Automated Cell Type Annotation

CellTypist (Python)

```python

import celltypist

import scanpy as sc

adata = sc.read_h5ad('adata_processed.h5ad')

List available models

celltypist.models.models_description()

Download model

celltypist.models.download_models(model='Immune_All_Low.pkl')

Load model

model = celltypist.models.Model.load(model='Immune_All_Low.pkl')

Predict cell types

predictions = celltypist.annotate(adata, model=model, majority_voting=True)

Add predictions to adata

adata = predictions.to_adata()

Access predictions

adata.obs['cell_type_celltypist'] = adata.obs['majority_voting']

adata.obs['cell_type_confidence'] = adata.obs['conf_score']

Visualize

sc.pl.umap(adata, color=['cell_type_celltypist', 'conf_score'])

```

CellTypist with Custom Model

```python

Train custom model

new_model = celltypist.train(adata_reference, labels='cell_type', n_jobs=10,

feature_selection=True, use_SGD=True)

Save model

new_model.write('custom_model.pkl')

Use custom model

predictions = celltypist.annotate(adata_query, model='custom_model.pkl')

```

SingleR (R)

```r

library(SingleR)

library(celldex)

library(Seurat)

library(SingleCellExperiment)

seurat_obj <- readRDS('seurat_processed.rds')

sce <- as.SingleCellExperiment(seurat_obj)

Load reference (multiple available)

ref <- celldex::HumanPrimaryCellAtlasData()

Other options:

ref <- celldex::BlueprintEncodeData()

ref <- celldex::MonacoImmuneData()

ref <- celldex::ImmGenData() # mouse

Run SingleR

pred <- SingleR(test = sce, ref = ref, labels = ref$label.main, de.method = 'wilcox')

Add to Seurat

seurat_obj$SingleR_labels <- pred$labels

seurat_obj$SingleR_pruned <- pred$pruned.labels

Check annotation quality

plotScoreHeatmap(pred)

plotDeltaDistribution(pred)

```

SingleR Fine Labels

```r

Use fine-grained labels

pred_fine <- SingleR(test = sce, ref = ref, labels = ref$label.fine)

Combine multiple references

ref1 <- celldex::BlueprintEncodeData()

ref2 <- celldex::MonacoImmuneData()

pred_combined <- SingleR(test = sce, ref = list(BP = ref1, Monaco = ref2),

labels = list(ref1$label.main, ref2$label.main))

```

Azimuth (R/Seurat)

```r

library(Seurat)

library(Azimuth)

seurat_obj <- readRDS('seurat_processed.rds')

Run Azimuth with PBMC reference

seurat_obj <- RunAzimuth(seurat_obj, reference = 'pbmcref')

Available references: pbmcref, bonemarrowref, lungref, etc.

Access predictions

seurat_obj$azimuth_labels <- seurat_obj$predicted.celltype.l2

seurat_obj$azimuth_score <- seurat_obj$predicted.celltype.l2.score

Visualize

DimPlot(seurat_obj, group.by = 'azimuth_labels', label = TRUE) + NoLegend()

FeaturePlot(seurat_obj, features = 'predicted.celltype.l2.score')

```

scPred (R)

```r

library(scPred)

library(Seurat)

Train on reference

reference <- readRDS('reference_seurat.rds')

reference <- getFeatureSpace(reference, 'cell_type')

reference <- trainModel(reference)

Get training probabilities

get_probabilities(reference)

get_scpred(reference)

Plot model performance

plot_probabilities(reference)

Predict on query

query <- readRDS('query_seurat.rds')

query <- scPredict(query, reference)

Results

query$scpred_prediction

query$scpred_max

```

Annotation Confidence Filtering

```python

CellTypist: filter low confidence

high_conf = adata[adata.obs['conf_score'] > 0.5].copy()

Flag uncertain cells

adata.obs['annotation_uncertain'] = adata.obs['conf_score'] < 0.3

```

```r

SingleR: use pruned labels (low-quality removed)

seurat_obj$final_labels <- ifelse(is.na(pred$pruned.labels), 'Unknown', pred$labels)

Azimuth: filter by score

seurat_obj$high_conf_labels <- ifelse(seurat_obj$predicted.celltype.l2.score > 0.7,

seurat_obj$predicted.celltype.l2, 'Low_confidence')

```

Consensus Annotation

```r

Combine multiple methods

annotations <- data.frame(

SingleR = seurat_obj$SingleR_labels,

Azimuth = seurat_obj$azimuth_labels,

CellTypist = seurat_obj$celltypist_labels

)

Majority vote

get_consensus <- function(x) {

tbl <- table(x)

if (max(tbl) >= 2) names(which.max(tbl)) else 'Ambiguous'

}

seurat_obj$consensus_label <- apply(annotations, 1, get_consensus)

```

Compare Annotations

```python

import pandas as pd

from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score

Compare two annotations

ari = adjusted_rand_score(adata.obs['manual_annotation'], adata.obs['celltypist'])

nmi = normalized_mutual_info_score(adata.obs['manual_annotation'], adata.obs['celltypist'])

Confusion matrix

pd.crosstab(adata.obs['manual_annotation'], adata.obs['celltypist'])

```

Marker-Based Validation

```r

Validate predictions with known markers

canonical_markers <- list(

T_cell = c('CD3D', 'CD3E', 'CD4', 'CD8A'),

B_cell = c('CD19', 'MS4A1', 'CD79A'),

Monocyte = c('CD14', 'LYZ', 'S100A8'),

NK = c('NKG7', 'GNLY', 'NCAM1')

)

Check marker expression per predicted type

DotPlot(seurat_obj, features = unlist(canonical_markers), group.by = 'predicted_labels') +

RotatedAxis()

```

Related Skills