Version Compatibility

Reference examples tested with: pandas 2.2+, scanpy 1.10+, scikit-learn 1.4+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Automated Cell Type Annotation

CellTypist (Python)

Goal: Automatically annotate cell types using a pre-trained or custom CellTypist model.

Approach: Load a reference model, predict cell types with majority voting for cluster-level consensus, and add predictions to AnnData.

"Automatically label my cell types" → Apply a trained classifier to assign cell type identities based on transcriptomic similarity to a reference atlas.

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

Goal: Train a custom CellTypist model on a reference dataset for domain-specific annotation.

Approach: Train a logistic regression classifier on labeled reference data with feature selection, then apply to query data.

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)

Goal: Annotate cell types by correlating expression profiles against curated reference datasets.

Approach: Compare each cell's expression to reference transcriptomes using SingleR's correlation-based assignment, with pruning for low-confidence calls.

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)

Goal: Annotate cell types using Seurat's Azimuth reference-mapping framework.

Approach: Map query cells onto a pre-built Azimuth reference atlas to transfer cell type labels with confidence scores.

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)

Goal: Train and apply a supervised classifier for cell type prediction using scPred.

Approach: Extract informative PCA features from a labeled reference, train an SVM/RF classifier, and predict cell types on query data.

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

Goal: Combine predictions from multiple annotation tools into a single consensus label per cell.

Approach: Aggregate labels from SingleR, Azimuth, and CellTypist using majority voting, flagging ambiguous cells where methods disagree.

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

Goal: Quantitatively assess agreement between different annotation methods.

Approach: Compute adjusted Rand index and normalized mutual information between label sets, and build a confusion matrix.

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

• single-cell/clustering - Manual marker-based annotation
• single-cell/cell-communication - Use annotated types for CCC
• single-cell/trajectory-inference - Trajectory on annotated data