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Here is doc for scanpy-mcp, but you can refer to scanpy docs at https://scanpy.readthedocs.io/

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IO Module

  • read: Read data from various sources (10X directory, h5ad files, 10x files, text files)
  • write: Save AnnData object to a file

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Preprocessing Module

  • subset_cells: Filter or subset cells based on total genes expressed counts, number of cells, or values in adata.obs
  • subset_genes: Filter or subset genes based on number of cells, counts, or values in adata.var
  • calculate_qc_metrics: Calculate quality control metrics (total counts, gene number, mitochondrial genes)
  • log1p: Logarithmize the data matrix
  • normalize_total: Normalize counts per cell to the same total count
  • highly_variable_genes: Annotate highly variable genes in the dataset
  • regress_out: Regress out unwanted sources of variation
  • scale: Scale the data to unit variance and zero mean
  • combat: Perform batch effect correction using ComBat
  • scrublet: Detect and remove doublets using Scrublet
  • neighbors: Compute a neighborhood graph of observations

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Tools Module

  • tsne: Perform t-distributed stochastic neighborhood embedding (t-SNE) for visualization
  • umap: Perform Uniform Manifold Approximation and Projection (UMAP) for visualization
  • draw_graph: Perform force-directed graph drawing
  • diffmap: Compute Diffusion Maps for dimensionality reduction
  • embedding_density: Calculate the density of cells in an embedding
  • leiden: Perform Leiden clustering algorithm for community detection
  • louvain: Perform Louvain clustering algorithm for community detection
  • dendrogram: Compute hierarchical clustering dendrogram
  • dpt: Perform Diffusion Pseudotime (DPT) analysis
  • paga: Perform Partition-based graph abstraction
  • ingest: Map labels and embeddings from reference to query data
  • rank_genes_groups: Rank genes for characterizing groups
  • filter_rank_genes_groups: Filter ranked genes groups
  • marker_gene_overlap: Compute overlap between marker genes
  • score_genes: Score genes based on their expression
  • score_genes_cell_cycle: Score genes based on cell cycle phase
  • pca: Perform Principal Component Analysis

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Plotting Module

  • pca: Create a scatter plot in PCA coordinates
  • diffmap: Plot diffusion map embedding of cells
  • violin: Create violin plots of one or more variables
  • stacked_violin: Create stacked violin plots for compact visualization
  • heatmap: Create a heatmap of gene expression values
  • dotplot: Create a dot plot of expression values per gene for each group
  • matrixplot: Create a heatmap of mean expression values per group
  • tracksplot: Create a compact plot of gene expression
  • scatter: Create a scatter plot of two variables
  • embedding: Create a scatter plot for user-specified embedding basis (e.g., UMAP, t-SNE)
  • embedding_density: Plot the density of cells in an embedding
  • rank_genes_groups: Plot ranking of genes based on differential expression
  • rank_genes_groups_dotplot: Create a dot plot of ranked genes groups
  • clustermap: Create a hierarchical clustering heatmap
  • highly_variable_genes: Plot highly variable genes
  • pca_variance_ratio: Plot PCA variance ratio