CellAgent
An LLM-driven agent for single-cell data analysis, ensuring high-quality results with minimal effort.
An LLM-driven agent for single-cell data analysis, ensuring high-quality results with minimal effort.
1. Quality Control
2. Normalization
3. Identification of Highly Variable Genes
4. Dimensionality Reduction
5. Clustering
6. Cell Type Annotation
# Filter based on the properties of current dataset and empirical thresholds
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata.var['mt'] = adata.var_names.str.startswith('MT-')
# Calculate quality control metrics
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)
# Filter cells with less than 2500 genes and mitochondrial gene percentage less than 5%
adata = adata[adata.obs.n_genes_by_counts < 2500, :]
adata = adata[adata.obs.pct_counts_mt < 5, :]
# Other steps omitted
sc.tl.pca(adata, svd_solver='arpack')
# For this dataset, n_neighbors=10 and n_pcs=40 show clear results
sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)
sc.tl.leiden(adata)
sc.tl.umap(adata)
sc.pl.umap(adata, color=['leiden'])
# Find differentially expressed genes for each cluster
sc.tl.rank_genes_groups(adata, 'leiden', method='t-test')
sc.pl.rank_genes_groups(adata, n_genes=20, sharey=False)
#After comparing the differentially expressed genes in each cluster, and under the guidance of experts, cell type labels were assigned to each cluster.
cluster_annotations = {
'0': 'T cells','1': 'B cells','2': 'NK cells','3': 'Monocytes','4': 'Dendritic cells'...
}
adata.obs['cell_type'] = adata.obs['leiden'].map(cluster_annotations)
CellAgent can streamline your single-cell data analysis workflow, allowing you to obtain high-quality results without the need for complex coding. Whether you are a domain expert or a novice, our online platform enables effortless data analysing and interpretation. With CellAgent, you can:
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