Package index • Shennong

Data and Import

marker_genes: Pan-Immune CellTypist Metadata
hom_genes: Human-Mouse Homologous Genes Table
pbmc_small: Small Built-In PBMC Seurat Object
pbmc_small_raw: Small Built-In PBMC Raw Counts Matrix
shennong_gene_annotations: Bundled Shennong Gene Annotations
shennong_signature_catalog: Bundled Shennong Signature Catalog
sn_load_data(): Load example datasets from Zenodo
sn_read() .import.rio_bpcells() .import.rio_10x() .import.rio_starsolo() .import.rio_h5ad() .import.rio_h5() .import.rio_gmt(): Read tabular and bioinformatics file formats
sn_write() .export.rio_bpcells() .export.rio_h5ad() .export.rio_h5(): Write tabular and bioinformatics file formats
sn_add_data_from_anndata(): Add metadata and embeddings exported from AnnData

sn_get_species(): Retrieve or infer species information
sn_initialize_seurat_object(): Initialize a Seurat object with optional QC metrics
sn_standardize_gene_symbols(): Standardize gene symbols in a count matrix or Seurat object
sn_normalize_data(): Normalize data in a Seurat object
sn_score_cell_cycle(): Score Cell Cycle Phases
sn_filter_genes(): Filter genes based on the number of expressing cells
sn_filter_cells(): Filter cells in a Seurat object based on QC metrics
sn_find_doublets(): Find doublets using scDblFinder
sn_remove_ambient_contamination(): Remove ambient RNA contamination from counts

sn_run_cluster(): Run clustering for a single dataset or Harmony integration workflow

sn_assess_integration(): Assess integration quality across multiple metrics
sn_detect_rare_cells(): Detect rare cells with native or optional rare-cell backends
sn_calculate_lisi(): Calculate LISI scores from a Seurat embedding
sn_calculate_silhouette(): Calculate silhouette widths from a Seurat embedding
sn_calculate_graph_connectivity(): Calculate graph connectivity for a grouping label
sn_calculate_pcr_batch(): Calculate PCR batch effect scores
sn_calculate_clustering_agreement(): Calculate agreement between clusters and reference labels
sn_calculate_isolated_label_score(): Calculate isolated-label preservation scores
sn_calculate_cluster_purity(): Calculate cluster purity against a reference label
sn_calculate_cluster_entropy(): Calculate cluster entropy for a categorical label
sn_identify_challenging_groups(): Identify rare or difficult-to-separate groups
sn_calculate_rogue(): Calculate ROGUE score for Seurat Object

sn_run_celltypist(): Run CellTypist for automated cell type annotation
sn_find_de(): Run differential expression analysis on a Seurat object
sn_enrich(): Run gene set enrichment analysis
sn_list_signatures(): List bundled Shennong signatures
sn_get_signatures(): Retrieve bundled Shennong signature genes by category or tree path
sn_add_signature(): Add a signature to the editable source registry
sn_update_signature(): Update a signature in the editable source registry
sn_delete_signature(): Delete a signature from the editable source registry

sn_calculate_composition(): Calculate composition proportions
sn_deconvolve_bulk(): Run bulk RNA-seq deconvolution with single-cell references
sn_set_cibersortx_credentials(): Store local CIBERSORTx credentials for container execution
sn_store_deconvolution(): Store a deconvolution result on a Seurat object
sn_get_deconvolution_result(): Retrieve a stored deconvolution result from a Seurat object

sn_list_results(): List stored Shennong analysis and interpretation results on a Seurat object
sn_get_de_result(): Retrieve a stored DE result from a Seurat object
sn_get_enrichment_result(): Retrieve a stored enrichment result from a Seurat object
sn_get_interpretation_result(): Retrieve a stored interpretation result from a Seurat object
sn_store_enrichment(): Store an enrichment result on a Seurat object
sn_prepare_annotation_evidence(): Prepare cluster-annotation evidence from a Seurat object
sn_prepare_de_evidence(): Prepare differential-expression evidence from a stored DE result
sn_prepare_enrichment_evidence(): Prepare enrichment evidence
sn_prepare_results_evidence(): Prepare manuscript-style results evidence
sn_build_prompt(): Build an LLM prompt from structured Shennong evidence
sn_run_llm(): Run an LLM provider on a prepared message list
sn_interpret_annotation(): Interpret cluster markers for cell-type annotation
sn_interpret_de(): Interpret a stored differential-expression result
sn_interpret_enrichment(): Interpret a stored enrichment result
sn_write_results(): Write a manuscript-style results summary from stored analysis outputs
sn_write_figure_legend(): Write a figure legend from stored analysis outputs
sn_write_presentation_summary(): Write a presentation-style summary from stored analysis outputs

sn_plot_dim(): Create a dimensionality reduction plot for categorical data
sn_plot_feature(): Plot feature expression in reduced dimensions
sn_plot_violin(): Plot a violin plot with categorical groups
sn_plot_dot(): Plot a dot plot with categorical groups
sn_plot_boxplot(): Create a boxplot from a data frame
sn_plot_barplot(): Create a bar plot from a data frame
show_all_palettes(): List available color palettes

sn_check_version(): Check whether Shennong is up to date
sn_install_shennong(): Install Shennong from CRAN or GitHub
sn_check_file(): Check if files exist
sn_get_codex_skill_path(): Return installed Shennong Codex asset paths
sn_initialize_project(): Initialize a Shennong analysis project
sn_initialize_codex_project(): Initialize Codex-style project guidance for a Shennong analysis
sn_install_codex_skill(): Install the bundled Shennong Codex skill for end-user agents
sn_set_path(): Create a directory path if needed