Marker weights improve cell type assignment • hitype

library(hitype)

Unweighted markers:

markers <- data.frame(
    cellName = c(
        "Naive CD4+ T", "CD14+ Mono", "Memory CD4+", "B",
        "CD8+ T", "FCFR3A+ Mono", "NK", "DC", "Platelet"
    ),
    geneSymbolmore1 = c(
        "IL7R,CCR7",  "CD14,LYZ", "IL7R,S100A4", "MS4A1",
        "CD8A", "FCGR3A,MS4A7", "GNLY,NKG7", "FCER1A,CST3", "PPBP"
    ),
    geneSymbolmore2 = rep("", 9)
)

markers
#>       cellName geneSymbolmore1 geneSymbolmore2
#> 1 Naive CD4+ T       IL7R,CCR7                
#> 2   CD14+ Mono        CD14,LYZ                
#> 3  Memory CD4+     IL7R,S100A4                
#> 4            B           MS4A1                
#> 5       CD8+ T            CD8A                
#> 6 FCFR3A+ Mono    FCGR3A,MS4A7                
#> 7           NK       GNLY,NKG7                
#> 8           DC     FCER1A,CST3                
#> 9     Platelet            PPBP

Prepare pbmc data:

pbmc <- pbmc3k.SeuratData::pbmc3k
pbmc <- Seurat::UpdateSeuratObject(pbmc)
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)
new_cluster_ids <- markers$cellName
names(new_cluster_ids) <- levels(pbmc)
pbmc <- Seurat::RenameIdents(pbmc, new_cluster_ids)

Use unweighted markers:

gs <- gs_prepare(markers)
scores <- hitype_score(Seurat::GetAssayData(pbmc, layer = "data"), gs)
assigned <- hitype_assign(pbmc$seurat_clusters, scores, gs, threshold = 0.01)
out <- summary(assigned)
out$ManualAssignment <- markers$cellName
out
#> # A tibble: 9 × 6
#>   Level Cluster CellType      Score  Margin ManualAssignment
#>   <int> <fct>   <chr>         <dbl>   <dbl> <chr>           
#> 1     1 0       Naive CD4+ T  0.721  0.685  Naive CD4+ T    
#> 2     1 1       CD14+ Mono    2.28   1.20   CD14+ Mono      
#> 3     1 2       Memory CD4+   0.599  0.0927 Memory CD4+     
#> 4     1 3       B             2.09   2.17   B               
#> 5     1 4       NK            1.43   0.179  CD8+ T          
#> 6     1 5       FCFR3A+ Mono  3.42   2.44   FCFR3A+ Mono    
#> 7     1 6       NK            3.91   2.88   NK              
#> 8     1 7       DC            5.55   4.59   DC              
#> 9     1 8       Platelet     11.5   11.3    Platelet

Use trained weights (glmnet, 5-fold CV). Note the recommended scoring settings for learned weights: norm = "weight", use_sensitivity = FALSE:

weights <- train_weights(
    path_to_gs = markers,
    exprs = pbmc,
    method = "glmnet",
    cv_folds = 5
)
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> Warning in lognet(x, is.sparse, y, weights, offset, alpha, nobs, nvars, : one
#> multinomial or binomial class has fewer than 8 observations; dangerous ground
#> # A tibble: 9 × 5
#>   Level Cluster      CellType     Score Margin
#>   <dbl> <fct>        <chr>        <dbl>  <dbl>
#> 1     1 Naive CD4+ T Naive CD4+ T 0.258  0.184
#> 2     1 CD14+ Mono   CD14+ Mono   3.48   2.29 
#> 3     1 Memory CD4+  Memory CD4+  0.241  0.179
#> 4     1 B            B            3.35   3.41 
#> 5     1 CD8+ T       NK           1.61   1.11 
#> 6     1 FCFR3A+ Mono FCFR3A+ Mono 3.63   2.42 
#> 7     1 NK           NK           4.02   2.13 
#> 8     1 DC           DC           3.36   0.211
#> 9     1 Platelet     Platelet     5.40   5.08

gs <- gs_prepare(weights)
scores <- hitype_score(Seurat::GetAssayData(pbmc, layer = "data"), gs,
                       norm = "weight", use_sensitivity = FALSE)
assigned <- hitype_assign(pbmc$seurat_clusters, scores, gs, threshold = 0.01)
out <- summary(assigned)
out$ManualAssignment <- markers$cellName
out
#> # A tibble: 9 × 6
#>   Level Cluster CellType      Score Margin ManualAssignment
#>   <int> <fct>   <chr>         <dbl>  <dbl> <chr>           
#> 1     1 0       Naive CD4+ T  0.605  0.569 Naive CD4+ T    
#> 2     1 1       CD14+ Mono    1.67   0.796 CD14+ Mono      
#> 3     1 2       Memory CD4+   0.388  0.160 Memory CD4+     
#> 4     1 3       B             2.09   2.17  B               
#> 5     1 4       CD8+ T        1.25   0.153 CD8+ T          
#> 6     1 5       FCFR3A+ Mono  2.44   1.63  FCFR3A+ Mono    
#> 7     1 6       NK            2.72   1.49  NK              
#> 8     1 7       DC            3.64   2.48  DC              
#> 9     1 8       Platelet     11.5   11.3   Platelet

Use unweighted markers on ifnb dataset:

suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(dplyr))

ifnb <- ifnb.SeuratData::ifnb
ifnb <- Seurat::UpdateSeuratObject(ifnb)

# Keep only the cells that exist in the pbmc dataset
ifnb <- subset(ifnb, subset = seurat_annotations %in% c(
  "CD14 Mono",
  "DC",
  "CD4 Memory T",
  "CD4 Naive T",
  "CD8 T",
  "B",
  "NK"
))

# ifnb ships raw counts in SeuratData — normalize before scoring
ifnb <- Seurat::NormalizeData(ifnb)

ifnb@meta.data <- ifnb@meta.data %>%
  mutate(seurat_clusters = case_when(
    seurat_annotations == "CD14 Mono" ~ "CD14+ Mono",
    seurat_annotations == "CD4 Memory T" ~ "Memory CD4+",
    seurat_annotations == "CD4 Naive T" ~ "Naive CD4+ T",
    seurat_annotations == "CD8 T" ~ "CD8+ T",
    TRUE ~ seurat_annotations
  ))

gs <- gs_prepare(markers)

scores <- hitype_score(Seurat::GetAssayData(ifnb, layer = "data"), gs)

assigned <- hitype_assign(ifnb$seurat_clusters, scores, gs, threshold = 0.01)

summary(assigned)

Use trained weights on ifnb dataset (cross-dataset transfer — the weights were learned on pbmc, never on ifnb):

gs <- gs_prepare(weights)

scores <- hitype_score(Seurat::GetAssayData(ifnb, layer = "data"), gs,
                       norm = "weight", use_sensitivity = FALSE)

assigned <- hitype_assign(ifnb$seurat_clusters, scores, gs, threshold = 0.01)

summary(assigned)