bio-applied-cell-type-annotation

name	bio-applied-cell-type-annotation
description	Annotate scRNA-seq Leiden/Louvain clusters into cell types via canonical marker scoring, SingleR reference correlation, and CellTypist logistic-regression classification on an AnnData/SingleCellExperiment object. Use when doing cell type annotation, labeling clusters, scoring marker genes on a UMAP, running SingleR or CellTypist, or resolving ambiguous/NA-labeled clusters in PBMC or other scRNA-seq data.
tool_type	python
primary_tool	scanpy

scRNA-seq: Cell Type Annotation

When to Use

• You have Leiden/Louvain clusters (e.g. from sc.tl.leiden) and need biological labels, not just cluster numbers.
• You want to score canonical marker genes (CD3D, MS4A1, LYZ, ...) per cluster and assign the best-matching cell type.
• You need automated, reference-based annotation with SingleR (R/Bioconductor) or CellTypist (Python) instead of manual marker curation.
• Clusters come back ambiguous, mixed, or NA/low-confidence and you need a strategy to resolve them.
• You need to subcluster a broad population (e.g. "T cells") to resolve finer subtypes (CD4/CD8/Treg).

Version Compatibility

• scanpy ≥ 1.10, anndata ≥ 0.10, Python ≥ 3.10
• celltypist ≥ 1.6 (model files Immune_All_Low.pkl, Immune_All_High.pkl, Pan_Fetal_Human.pkl)
• R ≥ 4.3, SingleR ≥ 2.4, celldex ≥ 1.12 (Bioconductor 3.18+)

Prerequisites

• pip install scanpy anndata celltypist (Python) or BiocManager::install(c("SingleR", "celldex", "SingleCellExperiment")) (R)
• Data must already be QC'd, normalized (sc.pp.normalize_total(target_sum=1e4) + sc.pp.log1p), and clustered — see bio-applied-scrna-preprocessing and bio-applied-dimensionality-reduction for those steps.

Canonical PBMC Markers

Cell Type	Marker Genes	Notes
T cells	CD3D, CD3E, CD3G	Pan-T marker, present in all T subsets
CD4+ T cells	CD4, IL7R	Helper T
CD8+ T cells	CD8A, CD8B	Cytotoxic T
B cells	CD79A, CD79B, MS4A1 (CD20)	MS4A1 is the rituximab target
NK cells	GNLY, NKG7, KLRD1	No CD3 — distinguishes from T cells
Monocytes (classical)	LYZ, S100A8, S100A9, CST3	High LYZ = phagocytic
Monocytes (non-classical)	FCGR3A (CD16), MS4A7	Patrolling
Dendritic cells	FCER1A, CLEC10A	Low count, high HLA-DR
Platelets	PPBP, PF4	Often ambient/contaminant signal

Manual Marker-Based Annotation

Goal: map each Leiden cluster to a cell type using mean expression of canonical marker sets. Approach: for every candidate cell type, take the marker genes present in adata.var_names, average their log-normalized expression within each cluster, and assign the cell type with the highest score. Requires ≥2 concordant markers to trust a call; flag clusters where the top two scores are close (ambiguous / possible doublet population).

import numpy as np
import pandas as pd


def annotate_clusters_by_markers(adata, marker_dict, cluster_key="leiden"):
    """Score each cluster against marker gene sets and assign the top-scoring cell type.

    Parameters
    ----------
    adata : AnnData, log1p-normalized (target_sum=1e4)
    marker_dict : dict[str, list[str]] mapping cell type -> marker gene names
    cluster_key : obs column with cluster labels

    Returns
    -------
    score_df : clusters x cell types mean-expression table
    annotation : dict mapping cluster id -> assigned cell type
    """
    X = adata.X if not hasattr(adata.X, "toarray") else adata.X.toarray()
    gene_names = adata.var_names.tolist()
    clusters = adata.obs[cluster_key].values
    cluster_ids = sorted(adata.obs[cluster_key].unique())

    scores = {}
    for cell_type, markers in marker_dict.items():
        valid = [m for m in markers if m in gene_names]
        if not valid:
            continue
        marker_idx = [gene_names.index(m) for m in valid]
        scores[cell_type] = {cid: X[clusters == cid][:, marker_idx].mean() for cid in cluster_ids}

    score_df = pd.DataFrame(scores, index=cluster_ids)
    annotation = score_df.idxmax(axis=1).to_dict()

    # flag ambiguous clusters: top two scores within 15% of each other
    for cid in cluster_ids:
        row = score_df.loc[cid].sort_values(ascending=False)
        if len(row) >= 2 and row.iloc[] >  * row.iloc[]:
            (
                  )

     score_df, annotation


marker_dict = {
    : [, , ],
    : [, ],
    : [, ],
    : [, ],
    : [, ],
}

score_df, cluster_annotation = annotate_clusters_by_markers(adata, marker_dict)
adata.obs[] = adata.obs[].(cluster_annotation)

SingleR (R, Reference-Based)

Goal: assign per-cell labels by Spearman-correlating each cell's expression profile against reference cell-type profiles, with iterative fine-tuning. Approach: load a curated reference from celldex, run SingleR() against your SingleCellExperiment, then inspect pruned.labels for low-confidence (NA) calls.

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

# Reference options:
# HumanPrimaryCellAtlasData() - 37 cell types, diverse human tissues
# MonacoImmuneData()          - 29 immune subtypes, best for PBMC
# BlueprintEncodeData()       - hematopoietic and epithelial lineages
ref <- MonacoImmuneData()

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

plotScoreHeatmap(pred)
table(pred$pruned.labels)  # NA = low confidence -- inspect these cells

sce$cell_type <- pred$pruned.labels

pred$pruned.labels NA cells may be novel cell types not in the reference, transitional/intermediate states, or poor-quality cells that survived QC.

CellTypist (Python, Automated Classification)

Goal: classify cells with a logistic-regression model pretrained on a curated multi-million-cell human atlas. Approach: normalize exactly as CellTypist expects (target_sum=1e4, log1p), load a model, and run annotate with majority_voting=True to pool per-cluster predictions and suppress single-cell noise.

import celltypist
from celltypist import models

models.download_models(force_update=False)
# Immune_All_Low.pkl  -- 36 immune subtypes (fine)
# Immune_All_High.pkl -- 9 broad immune categories
# Pan_Fetal_Human.pkl -- fetal cell types across organs
model = models.Model.load(model="Immune_All_Low.pkl")

# IMPORTANT: expects log1p-normalized data with target_sum=1e4
# (i.e. sc.pp.normalize_total(adata, target_sum=1e4); sc.pp.log1p(adata))
predictions = celltypist.annotate(adata, model=model, majority_voting=True)
adata = predictions.to_adata()

print(adata.obs["majority_voting"].value_counts())

majority_voting=True pools per-cell predictions within each Leiden cluster and assigns the plurality label to the whole cluster, reducing noise from individual misclassified cells.

When CellTypist fails: non-immune tissue (neurons, hepatocytes) needs a tissue-specific model; non-human species need retraining; if every cluster gets the same label, check normalization (target_sum=1e4 + log1p, not raw counts).

Subclustering for Refined Annotation

Goal: resolve subtypes (e.g. CD4/CD8/Treg) hidden inside one broad annotated population. Approach: subset to one annotated cell type, rerun neighbors/leiden/umap on just that subset, then re-annotate with subtype-specific markers. Never compare subcluster UMAP coordinates to the full-dataset UMAP — they were computed on different variance.

import scanpy as sc

t_cells = adata[adata.obs["cell_type_annotated"] == "T_cell"].copy()
sc.pp.neighbors(t_cells, n_neighbors=10, n_pcs=10)
sc.tl.leiden(t_cells, resolution=0.8, key_added="leiden_tcell")
sc.tl.umap(t_cells)

# CD4+ naive: CD4, CCR7, SELL | Treg: CD4, FOXP3, IL2RA
# CD8+ cytotoxic: CD8A, GZMB, PRF1 | CD8+ exhausted: CD8A, PDCD1, HAVCR2
sc.pl.umap(t_cells, color=["CD4", "CD8A", "FOXP3", "leiden_tcell"])

Pitfalls

• Trusting a single marker: require ≥2 concordant markers per cluster; one gene can be noisy or ambient RNA contamination.
• Wrong normalization for CellTypist: it expects target_sum=1e4 log1p data — raw counts or a different target_sum collapse all clusters to one prediction.
• Ignoring pruned.labels == NA (SingleR): these are not errors to discard, they flag novel types, transitional states, or low-quality cells worth inspecting.
• Comparing UMAPs across subclustering runs: a subcluster's PCA/UMAP is fit on different variance than the full dataset; coordinates are not comparable.
• Batch effects masquerading as cell types: always confirm a cluster isn't just a batch before assigning a novel biological label — see bio-single-cell-batch-integration.
• Doublet clusters: a cluster co-expressing two unrelated marker sets (e.g. T-cell and monocyte markers) is often a doublet population, not a real hybrid cell type.

See Also

• bio-applied-scrna-preprocessing — QC and normalization steps required before clustering/annotation
• bio-applied-dimensionality-reduction — PCA/UMAP/Leiden clustering that produces the clusters annotated here
• bio-applied-single-cell-scanpy — general scanpy workflow this skill plugs into
• bio-applied-cite-seq-integration — protein-level markers (CITE-seq) to corroborate RNA-based annotation