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Skillscell-type-annotation

cell-type-annotation

Data & analytics

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Updated 10-15 17:23

About this skill

Cell Type Annotation

Overview

This pipeline skill generates code for annotating cell types in single-cell RNA-seq data. It covers four complementary approaches:

  1. Automated — CellTypist (logistic-regression classifiers trained on curated atlases).
  2. Reference-based — SingleR (R) or scANVI/sc.tl.ingest (Python) label transfer.
  3. Marker-based — Manual assignment from canonical marker gene panels.
  4. Hierarchical — Coarse compartment assignment followed by specialized fine-grained annotation.

Use it when the user has a clustered AnnData (or SingleCellExperiment) and needs cell-type labels in adata.obs.

Usage

Decision Tree: Choosing an Annotation Approach

Follow this decision tree to select the right method:

  1. Is there a CellTypist model matching your tissue and species?
    • YES → Use CellTypist (Steps 1–6 below)
    • NO → Go to step 2
  2. Do you have a labeled reference AnnData from the same tissue?
    • YES, same batch/technology → Use sc.tl.ingest (Step 12)
    • YES, different batches → Use scANVI label transfer (Step 13)
    • NO → Go to step 3
  3. Are you working in R with bulk or sorted-cell references?
    • YES → Use SingleR (Steps 7–9)
    • NO → Go to step 4
  4. Do you have canonical marker gene panels for expected cell types?
    • YES → Use marker-based annotation (Steps 10–11)
    • NO → Start with CellTypist's broadest model (Immune_All_Low.pkl or Developing_Human_Brain.pkl), then refine with markers

Annotation Workflow (15 Steps)

Step 1 — Confirm normalization. CellTypist requires log1p(CP10k). Check adata.uns['log1p'] exists or run normalization. If data is in adata.raw or a layer, subset appropriately.

Step 2 — Verify gene symbols. Confirm adata.var_names are HGNC (human) or MGI (mouse) symbols, not Ensembl IDs. Convert if needed with sc.queries.biomart_annotations.

Step 3 — Select CellTypist model. Match species + tissue. Use celltypist.models.models_description() to list options. If unsure, start with the broadest model for the compartment.

Step 4 — Run CellTypist annotation. Use celltypist.annotate() with majority_voting=True and appropriate p_thres (default 0.5; raise to 0.7 for stringent calls).

Step 5 — Extract results via to_adata(). Call predictions.to_adata() to get annotations in .obs. Access confidence via adata_result.obs['conf_score'].

Step 6 — Filter low-confidence cells. Mask cells with conf_score < 0.5 (or user-specified threshold) as 'Unknown'.

Step 7 — (SingleR) Install references. Use BiocManager::install("celldex") and load the appropriate reference dataset.

Step 8 — (SingleR) Run prediction. Call SingleR(test, ref, labels) with assay.type.test = "logcounts".

Step 9 — (SingleR) Prune low-quality calls. Use pruneScores(pred) to identify unreliable assignments; set those to "Unknown".

Step 10 — (Marker-based) Define panels. Provide ≥3 markers per cell type from literature. Include negative markers where informative.

Step 11 — (Marker-based) Score and assign. Use sc.pl.dotplot to visualize, then map clusters to types. Leave ambiguous clusters as 'Unknown'.

Step 12 — (Ingest) Project query onto reference. Requires shared PCA space; run sc.tl.ingest(adata_query, adata_ref, obs='cell_type'). Only works well when query and reference share the same technology and minimal batch effects.

Step 13 — (scANVI) Semi-supervised transfer. Train SCVI on reference, extend to SCANVI with labels, then load_query_data for the query. Handles batch effects natively.

Step 14 — Validate annotations. Confirm with dotplot of canonical markers grouped by assigned cell type. Check per-sample proportions for biological plausibility.

Step 15 — Hierarchical refinement. If fine labels are unstable, annotate coarse compartments first (immune/stromal/epithelial via PTPRC/EPCAM/COL1A1), subset, then re-annotate each with a specialized model or markers.

Branch Points

  • At Step 4: if >30% of cells get conf_score < 0.5, the model is a poor fit → switch to marker-based or try a different model.
  • At Step 6: if a single label dominates >80% of cells unexpectedly, suspect wrong model or un-normalized data.
  • At Step 12: if query UMAP doesn't align with reference after ingest, batch effects are too strong → switch to scANVI (Step 13).
  • At Step 14: if proportions are biologically implausible (e.g., 60% fibroblasts in PBMC), re-examine normalization and model choice.

Response Format

  • Lead with the command or code the user needs — explain after.
  • Structure as: confirm inputs → working code → key parameters explained → gotchas.
  • One complete working example per task; do not show every alternative.
  • Keep code comments minimal and functional.
  • Target: 50–100 lines of code with brief surrounding explanation.
  • Always include confidence score extraction and a filtering step.

Core Concepts

1. CellTypist (automated, Python)

import scanpy as sc
import celltypist
from celltypist import models

# Input: log1p-normalized to 10,000 counts/cell
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

# Download models (first run only)
models.download_models()
print(models.models_description())  # list available models

# Load tissue-matched model
model = models.Model.load(model='Immune_All_Low.pkl')

# Annotate with majority voting
predictions = celltypist.annotate(
    adata,
    model=model,
    majority_voting=True,       # smooth labels over neighborhood graph
    over_clustering='leiden',   # obs key for majority voting resolution
    p_thres=0.5,               # minimum probability threshold (raise for stringency)
)

# Extract results — modern API (CellTypist >=1.3)
adata_result = predictions.to_adata()
# Annotations now in adata_result.obs:
#   'predicted_labels'        — per-cell raw predictions
#   'majority_voting'         — smoothed labels
#   'conf_score'              — max probability per cell

# Transfer to original adata
adata.obs['cell_type'] = adata_result.obs['majority_voting']
adata.obs['cell_type_conf'] = adata_result.obs['conf_score']

# Filter low-confidence assignments
adata.obs.loc[adata.obs['cell_type_conf'] < 0.5, 'cell_type'] = 'Unknown'

Key parameters for celltypist.annotate():

Parameter Default Effect
majority_voting False Smooth labels over kNN graph; always enable
over_clustering None obs key (e.g., 'leiden') for voting resolution; finer clusters → better resolution
p_thres 0.5 Min probability to assign a label; raise to 0.7 for high-confidence only
mode 'best match' Use 'prob match' for multi-label scenarios

Common models: Immune_All_Low, Immune_All_High, Healthy_COVID19_PBMC, Human_Lung_Atlas, Cells_Intestinal_Tract, Developing_Human_Brain, Adult_Mouse_Gut. Match species and tissue.

2. SingleR (reference-based, R)

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("SingleR", "celldex", "SingleCellExperiment"))

library(SingleR)
library(celldex)
library(SingleCellExperiment)

# Choose reference matching species/tissue
ref <- celldex::HumanPrimaryCellAtlasData()
# ref <- celldex::MonacoImmuneData()       # sorted human immune
# ref <- celldex::ImmGenData()             # mouse immune
# ref <- celldex::BlueprintEncodeData()    # human stromal + immune

pred <- SingleR(
    test = sce,
    ref = ref,
    labels = ref$label.main,
    assay.type.test = "logcounts"
)

sce$cell_type <- pred$labels
table(pred$labels)

# Prune low-confidence calls (delta < median - 3*MAD)
sce$cell_type[is.na(pred$pruned.labels)] <- "Unknown"

3. Marker-based (manual, Python)

import scanpy as sc

marker_genes = {
    'T cells':      ['CD3D', 'CD3E', 'CD2'],
    'CD4 T':        ['CD4', 'IL7R'],
    'CD8 T':        ['CD8A', 'CD8B'],
    'B cells':      ['CD79A', 'MS4A1', 'CD19'],
    'NK cells':     ['NKG7', 'GNLY', 'KLRD1'],
    'Monocytes':    ['CD14', 'LYZ', 'S100A8'],
    'Dendritic':    ['FCER1A', 'CST3', 'CLEC10A'],
    'Fibroblasts':  ['COL1A1', 'DCN', 'COL3A1'],
    'Endothelial':  ['PECAM1', 'VWF', 'CDH5'],
    'Epithelial':   ['EPCAM', 'KRT18', 'KRT19'],
}

sc.pl.dotplot(adata, marker_genes, groupby='leiden', standard_scale='var')

# Assign after reviewing the dotplot
cluster_to_celltype = {
    '0': 'T cells', '1': 'Monocytes', '2': 'B cells',
    '3': 'NK cells', '4': 'Unknown',
}
adata.obs['cell_type'] = adata.obs['leiden'].map(cluster_to_celltype).astype('category')

4. Label transfer from a reference atlas

# Quick path: sc.tl.ingest (PCA/UMAP projection, kNN labels)
# Only use when batch effects are minimal
import scanpy as sc
sc.pp.pca(adata_ref)
sc.pp.neighbors(adata_ref)
sc.tl.umap(adata_ref)
sc.tl.ingest(adata_query, adata_ref, obs='cell_type')

# Robust path: scANVI (semi-supervised, handles batch)
import scvi
scvi.model.SCVI.setup_anndata(adata_ref, batch_key='batch')
scvi_model = scvi.model.SCVI(adata_ref)
scvi_model.train()

scvi.model.SCANVI.setup_anndata(
    adata_ref, labels_key='cell_type', unlabeled_category='Unknown', batch_key='batch'
)
scanvi_model = scvi.model.SCANVI.from_scvi_model(scvi_model, unlabeled_category='Unknown')
scanvi_model.train(max_epochs=20)

scanvi_model = scvi.model.SCANVI.load_query_data(adata_query, scanvi_model)
scanvi_model.train(max_epochs=100, plan_kwargs={'weight_decay': 0.0})
adata_query.obs['cell_type_pred'] = scanvi_model.predict(adata_query)

5. Validation

# Confirm assigned labels express their markers
sc.pl.dotplot(adata, marker_genes, groupby='cell_type', standard_scale='var')
sc.pl.violin(adata, ['CD3D', 'MS4A1', 'CD14'], groupby='cell_type', rotation=90)

# Sanity-check proportions per sample
adata.obs.groupby(['sample', 'cell_type']).size().unstack(fill_value=0)

Mark any cluster without clear marker support as 'Unknown' rather than forcing a label.

6. Hierarchical annotation

# Step 1: coarse compartments
compartment_markers = {
    'Immune':     ['PTPRC'],           # CD45
    'Epithelial': ['EPCAM', 'KRT8'],
    'Stromal':    ['COL1A1', 'PECAM1'],
}
# Step 2: subset and re-annotate each compartment
adata_immune = adata[adata.obs['compartment'] == 'Immune'].copy()
model = models.Model.load('Immune_All_Low.pkl')
preds = celltypist.annotate(adata_immune, model=model, majority_voting=True)
adata_immune_result = preds.to_adata()
adata_immune.obs['cell_type_fine'] = adata_immune_result.obs['majority_voting']

Quick Reference

Task Tool Key call
PBMC / immune automated CellTypist models.Model.load('Immune_All_Low.pkl')
Lung atlas mapping CellTypist 'Human_Lung_Atlas.pkl'
Gut / intestine CellTypist 'Cells_Intestinal_Tract.pkl'
Broad human ref (R) SingleR celldex::HumanPrimaryCellAtlasData()
Sorted immune ref (R) SingleR celldex::MonacoImmuneData()
Mouse immune (R) SingleR celldex::ImmGenData()
Query→reference transfer scanpy sc.tl.ingest(adata_q, adata_ref, obs='cell_type')
Batch-aware transfer scvi-tools SCANVI.load_query_data(...)
Marker review scanpy sc.pl.dotplot(adata, markers, groupby='leiden')
Confidence (CellTypist) CellTypist predictions.to_adata().obs['conf_score']
Confidence (SingleR) SingleR is.na(pred$pruned.labels) flags low-quality

Required inputs:

  • CellTypist: adata.X = log1p(CP10k) normalized, genes as HGNC symbols in adata.var_names.
  • SingleR: SingleCellExperiment with a logcounts assay.
  • scANVI: raw counts in adata.layers['counts'], batch_key, labels_key on reference.

Numeric Thresholds

Metric Threshold Action
CellTypist conf_score < 0.5 Label as 'Unknown'
CellTypist conf_score < 0.7 Flag for manual review
CellTypist p_thres 0.5 (default) Raise to 0.7 for stringent annotation
SingleR delta score < median - 3×MAD Pruned (set to NA)
Cluster size < 20 cells Do not assign rare subtypes; use parent label
Marker dotplot fraction < 0.3 Marker not reliably expressed; do not use for assignment
Expected proportion deviation > 3× literature range Suspect wrong model or normalization error

Common Mistakes

  1. Wrong: Feeding raw counts to CellTypist Right: Always run sc.pp.normalize_total(adata, target_sum=1e4) + sc.pp.log1p(adata) before annotation Why: CellTypist expects log1p of counts normalized to 10,000/cell — raw or SCTransform'd data produces nonsense labels

  2. Wrong: Using a reference model that doesn't match the tissue or species Right: Match species (Human_* vs Mouse_*) and tissue type when selecting CellTypist models or SingleR references Why: Using Immune_All_Low on epithelial tumor cells or HumanPrimaryCellAtlasData on mouse data produces confident but wrong labels

  3. Wrong: Passing Ensembl IDs or mixed-case gene symbols to CellTypist/SingleR Right: Convert adata.var_names to HGNC (human) or MGI (mouse) symbols before annotation Why: Ensembl IDs or wrong case (Cd3d vs CD3D) silently drop features, degrading model performance without warning

  4. Wrong: Using predictions.probability_matrix.max(axis=1) to get confidence scores Right: Use predictions.to_adata().obs['conf_score'] which is the stable API since CellTypist ≥1.3 Why: The probability matrix approach is fragile and version-dependent — it may break or return incorrect values across updates

  5. Wrong: Assigning a rare subtype label to a cluster of fewer than 20 cells Right: Collapse to the parent cell type or label as 'Unknown' when cluster size is below 20 cells Why: Rare subtype assignments on tiny clusters lack statistical power and are unreliable without strong distinguishing markers

  6. Wrong: Forcing a label on clusters that lack clear markers after dotplot review Right: Keep ambiguous clusters as 'Unknown' rather than picking the next-best guess Why: Forced labels propagate errors into downstream analyses (DE, trajectory) and create false biological narratives

  7. Wrong: Running CellTypist with majority_voting=False and using per-cell labels directly Right: Always enable majority_voting=True to smooth labels over the local neighborhood graph Why: Per-cell predictions are noisy; majority voting leverages cluster structure to produce coherent, stable annotations

  8. Wrong: Running sc.tl.ingest for label transfer without batch correction Right: Integrate query and reference first, or use scANVI's load_query_data path which handles batch natively Why: ingest assumes query and reference share the same embedding — batch effects cause misalignment and wrong label transfers

  9. Wrong: Not validating cell-type proportions against biological expectations Right: Always cross-check per-sample cell-type proportions against known tissue composition Why: A PBMC sample with 80% fibroblasts is a red flag indicating wrong model, normalization error, or data quality issues

  10. Wrong: Annotating at one resolution only without hierarchical refinement Right: Annotate coarse compartments first (immune/stromal/epithelial), then refine each with specialized models or markers Why: Fine labels on poorly separated clusters are unstable — hierarchical annotation produces more reliable results

References