bio-machine-learning-atlas-mapping

name	bio-machine-learning-atlas-mapping
description	Maps query single-cell data onto reference atlases and transfers cell-type labels using scArches surgery (scVI/scANVI), Symphony, Azimuth, CellTypist, scPoli, popV, and foundation models, with explicit out-of-distribution and label-transfer uncertainty. Use when annotating new single-cell datasets against a pre-trained reference, deciding which mapping method fits, or judging whether transferred labels are trustworthy. For de novo clustering and manual annotation see single-cell/cell-annotation; for batch integration without a reference see single-cell/batch-integration.
tool_type	python
primary_tool	scvi-tools

Version Compatibility

Reference examples tested with: anndata 0.10+, scanpy 1.10+, scvi-tools 1.1+, scikit-learn 1.3+, celltypist 1.6+.

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

• Python: pip show <package> then help(module.function) to check signatures

scvi-tools 1.x has had API churn around minified/registered models and the semi-supervised loader. Confirm scvi.__version__ and help(scvi.model.SCANVI.load_query_data) before relying on argument names. If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Reference Mapping and Label Transfer for Single-Cell Data

"Annotate my scRNA-seq query against a reference atlas" -> Project query cells into a fixed reference latent space and transfer labels, then gate every label on an out-of-distribution signal.

• scArches surgery: scvi.model.SCANVI.prepare_query_anndata() -> load_query_data() -> train(plan_kwargs={'weight_decay': 0.0}) -> predict(soft=True)
• Linear/fixed reference: Symphony (R) or Azimuth/Seurat anchor transfer
• Pure classifier (no embedding): CellTypist logistic regression

The Single Most Important Modern Insight -- Mapping Is a Projection, Not an Annotation Oracle

Reference mapping indicates where in a fixed reference manifold a query cell lands; it does not establish whether that location is biologically meaningful for the query. The projection always succeeds geometrically: every query cell is assigned its nearest reference label whether or not that label is true. A softmax over reference classes is normalized to sum to 1 and therefore cannot express "I have never seen this cell" -- a hepatocyte handed to an immune reference gets confidently called a T cell. Every failure mode below is a corollary of confusing projection with annotation.

The operational consequence: a transferred label is untrustworthy until paired with an out-of-distribution / label-transfer-uncertainty signal that measures whether the cell belongs to the reference at all, which is a different quantity from the prediction probability that measures which reference label. Conflating these two is the field's most common error.

Methods Taxonomy

Method	Model class	Needs reference model?	Novel-cell-type signal	Best when	Fails when
scVI + scArches surgery	Conditional VAE; surgery freezes reference weights, fits query-batch nodes	Yes (saved scVI model)	None intrinsic; add kNN uncertainty / OOD distance	Unseen batch; want a de novo joint embedding then cluster query yourself	Expected to label (it only embeds); reference lacks query biology
scANVI + scArches surgery	Semi-supervised VAE (scVI latent + label classifier head)	Yes (scANVI, often from_scvi_model)	Classifier softmax (overconfident); kNN-on-latent uncertainty	Reference well-labeled, query is the same tissue/biology	Semi-supervised leakage carves latent; novel states confidently mislabeled
Symphony	Linear Harmony soft-cluster mixture; query projected into fixed reference	Yes (compressed reference object)	Per-cell Mahalanobis distance to soft-cluster centroids	Seconds-scale, deterministic, CPU-only, reproducible/clinical	Strong nonlinear batch the reference never saw
Azimuth / Seurat anchor transfer	CCA/PCA anchors; supervised PCA projection	Yes (precomputed ref)	prediction.score.max, mapping.score	Multimodal refs (CITE-seq/WNN), curated tissue atlases, R shop	Filtered-anchor pathology when query is very divergent
scPoli	Conditional VAE + learnable sample embeddings + cell-type prototypes	Yes (built on scArches)	Prototype distance + uncertainty	Want sample-level (patient) embeddings too, many small batches	Few samples (condition embedding underdetermined)
popV	Ensemble of methods + ontology-aware voting	Mixed (wraps several)	Cross-method disagreement = uncertainty	High-stakes atlas annotation; distrust any single method	Compute-heavy; consensus can be confidently wrong if all share reference bias
CellTypist	Logistic regression (pre-trained models)	No embedding (ships models)	Low max-probability = ambiguous; no true OOD	Fast immune annotation, no integration needed	Treated as a mapper (no shared embedding, no batch handling)
treeArches / scHPL	scArches + hierarchical classifier with rejection	Yes	Explicit rejection -> "unseen" node	Expect novel subtypes, want hierarchy-aware fallback	Mis-specified hierarchy propagates error down branches
Foundation models (scGPT, Geneformer)	Transformer pretrained on 10s of millions of cells	Checkpoint; fine-tune needs labels	None intrinsic; OOD poorly characterized	Fine-tuned on the target task; cross-modality/species; data-scarce	Zero-shot: underperform scVI/Harmony/HVG-PCA (Kedzierska 2025)

Cross-cutting: linear methods (Symphony, Azimuth-sPCA) give a fixed, reproducible reference embedding (the query never perturbs the reference); VAE surgery fine-tunes and can drift. Reproducibility-critical or clinical pipelines lean linear; maximal batch-effect flexibility leans VAE.

Decision Tree by Scenario

Scenario	Recommended approach	Why
Same tissue as a published scVI/scANVI atlas; want one embedding + labels	scArches surgery onto the scANVI model; then gate labels on kNN uncertainty	Built for this; reuses the learned manifold; only query fine-tuned
Seconds-scale, deterministic, CPU-only, must re-run identically (clinical)	Symphony (or Azimuth if curated)	Fixed reference embedding, no fine-tuning drift, built-in Mahalanobis OOD
Query likely contains cell types/states NOT in the reference (disease, new niche)	treeArches/scHPL rejection, or scArches + explicit OOD distance	Default kNN/softmax confidently mislabels novel cells
High-stakes annotation; distrust any single method	popV ensemble	Cross-method disagreement is a more honest uncertainty than any softmax
Want patient/sample-level structure too	scPoli	Only method learning sample (condition) embeddings jointly with cell prototypes
Just need fast immune labels, no integration	CellTypist (Immune_All_Low, majority_voting=True)	Calibrated classifier, no embedding needed; QC the query first
Multimodal reference (CITE-seq/ATAC)	Azimuth/Seurat WNN or totalVI+scArches	Anchor framework natively weights modalities
Considering scGPT/Geneformer	Only if fine-tuning with labels, or cross-modality/species, or data too scarce	Zero-shot foundation embeddings are not a justified default for same-tissue transfer
De novo clustering, no reference, or manual marker annotation	-> single-cell/cell-annotation, single-cell/clustering	Out of scope here

scArches Surgery: Embedding (scVI)

Goal: Project query cells into a pre-trained reference latent space without retraining on combined data.

Approach: Align query genes to the reference exactly, load into the frozen reference model, and fine-tune only query-specific parameters with zero weight decay so the shared manifold does not drift.

import scvi
import scanpy as sc

ref_model = scvi.model.SCVI.load('reference_model/')          # saved with save_anndata=True (or minified)

adata_query = sc.read_h5ad('query.h5ad')
# Align genes to the reference EXACTLY: zero-pad missing, reorder. Mandatory and silent if skipped.
scvi.model.SCVI.prepare_query_anndata(adata_query, 'reference_model/')

query_model = scvi.model.SCVI.load_query_data(adata_query, 'reference_model/')
# weight_decay=0.0 + frozen reference weights make surgery a query-only fine-tune;
# non-zero decay drifts the shared latent and breaks cross-query comparability.
query_model.train(max_epochs=200, plan_kwargs={'weight_decay': 0.0}, check_val_every_n_epoch=10)
adata_query.obsm['X_scVI'] = query_model.get_latent_representation()

scANVI Label Transfer

Goal: Transfer reference cell-type labels to an unlabeled query.

Approach: Build a semi-supervised scANVI head on the reference, map the query by surgery, then read hard labels and per-class probabilities -- treating the probability as "which label," not "does it belong."

# Reference side (once): scANVI from a trained scVI model. unlabeled_category is REQUIRED.
ref_scanvi = scvi.model.SCANVI.from_scvi_model(ref_vae, unlabeled_category='Unknown', labels_key='cell_type')
ref_scanvi.train(max_epochs=20, n_samples_per_label=100)
ref_scanvi.save('ref_scanvi/', save_anndata=True)

# Query side (surgery):
scvi.model.SCANVI.prepare_query_anndata(adata_query, 'ref_scanvi/')
query_scanvi = scvi.model.SCANVI.load_query_data(adata_query, 'ref_scanvi/')
query_scanvi.train(max_epochs=100, plan_kwargs={'weight_decay': 0.0})

adata_query.obs['predicted_label'] = query_scanvi.predict()          # hard labels
adata_query.obsm['X_scANVI'] = query_scanvi.get_latent_representation()
proba = query_scanvi.predict(soft=True)                             # per-class probabilities (which label)

Out-of-Distribution Gating (the step that makes labels trustworthy)

Goal: Decide whether each query cell belongs to the reference, separately from which label it would get.

Approach: Compute a distance/entropy signal on the shared latent. The canonical scArches/HLCA approach is a weighted-kNN label-transfer uncertainty (neighbor disagreement in the reference latent), thresholded at 0.2 to set cells to "Unknown." A portable kNN-entropy version is shown; the softmax proba is NOT this signal.

import numpy as np
from sklearn.neighbors import KNeighborsClassifier

ref_latent = ref_scanvi.get_latent_representation()                  # reference cells in latent
knn = KNeighborsClassifier(n_neighbors=15, weights='distance').fit(ref_latent, adata_ref.obs['cell_type'])
query_latent = adata_query.obsm['X_scANVI']

neighbor_proba = knn.predict_proba(query_latent)                    # weighted neighbor label distribution
# Uncertainty = 1 - max neighbor agreement. HLCA sets cells above 0.2 to 'Unknown'.
uncertainty = 1.0 - neighbor_proba.max(axis=1)
adata_query.obs['transfer_uncertainty'] = uncertainty
adata_query.obs.loc[uncertainty > 0.2, 'predicted_label'] = 'Unknown'   # gate, do not trust ungated labels
print(f'Flagged Unknown: {(uncertainty > 0.2).mean():.1%}')

Per-Method Failure Modes

scANVI / kNN -- forcing the query onto reference labels

• Trigger: Query contains a population absent from the reference (novel type, disease state, perturbed program).
• Mechanism: The classifier/kNN assigns every query cell to its nearest reference label; there is no "none of the above" unless added.
• Symptom: A coherent novel cluster split across 2-3 reference labels, each with high probability; the UMAP looks "well integrated."
• Fix: Always gate on transfer uncertainty / OOD distance (above). Inspect query-only clusters for marker genes independent of transferred labels.

Softmax overconfidence vs label-transfer uncertainty conflated

• Trigger: Reporting "confidence" as the predict(soft=True) max.
• Mechanism: The softmax measures which reference label conditional on belonging; it is normalized away from distance and cannot say "far from everything." A cell can be 0.99 "T cell" and be a hepatocyte.
• Symptom: Pipeline filters on softmax >= 0.5 and still passes OOD cells.
• Fix: Threshold the weighted-kNN uncertainty (HLCA 0.2) or a Mahalanobis/ensemble OOD signal for the "Unknown" decision; use the softmax only to disambiguate among in-distribution labels.

scANVI -- semi-supervised label leakage / latent carving

• Trigger: scANVI reference where labels strongly drive latent geometry; trajectory or novel-state query.
• Mechanism: The classifier head back-propagates label structure into the latent, carving it to separate reference types; query cells are pulled toward that structure even when their biology lies between/outside it.
• Symptom: A query continuum (differentiation trajectory) collapses onto discrete reference clusters; intermediate states vanish.
• Fix: For trajectory/novel-state queries prefer unsupervised scVI surgery, annotate the query independently, and cross-check against the scVI latent.

Reference composition / missing-biology bias

• Trigger: Reference from healthy/limited donors; query from disease, different age, ancestry, or tissue region.
• Mechanism: The reference manifold is the entire hypothesis space; off-manifold cells are projected onto the nearest on-manifold point and rare reference populations act as attractors.
• Symptom: Disease-specific states labeled as the closest healthy type; ancestry/age effects read as "batch."
• Fix: Audit reference composition before mapping; prefer references covering the query's expected biology; treat mapping as hypothesis generation and validate query findings de novo; consider extending the reference (treeArches).

Query QC artifacts laundered into confident labels

• Trigger: Query not QC'd to the reference's standard (empty droplets, ambient RNA, doublets, high-MT).
• Mechanism: A doublet sits between two reference types and maps to a spurious "intermediate"; ambient RNA shifts profiles toward the dominant type.
• Symptom: Artifactual "transitional" populations; doublet clusters labeled as rare real types.
• Fix: Run full query QC before mapping (single-cell/doublet-detection, ambient correction, MT/count filters matched to the reference). Mapping does not clean data.

Feature-space / gene-set mismatch

• Trigger: Query missing reference HVGs; different gene annotation/version; prepare_query_anndata skipped.
• Mechanism: The encoder expects the exact reference gene order; missing genes are zero-padded and reordering silently corrupts the input.
• Symptom: Garbage latent, everything maps to one blob, or a silent accuracy cliff (no error raised).
• Fix: Always prepare_query_anndata(query, reference_model); verify the shared-gene fraction; too few shared HVGs is a hard stop.

Good integration metrics, wrong labels

• Trigger: Judging mapping by scIB integration scores alone.
• Mechanism: Integration metrics reward query/reference mixing; mixing OOD cells into the wrong neighborhood raises the batch-removal score, and bio-conservation uses reference labels (circular for the query).
• Symptom: Top scIB total score with biologically wrong annotation.
• Fix: Integration metrics validate the embedding, not labels. Evaluate transfer on held-out labeled query cells (per-type F1, especially rare types), OOD detection on spiked-in unseen types, and marker-gene sanity checks.

Zero-shot foundation-model embedding as a mapper

• Trigger: Using scGPT/Geneformer zero-shot embeddings for clustering/transfer expecting SOTA.
• Mechanism: The masked-gene pretraining objective does not guarantee a label- or batch-aware embedding; zero-shot embeddings are not batch-corrected.
• Symptom: Worse AvgBio/integration than scVI or even HVG-PCA + Harmony.
• Fix: Fine-tune with task labels, or use an established mapper; reserve foundation models for cross-modality/species/data-scarce cases (Kedzierska 2025).

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
scANVI label confident but kNN uncertainty high	OOD cell forced onto nearest label	Trust the uncertainty; set Unknown and inspect markers
Symphony Mahalanobis flags OOD but scANVI does not	scANVI latent carved to absorb the cell	Prefer the distance-based flag; novel biology likely
popV members disagree	Genuine ambiguity or granularity mismatch	Route to manual review; report the disagreement, do not force a leaf
High scIB score, poor per-type F1 on held-out labels	Embedding mixes well but labels wrong	Believe the F1; integration score is not a label metric

Quantitative Thresholds

Threshold	Source	Rationale
Transfer uncertainty > 0.2 -> "Unknown"	Sikkema 2023 (HLCA)	Weighted-kNN neighbor-disagreement cutoff bounding false labels; recalibrate per reference
Surgery weight_decay=0.0, ~100-200 epochs	scvi-tools scArches tutorial	Frozen reference weights + no decay keep the shared latent fixed
scIB total = 0.6bio + 0.4batch	Luecken 2022	Benchmark weighting; scores the embedding, NOT query labels
CellTypist input = log1p of CP10k	CellTypist docs	Wrong normalization silently degrades accuracy

Common Errors

Error / symptom	Cause	Solution
Everything maps to one blob	prepare_query_anndata skipped; gene mismatch	Run it before load_query_data; check shared-gene fraction
OOD cells pass a 0.5 softmax filter	Thresholding the wrong quantity	Gate on weighted-kNN uncertainty / OOD distance, not softmax
Reference cells move between runs	Non-zero weight_decay or over-training in surgery	Set weight_decay=0.0, keep freeze_* defaults, modest epochs
load_query_data errors on labels	scANVI needs unlabeled_category	Pass it to from_scvi_model; query labels filled with that category
CellTypist labels look random	Raw or wrongly normalized counts	Feed log1p CP10k input

References

• Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. 2018. Deep generative modeling for single-cell transcriptomics. Nat Methods 15:1053-1058.
• Xu C, Lopez R, Mehlman E, Regier J, Jordan MI, Yosef N. 2021. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol Syst Biol 17:e9620.
• Lotfollahi M, Naghipourfar M, Luecken MD, et al. 2022. Mapping single-cell data to reference atlases by transfer learning. Nat Biotechnol 40:121-130.
• Kang JB, Nathan A, Weinand K, et al. 2021. Efficient and precise single-cell reference atlas mapping with Symphony. Nat Commun 12:5890.
• Hao Y, Hao S, Andersen-Nissen E, et al. 2021. Integrated analysis of multimodal single-cell data. Cell 184:3573-3587.
• De Donno C, Hediyeh-Zadeh S, Moinfar AA, et al. 2023. Population-level integration of single-cell datasets enables multi-scale analysis across samples. Nat Methods 20:1683-1692.
• Ergen C, Xing G, Xin C, et al. 2024. Consensus prediction of cell type labels in single-cell data with popV. Nat Genet 56:2731-2738.
• Dominguez Conde C, Xu C, Jarvis LB, et al. 2022. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376:eabl5197.
• Michielsen L, Lotfollahi M, Strobl D, et al. 2023. Single-cell reference mapping to construct and extend cell-type hierarchies. NAR Genom Bioinform 5:lqad070.
• Luecken MD, Buttner M, Chaichoompu K, et al. 2022. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods 19:41-50.
• Sikkema L, Ramirez-Suastegui C, Strobl DC, et al. 2023. An integrated cell atlas of the lung in health and disease. Nat Med 29:1563-1577.
• Kedzierska KZ, Crawford L, Amini AP, Lu AX. 2025. Zero-shot evaluation reveals limitations of single-cell foundation models. Genome Biol 26:101.

Related Skills

• single-cell/preprocessing - QC, normalization, and HVG selection the query needs before mapping
• single-cell/cell-annotation - De novo clustering and manual marker annotation when there is no reference
• single-cell/batch-integration - Integrating datasets without a labeled reference
• single-cell/doublet-detection - Removing doublets that map to spurious intermediates
• differential-expression/de-results - Pseudobulk validation of mapping-derived populations