bio-single-cell-cell-annotation

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot b2fa6c982fd4

Summary

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SKILL.md

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.

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.

# 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')

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)

# 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))

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')

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

# 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

# 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')

# 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)

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'])

# 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()

bio-single-cell-cell-annotation

Summary

SKILL.md

Version Compatibility

Automated Cell Type Annotation

CellTypist (Python)

CellTypist with Custom Model

SingleR (R)

SingleR Fine Labels

Azimuth (R/Seurat)

scPred (R)

Annotation Confidence Filtering

Consensus Annotation

Compare Annotations

Marker-Based Validation

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