(x = features.scores.use)]] <- features.scores.use

# Voila!
FeaturePlot(object, features = "cluster_4_markers1")

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.5.1      Seurat_5.1.0       SeuratObject_5.0.2 sp_2.1-4          
[5] workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.17.1      jsonlite_1.8.9        
  [4] magrittr_2.0.3         spatstat.utils_3.1-0   farver_2.1.2          
  [7] rmarkdown_2.28         fs_1.6.4               vctrs_0.6.5           
 [10] ROCR_1.0-11            spatstat.explore_3.3-3 htmltools_0.5.8.1     
 [13] sass_0.4.9             sctransform_0.4.1      parallelly_1.38.0     
 [16] KernSmooth_2.23-24     bslib_0.8.0            htmlwidgets_1.6.4     
 [19] ica_1.0-3              plyr_1.8.9             plotly_4.10.4         
 [22] zoo_1.8-12             cachem_1.1.0           whisker_0.4.1         
 [25] igraph_2.1.1           mime_0.12              lifecycle_1.0.4       
 [28] pkgconfig_2.0.3        Matrix_1.7-0           R6_2.5.1              
 [31] fastmap_1.2.0          fitdistrplus_1.2-1     future_1.34.0         
 [34] shiny_1.9.1            digest_0.6.37          colorspace_2.1-1      
 [37] patchwork_1.3.0        ps_1.8.1               rprojroot_2.0.4       
 [40] tensor_1.5             RSpectra_0.16-2        irlba_2.3.5.1         
 [43] labeling_0.4.3         progressr_0.15.0       fansi_1.0.6           
 [46] spatstat.sparse_3.1-0  httr_1.4.7             polyclip_1.10-7       
 [49] abind_1.4-8            compiler_4.4.1         withr_3.0.2           
 [52] fastDummies_1.7.4      highr_0.11             R.utils_2.12.3        
 [55] MASS_7.3-60.2          tools_4.4.1            lmtest_0.9-40         
 [58] httpuv_1.6.15          future.apply_1.11.3    goftest_1.2-3         
 [61] R.oo_1.26.0            glue_1.8.0             callr_3.7.6           
 [64] nlme_3.1-164           promises_1.3.0         grid_4.4.1            
 [67] Rtsne_0.17             getPass_0.2-4          cluster_2.1.6         
 [70] reshape2_1.4.4         generics_0.1.3         gtable_0.3.6          
 [73] spatstat.data_3.1-2    R.methodsS3_1.8.2      tidyr_1.3.1           
 [76] data.table_1.16.2      utf8_1.2.4             spatstat.geom_3.3-3   
 [79] RcppAnnoy_0.0.22       ggrepel_0.9.6          RANN_2.6.2            
 [82] pillar_1.9.0           stringr_1.5.1          limma_3.62.2          
 [85] spam_2.11-0            RcppHNSW_0.6.0         later_1.3.2           
 [88] splines_4.4.1          dplyr_1.1.4            lattice_0.22-6        
 [91] survival_3.6-4         deldir_2.0-4           tidyselect_1.2.1      
 [94] miniUI_0.1.1.1         pbapply_1.7-2          knitr_1.48            
 [97] git2r_0.35.0           gridExtra_2.3          scattermore_1.2       
[100] xfun_0.48              statmod_1.5.0          matrixStats_1.4.1     
[103] stringi_1.8.4          lazyeval_0.2.2         yaml_2.3.10           
[106] evaluate_1.0.1         codetools_0.2-20       tibble_3.2.1          
[109] cli_3.6.3              uwot_0.2.2             xtable_1.8-4          
[112] reticulate_1.39.0      munsell_0.5.1          processx_3.8.4        
[115] jquerylib_0.1.4        Rcpp_1.0.13            globals_0.16.3        
[118] spatstat.random_3.3-2  png_0.1-8              spatstat.univar_3.0-1 
[121] parallel_4.4.1         presto_1.0.0           dotCall64_1.2         
[124] listenv_0.9.1          viridisLite_0.4.2      scales_1.3.0          
[127] ggridges_0.5.6         leiden_0.4.3.1         purrr_1.0.2           
[130] rlang_1.1.4            cowplot_1.1.3