git:20260531.6e617c4 to git:20260607.d890519
169 added, 91 removed. Audit A to A.
---
name: bio-machine-learning-atlas-mapping
- description: Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.
+ 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+
+ 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
- If code throws ImportError, AttributeError, or TypeError, introspect the installed
- package and adapt the example to match the actual API rather than retrying.
-
- # Transfer Learning for Single-Cell Data
-
- **"Map my scRNA-seq data onto a reference atlas"** -> Transfer cell type labels from a pre-trained reference model to query cells using architectural surgery (scArches) without retraining.
- - Python: `scvi.model.SCVI.load_query_data()` -> `get_latent_representation()` -> `scanpy.tl.ingest()`
+ 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.
- ## scVI Reference Mapping (scArches)
+ # Reference Mapping and Label Transfer for Single-Cell Data
- **Goal:** Map query single-cell data onto a pre-trained reference model to obtain a shared latent embedding.
+ **"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
- **Approach:** Load a pre-trained scVI model, prepare query data with matching gene sets, then perform surgical fine-tuning that updates only query-specific parameters.
+ ## The Single Most Important Modern Insight -- Mapping Is a Projection, Not an Annotation Oracle
- ```python
- import scvi
- import scanpy as sc
+ 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.
- # Load pre-trained reference model
- adata_ref = sc.read_h5ad('reference.h5ad')
- # Model must have been saved with save_anndata=True
- scvi.model.SCVI.setup_anndata(adata_ref, layer='counts', batch_key='batch')
- ref_model = scvi.model.SCVI.load('reference_model/', adata=adata_ref)
+ 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.
- # Prepare query data
- adata_query = sc.read_h5ad('query.h5ad')
- # Subset to reference genes
- adata_query = adata_query[:, adata_ref.var_names].copy()
+ ## Methods Taxonomy
- # Set up query AnnData using reference setup
- scvi.model.SCVI.prepare_query_anndata(adata_query, ref_model)
+ | 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) |
- # Load query into model (creates "surgical" fine-tuned model)
- query_model = scvi.model.SCVI.load_query_data(adata_query, ref_model)
+ 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.
- # Surgical training: update only query-specific parameters
- # weight_decay=0.0: Standard for surgery; prevents reference drift
- query_model.train(max_epochs=200, plan_kwargs={'weight_decay': 0.0})
+ ## Decision Tree by Scenario
- # Get latent representation
- adata_query.obsm['X_scVI'] = query_model.get_latent_representation()
- ```
+ | 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 |
- ## scANVI for Label Transfer
+ ## scArches Surgery: Embedding (scVI)
- **Goal:** Transfer cell type labels from a labeled reference atlas to unlabeled query data.
+ **Goal:** Project query cells into a pre-trained reference latent space without retraining on combined data.
- **Approach:** Train a semi-supervised scANVI model on the reference, then map query cells via surgical fine-tuning and predict labels with confidence scores.
+ **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.
```python
import scvi
import scanpy as sc
- # Reference with cell type labels
- adata_ref = sc.read_h5ad('reference_labeled.h5ad')
- scvi.model.SCVI.setup_anndata(adata_ref, layer='counts', batch_key='batch')
- ref_vae = scvi.model.SCVI(adata_ref, n_latent=30)
- ref_vae.train(max_epochs=100)
-
- # Convert to scANVI (semi-supervised)
- scvi.model.SCANVI.setup_anndata(adata_ref, layer='counts', batch_key='batch', labels_key='cell_type', unlabeled_category='Unknown')
- ref_scanvi = scvi.model.SCANVI.from_scvi_model(ref_vae, labels_key='cell_type', unlabeled_category='Unknown')
- ref_scanvi.train(max_epochs=50)
- ref_scanvi.save('reference_scanvi/')
+ ref_model = scvi.model.SCVI.load('reference_model/') # saved with save_anndata=True (or minified)
- # Map query data
adata_query = sc.read_h5ad('query.h5ad')
- adata_query = adata_query[:, adata_ref.var_names].copy()
+ # 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/')
- scvi.model.SCANVI.prepare_query_anndata(adata_query, ref_scanvi)
- query_scanvi = scvi.model.SCANVI.load_query_data(adata_query, ref_scanvi)
+ 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."
+
+ ```python
+ # 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})
- # Transfer labels
- adata_query.obs['predicted_cell_type'] = query_scanvi.predict()
+ 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)
```
- ## Prediction Confidence
+ ## Out-of-Distribution Gating (the step that makes labels trustworthy)
- **Goal:** Assess reliability of transferred labels and flag cells that may represent novel types.
+ **Goal:** Decide *whether* each query cell belongs to the reference, separately from which label it would get.
- **Approach:** Extract soft prediction probabilities from the scANVI model and identify low-confidence assignments below a threshold.
+ **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.
```python
- # Get prediction probabilities
- soft_predictions = query_scanvi.predict(soft=True)
- adata_query.obs['prediction_confidence'] = soft_predictions.max(axis=1)
+ import numpy as np
+ from sklearn.neighbors import KNeighborsClassifier
- # Flag low-confidence predictions
- # confidence < 0.5: May be novel cell type or poor mapping
- low_conf = adata_query.obs['prediction_confidence'] < 0.5
- print(f'Low confidence predictions: {low_conf.sum()} ({low_conf.mean():.1%})')
+ 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%}')
```
- ## Joint Embedding Visualization
+ ## Per-Method Failure Modes
- **Goal:** Visualize reference and query cells together to assess integration quality.
+ ### 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.
- **Approach:** Concatenate reference and query datasets, compute UMAP from the shared latent representation, and color by dataset and cell type.
+ ### 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.
- ```python
- import scanpy as sc
+ ### 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.
- # Combine reference and query for visualization
- adata_combined = adata_ref.concatenate(adata_query, batch_key='dataset', batch_categories=['reference', 'query'])
+ ### 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).
- # Use latent space for neighbors/UMAP
- sc.pp.neighbors(adata_combined, use_rep='X_scVI')
- sc.tl.umap(adata_combined)
- sc.pl.umap(adata_combined, color=['dataset', 'cell_type'], save='_transfer.png')
- ```
+ ### 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.
- ## Pre-trained Reference Atlases
+ ### 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.
- | Atlas | Model | URL |
- |-------|-------|-----|
- | Human Lung Cell Atlas | scANVI | cellxgene.cziscience.com |
- | Tabula Sapiens | scVI | tabula-sapiens-portal.ds.czbiohub.org |
- | Mouse Cell Atlas | scVI | bis.zju.edu.cn/MCA |
+ ### 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.
- ## Training Parameters
+ ### 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).
- | Parameter | Surgical | Full Retrain | Notes |
- |-----------|----------|--------------|-------|
- | weight_decay | 0.0 | 0.001 | 0.0 preserves reference |
- | max_epochs | 100-200 | 200-400 | Less for surgery |
- | early_stopping | True | True | Prevents overfitting |
+ ## 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 = 0.6*bio + 0.4*batch | 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/cell-annotation - Manual annotation methods
- - single-cell/batch-integration - Batch effect correction
- - single-cell/preprocessing - Data preparation before transfer
+ - 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