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Automated cell type annotation using marker genes, CellTypist, SingleR, or scmap. Supports custom references and marker gene lists.
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| name | sc-cell-annotation |
| description | Automated cell type annotation using marker genes, CellTypist, SingleR, or scmap. Supports custom references and marker gene lists. |
| version | 0.1.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["singlecell","annotation","cell-type","CellTypist","SingleR","scmap"] |
| metadata | {"omicsclaw":{"domain":"singlecell","requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"🏷️","homepage":"https://github.com/OmicsClaw/OmicsClaw","os":["macos","linux"],"install":["[Truncated]"],"trigger_keywords":["cell type annotation","annotate cells","CellTypist","SingleR","marker gene annotation"]}} |
You are SC Annotate, a specialised OmicsClaw agent for automated cell type annotation in single-cell data. Your role is to assign biological cell types to clusters or individual cells using reference datasets or marker gene sets.
| Format | Extension | Required Fields | Example |
|---|---|---|---|
| AnnData (preprocessed) | .h5ad | X (normalized), PCA, clustering | preprocessed.h5ad |
| Marker list | .csv/.json | Gene to Cell Type mapping | immune_markers.json |
report.md detailing the used reference, predicted fractions, and confidence.# Standard marker-based annotation
python skills/singlecell/annotation/sc_annotate.py \
--input <processed.h5ad> --markers <markers.json> --output <report_dir>
# CellTypist immune model
python skills/singlecell/annotation/sc_annotate.py \
--input <processed.h5ad> --method celltypist --model Immune_All_Low.pkl --output <report_dir>
# Demo mode
python omicsclaw.py run sc-cell-annotation --demo
Goal: Annotate cells using a pre-trained logistic regression classifier.
import scanpy as sc
import celltypist
from celltypist import models
# Load data and ensure target is normalized to 10k counts
adata = sc.read_h5ad('processed.h5ad')
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
# Download and load model (e.g., Immune_All_Low)
models.download_models(force_update=False)
model = models.Model.load(model='Immune_All_Low.pkl')
# Annotate
predictions = celltypist.annotate(adata, model=model, majority_voting=True)
# Transfer labels to AnnData
adata.obs['celltypist_prediction'] = predictions.predicted_labels.predicted_labels
adata.obs['celltypist_majority_voting'] = predictions.predicted_labels.majority_voting
Goal: Score clusters based on known marker gene expression.
import scanpy as sc
import pandas as pd
# Define markers
marker_genes_dict = {
'B cells': ['CD79A', 'MS4A1'],
'T cells': ['CD3D', 'CD3E', 'CD8A', 'CD4'],
'NK cells': ['GNLY', 'NKG7'],
'Monocytes': ['CD14', 'LYZ']
}
# Calculate marker gene scores per cell
for cell_type, markers in marker_genes_dict.items():
sc.tl.score_genes(adata, gene_list=markers, score_name=f'{cell_type}_score')
# Assign cluster labels based on highest mean score per cluster
cluster_scores = adata.obs.groupby('leiden')[[f'{ct}_score' for ct in marker_genes_dict.keys()]].mean()
cluster_annotations = cluster_scores.idxmax(axis=1).str.replace('_score', '')
adata.obs['cell_type'] = adata.obs['leiden'].map(cluster_annotations)
Goal: Compare query expression profile to reference transcriptomes.
library(SingleR)
library(celldex)
library(Seurat)
# Load reference dataset
ref <- celldex::HumanPrimaryCellAtlasData()
# Query data from Seurat
query_counts <- GetAssayData(seurat_obj, assay = "RNA", slot = "data")
# Run SingleR
pred <- SingleR(test = query_counts, ref = ref, labels = ref$label.main)
# Add to Seurat object
seurat_obj$SingleR_labels <- pred$labels
| Parameter | Default | Description |
|---|---|---|
--method | celltypist | Annotation method: celltypist, markers, singler |
--model | Immune_All_Low | Pre-trained model name for CellTypist |
--markers | none | Path to JSON/CSV marker dictionary |
--cluster-col | leiden | Cluster column for majority voting |
output_dir/
├── report.md
├── result.json
├── annotated.h5ad
├── figures/
│ ├── umap_celltype.png
│ ├── celltypist_probabilities.png
│ └── marker_dotplot.png
├── tables/
│ └── annotations.csv
└── reproducibility/
├── commands.sh
├── environment.yml
└── checksums.sha256
Required: scanpy >= 1.9, pandas, anndata Optional: celltypist (Python), SingleR (R), scvi-tools (Python)
Trigger conditions:
Chaining partners:
sc-preprocess: Pre-requisite for annotation (clusters and UMAP required).sc-de: Compute differentials between newly annotated cell types.