Single Popv Annotation

Stage 1: Data Preparation

import omicverse as ov

# Process_Query preprocesses and concatenates query + reference
process_obj = ov.popv.Process_Query(
    query_adata=query_adata,
    ref_adata=ref_adata,
    ref_labels_key='cell_type',           # REQUIRED: column in ref_adata.obs
    ref_batch_key='batch',                # batch column in ref_adata.obs
    query_batch_key='batch',              # batch column in query_adata.obs (optional)
    cl_obo_folder=False,                  # False to skip ontology, or path to CL .obo file
    prediction_mode='retrain',            # 'retrain' | 'inference' | 'fast'
    unknown_celltype_label='unknown',     # label for query cells
    n_samples_per_label=300,              # subsample reference per cell type
    hvg=4000,                             # number of highly variable genes
    save_path_trained_models='tmp/',      # where to save models
    pretrained_scvi_path=None,            # path to pretrained scVI model (optional)
)

prediction_mode choices:

• 'retrain' — Train all models from scratch on reference+query. Most accurate, slowest.
• 'inference' — Load previously saved models. Requires save_path_trained_models from prior run.
• 'fast' — Skip integration-heavy algorithms. Uses FAST_ALGORITHMS subset.

Preprocessing applied automatically:

• Filters cells with < 30 total counts
• Log1p normalization (target_sum=1e4)
• PCA on reference (50 components)
• Stores raw counts in layers['scvi_counts']

Stage 2: Annotation

# Run all algorithms and compute consensus
ov.popv.annotate_data(
    process_obj.adata,
    methods='all',                        # or list of specific algorithms
    save_path='results/popv/',            # saves predictions.csv here
    methods_kwargs=None,                  # dict of per-method overrides
)

Available Algorithms (10 total)

Algorithm subsets:

• FAST_ALGORITHMS: KNN_SCVI, SCANVI_POPV, Support_Vector, XGboost, ONCLASS, CELLTYPIST (used with prediction_mode='fast')
• CURRENT_ALGORITHMS: All except Random_Forest and KNN_SCANORAMA (outdated)
• 'all' or None: Uses CURRENT_ALGORITHMS (or FAST_ALGORITHMS in fast mode)

Selecting Specific Methods

# Run only fast classical methods
ov.popv.annotate_data(
    process_obj.adata,
    methods=['CELLTYPIST', 'Support_Vector', 'XGboost'],
)

# Override per-method parameters
ov.popv.annotate_data(
    process_obj.adata,
    methods=['KNN_SCVI', 'SCANVI_POPV'],
    methods_kwargs={
        'KNN_SCVI': {'train_kwargs': {'max_epochs': 50}},
        'SCANVI_POPV': {'train_kwargs': {'max_epochs': 50}},
    },
)

Stage 3: Consensus Results & Visualization

After annotate_data(), these columns appear in adata.obs:

# Agreement plots: confusion matrices per method vs consensus
ov.popv.make_agreement_plots(
    process_obj.adata,
    prediction_keys=process_obj.adata.uns['prediction_keys'],
    popv_prediction_key='popv_prediction',
    save_folder='results/popv/',
    show=True,
)

# Bar plot: agreement score per cell type
ov.popv.agreement_score_bar_plot(
    process_obj.adata,
    popv_prediction_key='popv_prediction',
    save_folder='results/popv/',
)

# Bar plot: prediction score distribution
ov.popv.prediction_score_bar_plot(
    process_obj.adata,
    popv_prediction_score='popv_prediction_score',
    save_folder='results/popv/',
)

# Bar plot: cell type proportions (ref vs query)
ov.popv.celltype_ratio_bar_plot(
    process_obj.adata,
    popv_prediction='popv_prediction',
    save_folder='results/popv/',
)

Stage 4: Pretrained Hub Models (Optional)

For large references (e.g., Human Cell Atlas), use pretrained models to skip training:

from omicverse.popv.hub import HubModel

# Pull pretrained model from HuggingFace
model = HubModel.pull_from_huggingface_hub(
    repo_name='popv/immune_all',
    cache_dir='models/popv/',
)

# Annotate query data directly (fast mode)
result_adata = model.annotate_data(
    query_adata=query_adata,
    query_batch_key='batch',
    prediction_mode='fast',
    methods=None,  # uses model's default methods
)

Critical API Reference

# CORRECT: methods as list of strings matching class names
ov.popv.annotate_data(adata, methods=['KNN_SCVI', 'CELLTYPIST', 'Support_Vector'])

# WRONG: passing class objects or lowercase names
# ov.popv.annotate_data(adata, methods=[KNN_SCVI, CELLTYPIST])  # TypeError
# ov.popv.annotate_data(adata, methods=['knn_scvi'])             # KeyError

# CORRECT: ref_labels_key must exist in ref_adata.obs before Process_Query
assert 'cell_type' in ref_adata.obs.columns
process_obj = ov.popv.Process_Query(ref_labels_key='cell_type', ...)

# WRONG: forgetting to set unknown_celltype_label causes NaN in voting
# process_obj = ov.popv.Process_Query(..., unknown_celltype_label=None)  # NaN errors

# CORRECT: access consensus results after annotation
final_labels = process_obj.adata.obs['popv_majority_vote_prediction']
# or ontology-refined:
final_labels = process_obj.adata.obs['popv_prediction']

# WRONG: looking for results on the original query_adata
# query_adata.obs['popv_prediction']  # KeyError: results are on process_obj.adata

GPU Acceleration

import omicverse.popv as popv
popv.settings.accelerator = 'gpu'   # for scVI/scANVI training
popv.settings.cuml = True           # for KNN/SVM/RF via cuML
popv.settings.n_jobs = 10           # parallel jobs for CPU methods

Troubleshooting

• RuntimeError: CUDA out of memory during scVI/scANVI training: Reduce hvg (try 2000), decrease n_samples_per_label (try 100), or switch to prediction_mode='fast' which uses fewer epochs.
• CellTypist model download fails: Set methods_kwargs={'CELLTYPIST': {'method_kwargs': {'model': '/path/to/local/model.pkl'}}} to use a local model file.
• Low consensus agreement (<50% cells agree): Some algorithms may not suit your tissue. Exclude underperforming methods: check per-method predictions and drop outliers from the methods list.
• KeyError: 'gene_name' — gene identifier mismatch: Harmonize var_names between reference and query before calling Process_Query. Use adata.var_names = adata.var['gene_symbols'] if ENSEMBL IDs are in var_names.
• ValueError: batch_key contains NaN: Clean batch columns before PopV. Apply the batch validation pattern from the single-preprocessing skill: adata.obs['batch'] = adata.obs['batch'].fillna('unknown').astype('category').
• FileNotFoundError in inference mode: Ensure save_path_trained_models points to the same directory used during the original retrain run. Check that model files (.pt, .pkl, .joblib) exist.

Dependencies

• Core: omicverse, scanpy, anndata, numpy, pandas
• Deep learning: scvi-tools, torch (for KNN_SCVI, SCANVI_POPV)
• Classical ML: scikit-learn, xgboost (for RF, SVM, XGBoost)
• Integration: harmonypy, bbknn, scanorama (for respective KNN methods)
• Annotation: celltypist, OnClass (optional per method)
• Ontology: obonet, pronto (for ontology-aware voting)
• Hub: huggingface_hub (for pretrained models)

Examples

• "Annotate my PBMC query data against a reference atlas using PopV with all 10 algorithms and visualize the consensus."
• "Use a pretrained PopV hub model to quickly annotate my lung tissue scRNA-seq data."
• "Run PopV with only classical methods (SVM, XGBoost, CellTypist) to annotate my query cells without GPU."

References

• Quick copy/paste commands: reference.md