Package 'unitcm'

Description

Return compound-target pairs (keyed by InChIKey + UniProt ID) supported by interaction records in at least min_sources source databases. Optionally extends each pair with DrugCLIP / SEA prediction support.

Usage

aggregated_target2np(
  search = NULL,
  target_organism = NULL,
  min_sources = 2L,
  include_predictions = FALSE,
  page = 1L,
  page_size = 20L
)

Arguments

Value

A tibble::tibble() of aggregated pairs with attribute "total".

Examples

## Not run: 
aggregated_target2np(search = "quercetin")
aggregated_target2np(search = "TP53", min_sources = 3,
                     include_predictions = TRUE)

## End(Not run)

Description

Convert a NetVis graph response to igraph

Usage

as_igraph(graph_response)

Arguments

Value

An igraph graph object.

Examples

## Not run: 
resp <- get_neighbors("H:UNITCM_H001")
g <- as_igraph(resp)

## End(Not run)

Description

Convert a NetVis graph response to tidygraph

Usage

as_tidygraph(graph_response)

Arguments

Value

A tidygraph::tbl_graph object.

Examples

## Not run: 
resp <- get_neighbors("H:UNITCM_H001")
tg <- as_tidygraph(resp)

## End(Not run)

Description

Search for disease names with autocomplete. Query must be at least 2 characters.

Usage

autocomplete_disease(q)

Arguments

Value

A tibble::tibble() with columns: disease_name, disease_id, gene_count.

Examples

## Not run: 
autocomplete_disease("breast")

## End(Not run)

Description

Look up interaction records for up to 50 gene symbols, UniProt IDs, or Entrez gene IDs in one call.

Usage

batch_target2np(
  identifiers,
  id_type = c("gene_symbol", "uniprot_id", "entrez_gene_id")
)

Arguments

Value

A tibble::tibble() of matching records with attributes "total", "queries_matched", and "queries_not_found".

Examples

## Not run: 
batch_target2np(c("TP53", "BRCA1", "EGFR"))
batch_target2np(c("P04637", "P38398"), id_type = "uniprot_id")

## End(Not run)

Description

Given a formula order ID, fetches its herb doses and constructs a star-topology network.

Usage

build_formula_herb_network(formula_id)

Arguments

Value

An igraph graph object with vertex attributes name, type ("formula", "herb"), label, and dose (for herbs).

Examples

## Not run: 
g <- build_formula_herb_network(1)
igraph::V(g)$label

## End(Not run)

Description

Given herb names or IDs, fetches herb-compound-target data from the API and constructs a typed igraph network.

Usage

build_hct_network(
  herbs,
  target_method = "drugclip",
  max_compounds = 50L,
  progress = TRUE
)

Arguments

Value

An igraph graph object with vertex attributes name, type ("herb", "compound", "target"), and label.

Examples

## Not run: 
g <- build_hct_network(c("UNITCM_H001", "UNITCM_H002"))
igraph::vcount(g)

## End(Not run)

Description

Removes the API key from session memory and optionally from the system keyring.

Usage

clear_api_key(keyring = FALSE)

Arguments

Value

Invisible NULL.

Examples

## Not run: 
clear_api_key()

## End(Not run)

Description

Removes the token from session memory and optionally from the system keyring.

Usage

clear_unitcm_token(keyring = FALSE)

Arguments

Value

Invisible NULL.

Examples

## Not run: 
clear_unitcm_token()

## End(Not run)

Description

Convert a mixed list of gene identifiers (symbols, Entrez IDs, Ensembl IDs) to a standardized mapping.

Usage

convert_gene_ids(identifiers)

Arguments

Value

A tibble::tibble() with columns including match status.

Examples

## Not run: 
convert_gene_ids(c("TP53", "7157", "ENSG00000141510"))

## End(Not run)

Description

POST a graph (nodes + edges) to the server for community detection.

Usage

detect_communities(nodes, edges)

Arguments

Value

A tibble::tibble() with columns node_id and community_id.

Examples

## Not run: 
detect_communities(
  nodes = c("A", "B", "C"),
  edges = data.frame(source = c("A", "B"), target = c("B", "C"))
)

## End(Not run)

Description

Download a CSV of a specific data module for one compound.

Usage

export_compound_module(
  id,
  module = c("overview", "physicochemical", "admet", "targets"),
  file = NULL
)

Arguments

Value

Invisible file path.

Examples

## Not run: 
export_compound_module("UNITCM_I00001", "admet")

## End(Not run)

Description

Download a CSV export of compounds matching the given filters (max 10,000 rows).

Usage

export_compounds(
  q = NULL,
  mw_min = NULL,
  mw_max = NULL,
  clogp_min = NULL,
  clogp_max = NULL,
  tpsa_min = NULL,
  tpsa_max = NULL,
  qed_min = NULL,
  qed_max = NULL,
  ring_count_min = NULL,
  ring_count_max = NULL,
  lipinski = NULL,
  is_drug = NULL,
  sort = NULL,
  file = "compounds_export.csv"
)

Arguments

Value

Invisible file path.

Examples

## Not run: 
export_compounds(mw_min = 200, file = "filtered_compounds.csv")

## End(Not run)

Description

Download a CSV export of datasets matching the given filters.

Usage

export_datasets(
  q = NULL,
  tcm = NULL,
  omics = NULL,
  source = NULL,
  organism = NULL,
  tissue = NULL,
  disease = NULL,
  repo = NULL,
  year_min = NULL,
  year_max = NULL,
  sort = NULL,
  file = "datasets_export.csv"
)

Arguments

Value

Invisible file path.

Examples

## Not run: 
export_datasets(omics = "Transcriptomics", file = "transcriptomics.csv")

## End(Not run)

Description

Download a CSV export of all compounds for a specific herb.

Usage

export_herb_compounds(herb_id, file = "herb_compounds_export.csv")

Arguments

Value

Invisible file path.

Examples

## Not run: 
export_herb_compounds("UNITCM_H001")

## End(Not run)

Description

Download a CSV export of herbs matching the given filters.

Usage

export_herbs(
  q = NULL,
  therapeutic_en = NULL,
  therapeutic_cn = NULL,
  family = NULL,
  toxicity = NULL,
  source = NULL,
  flavors = NULL,
  properties = NULL,
  meridians = NULL,
  medicinal_part = NULL,
  file = "herbs_export.csv"
)

Arguments

Value

Invisible file path.

Examples

## Not run: 
export_herbs(q = "ginseng", file = "ginseng_herbs.csv")

## End(Not run)

Description

Download the full ontology in JSON, OWL/RDF, or CSV format.

Usage

export_ontology(format = c("json", "owl", "csv"), file = NULL, depth = 4L)

Arguments

Value

Invisible file path.

Examples

## Not run: 
export_ontology("csv")
export_ontology("json", depth = 2, file = "ontology_shallow.json")

## End(Not run)

Description

Returns summary statistics and filter option counts for the Ingredient Explorer.

Usage

fetch_compound_facets()

Value

A named list with fields: total, approved_count, lipinski_counts, drug_counts, mw_range, clogp_range, tpsa_range, qed_range.

Examples

## Not run: 
fetch_compound_facets()

## End(Not run)

Description

Returns available filter values and their counts for the TCMomics database.

Usage

fetch_dataset_facets()

Value

A named list of tibbles for each facet field.

Examples

## Not run: 
facets <- fetch_dataset_facets()
facets$omics_type

## End(Not run)

Description

Get TCMomics database statistics

Usage

fetch_dataset_stats()

Value

A named list with fields: total_datasets, total_downloads, omics_types_count, unique_organisms.

Examples

## Not run: 
fetch_dataset_stats()

## End(Not run)

Description

Returns the full 4-level ICD-11 disease classification tree used by the Disease-Formula Atlas.

Usage

fetch_disease_tree()

Value

A recursive nested list with structure list(label, count, children = list(...)).

Examples

## Not run: 
tree <- fetch_disease_tree()
names(tree[[1]])

## End(Not run)

Description

Returns available filter values and their counts for the Herb Explorer.

Usage

fetch_herb_facets()

Value

A named list of tibbles, one per facet field (e.g. therapeutic_en_class, family, toxicity).

Examples

## Not run: 
facets <- fetch_herb_facets()
facets$toxicity

## End(Not run)

Description

Get homepage statistics

Usage

fetch_home_stats()

Value

A named list with fields: total_datasets, total_downloads, total_file_size, recent_submissions_count.

Examples

## Not run: 
fetch_home_stats()

## End(Not run)

Description

Get latest submissions

Usage

fetch_latest_submissions()

Value

A tibble::tibble() of recent submissions with columns: submission_id, project_title, submitted_by, updated_at, institution, total_file_size.

Examples

## Not run: 
fetch_latest_submissions()

## End(Not run)

Description

Fetches all 6 filter option endpoints and returns them as a named list of tibbles.

Usage

fetch_mechanism_filters()

Value

A named list with elements: categories, omics_types, evidence_levels, confidence_levels, study_types, species. Each is a tibble::tibble() with columns value, label, count.

Examples

## Not run: 
filters <- fetch_mechanism_filters()
filters$categories

## End(Not run)

Description

List all available gene-disease association databases.

Usage

fetch_midas_sources()

Value

A tibble::tibble() with columns: key, label, has_score, weight, row_count.

Examples

## Not run: 
fetch_midas_sources()

## End(Not run)

Description

Get MIDAS statistics

Usage

fetch_midas_stats()

Value

A named list with fields: total_associations, total_genes, total_diseases, sources.

Examples

## Not run: 
fetch_midas_stats()

## End(Not run)

Description

Get NetVis network statistics

Usage

fetch_netvis_stats()

Value

A named list with node counts (formula, herb, compound, target, disease) and edges sub-list.

Examples

## Not run: 
fetch_netvis_stats()

## End(Not run)

Description

Get omics type statistics

Usage

fetch_omics_type_stats()

Value

A tibble::tibble() with columns: omics_type, count, percentage.

Examples

## Not run: 
fetch_omics_type_stats()

## End(Not run)

Description

Fetch ontology statistics

Usage

fetch_ontology_stats()

Value

A named list with fields: total_entities, total_level1– total_level4, total_relations, total_mappings, categories.

Examples

## Not run: 
fetch_ontology_stats()

## End(Not run)

Description

Returns the full ontology as a recursive nested list.

Usage

fetch_ontology_tree(depth = 4L)

Arguments

Value

A recursive nested list: list(tcm_id, name, name_cn, level, children = list(...)).

Examples

## Not run: 
tree <- fetch_ontology_tree(depth = 2)

## End(Not run)

Description

Returns the controlled values used by the Target2NP filter UI (source databases, evidence labels, top target organisms, interaction types, and top activity types).

Usage

fetch_target2np_filters()

Value

A named list with fields source_db, evidence_label, target_organism, interaction_type, activity_type.

Examples

## Not run: 
opts <- fetch_target2np_filters()
opts$source_db

## End(Not run)

Description

Returns counts and distributions across source databases, evidence levels, target organisms, and activity types.

Usage

fetch_target2np_stats()

Value

A named list with total_records and four distribution lists.

Examples

## Not run: 
stats <- fetch_target2np_stats()
stats$total_records

## End(Not run)

Description

Get TCM classification statistics

Usage

fetch_tcm_classification_stats()

Value

A tibble::tibble() with columns: classification, count, percentage.

Examples

## Not run: 
fetch_tcm_classification_stats()

## End(Not run)

Description

Get transcriptome filter options

Usage

fetch_transcriptome_filters()

Value

A named list of character vectors for each filter field.

Examples

## Not run: 
filters <- fetch_transcriptome_filters()
filters$organism

## End(Not run)

Description

Get Transcriptome Hub statistics

Usage

fetch_transcriptome_stats()

Value

A named list with fields: total_datasets, total_organisms, total_tcm_entities, total_analysis_modules, plus distribution data.

Examples

## Not run: 
fetch_transcriptome_stats()

## End(Not run)

Description

Find shortest path between two nodes

Usage

find_path(source, target, max_depth = 4L)

Arguments

Value

A named list with ⁠$nodes⁠ (tibble::tibble()) and ⁠$edges⁠ (tibble::tibble()).

Examples

## Not run: 
find_path("H:UNITCM_H001", "T:TP53")

## End(Not run)

Description

Recursively flattens a nested list into a single-row tibble. Nested lists that cannot be further flattened are kept as list-columns.

Usage

flatten_response(x)

Arguments

Value

A single-row tibble::tibble().

Examples

## Not run: 
herb <- get_herb("UNITCM_H001")
flatten_response(herb)

## End(Not run)

Description

Retrieve data for a specific analysis module. Return type varies by module.

Usage

get_analysis_data(dataset_id, module, gene = NULL)

Arguments

Value

A tibble::tibble() for tabular modules (deg, go, kegg, gsea, immune, tf), or a named list for structured modules (meta, expression, ppi, pca, qc).

Examples

## Not run: 
get_analysis_data("TCMtrans00001", "deg")
get_analysis_data("TCMtrans00001", "expression", gene = "TP53")

## End(Not run)

Description

List available analysis modules for a dataset

Usage

get_analysis_modules(dataset_id)

Arguments

Value

A character vector of available module names.

Examples

## Not run: 
get_analysis_modules("TCMtrans00001")

## End(Not run)

Description

Checks in order: (1) session value set via set_api_key(), (2) environment variable UNITCM_API_KEY, (3) system keyring.

Usage

get_api_key()

Value

A character string, or NULL if no API key is found.

Examples

## Not run: 
get_api_key()

## End(Not run)

Description

Checks in order: (1) session value set via set_base_url(), (2) option unitcm.base_url, (3) environment variable UNITCM_BASE_URL, (4) hardcoded default.

Usage

get_base_url()

Value

A character string.

Examples

## Not run: 
get_base_url()

## End(Not run)

Description

Retrieve full detail for one compound including cross-references.

Usage

get_compound(id)

Arguments

Value

A named list with 26+ fields including an xref sub-list.

Examples

## Not run: 
get_compound("UNITCM_I00001")

## End(Not run)

Description

Returns ~90 ADMET endpoint predictions as a single-row wide tibble.

Usage

get_compound_admet(id)

Arguments

Value

A single-row tibble::tibble() with ~90 ADMET columns.

Examples

## Not run: 
get_compound_admet("UNITCM_I00001")

## End(Not run)

Description

List herbs that contain a specific compound.

Usage

get_compound_herbs(id, page = 1L, page_size = 20L, all_pages = FALSE)

Arguments

Value

A tibble::tibble() of herbs with attribute "total".

Examples

## Not run: 
get_compound_herbs("UNITCM_I00001")

## End(Not run)

Description

Retrieve target predictions from DrugCLIP, ChEMBL similarity search, or both.

Usage

get_compound_targets(
  id,
  method = c("drugclip", "chembl", "both"),
  page = 1L,
  page_size = 20L
)

Arguments

Value

A tibble::tibble() of targets. When method = "both", a source column is added to distinguish results.

Examples

## Not run: 
get_compound_targets("UNITCM_I00001")
get_compound_targets("UNITCM_I00001", method = "both")

## End(Not run)

Description

Retrieve full detail including nested persons, publications, grants, and data files.

Usage

get_dataset(submission_id)

Arguments

Value

A named list with nested sub-lists for persons, publications, grants, and data_files.

Examples

## Not run: 
get_dataset("TMA2025001")

## End(Not run)

Description

Retrieve full detail for one formula from the Disease-Formula Atlas.

Usage

get_formula(order_id)

Arguments

Value

A named list with 30+ fields.

Examples

## Not run: 
get_formula(1)

## End(Not run)

Description

Retrieve the composition and dosage information for a specific formula.

Usage

get_formula_doses(order_id)

Arguments

Value

A tibble::tibble() with columns: id, herb_name, original_dose, composition_ratio, modern_dose_g, clinical_ref_dose_g, dynasty, notes, herb_ids.

Examples

## Not run: 
get_formula_doses(1)

## End(Not run)

Description

Retrieve full detail for one herb from the Herb Explorer.

Usage

get_herb(herb_id)

Arguments

Value

A named list with 31 fields including cross-reference IDs.

Examples

## Not run: 
get_herb("UNITCM_H001")

## End(Not run)

Description

List chemical compounds (ingredients) associated with a specific herb.

Usage

get_herb_compounds(herb_id, page = 1L, page_size = 20L, all_pages = FALSE)

Arguments

Value

A tibble::tibble() of compounds with attribute "total".

Examples

## Not run: 
get_herb_compounds("UNITCM_H001")

## End(Not run)

Description

Retrieve full detail for one term including nested arrays of biomarkers, pathways, gene targets, metabolites, etc.

Usage

get_mechanism(term_id)

Arguments

Value

A named list with ~50 fields. Nested arrays (e.g. biomarkers, signaling_pathways, gene_targets) are returned as-is (lists).

Examples

## Not run: 
get_mechanism("TMM001")

## End(Not run)

Description

Get neighbors of a node

Usage

get_neighbors(node_id, depth = 1L, limit = 50L, node_types = NULL)

Arguments

Value

A named list with ⁠$nodes⁠ (tibble::tibble()), ⁠$edges⁠ (tibble::tibble()), and ⁠$has_more⁠.

Examples

## Not run: 
get_neighbors("H:UNITCM_H001", depth = 1)

## End(Not run)

Description

Get node detail

Usage

get_node_detail(node_id)

Arguments

Value

A named list with fields: id, type, label, label_cn, properties, detail_url.

Examples

## Not run: 
get_node_detail("H:UNITCM_H001")

## End(Not run)

Description

Get node metrics

Usage

get_node_metrics(node_id)

Arguments

Value

A named list with fields: node_id, degree, neighbor_types.

Examples

## Not run: 
get_node_metrics("H:UNITCM_H001")

## End(Not run)

Description

Get ancestors of an ontology entity

Usage

get_ontology_ancestors(tcm_id)

Arguments

Value

A tibble::tibble() with columns: tcm_id, name, name_cn, level.

Examples

## Not run: 
get_ontology_ancestors("TCM_0001")

## End(Not run)

Description

Get ontology entities by level

Usage

get_ontology_by_level(level, parent_id = NULL)

Arguments

Value

A tibble::tibble().

Examples

## Not run: 
get_ontology_by_level(2)

## End(Not run)

Description

Get children of an ontology entity

Usage

get_ontology_children(tcm_id)

Arguments

Value

A tibble::tibble() with columns: tcm_id, name, name_cn, level, path, children_count, has_children.

Examples

## Not run: 
get_ontology_children("TCM_0001")

## End(Not run)

Description

Get all descendants of an ontology entity

Usage

get_ontology_descendants(tcm_id, max_level = NULL)

Arguments

Value

A tibble::tibble().

Examples

## Not run: 
get_ontology_descendants("TCM_0001", max_level = 2)

## End(Not run)

Description

Retrieve full detail for one entity including ancestors, children, external mappings, and relations.

Usage

get_ontology_entity(tcm_id)

Arguments

Value

A named list with sub-elements: ancestors, children, external_mappings, relations.

Examples

## Not run: 
get_ontology_entity("TCM_0001")

## End(Not run)

Description

Find datasets similar to a given submission based on content similarity.

Usage

get_similar_datasets(submission_id)

Arguments

Value

A tibble::tibble() with columns: submission_id, project_title, TCM_classification, similarity_score.

Examples

## Not run: 
get_similar_datasets("TMA2025001")

## End(Not run)

Description

Get subgraph for a set of nodes

Usage

get_subgraph(node_ids, limit = 200L)

Arguments

Value

A named list with ⁠$nodes⁠ (tibble::tibble()), ⁠$edges⁠ (tibble::tibble()), and ⁠$has_more⁠.

Examples

## Not run: 
get_subgraph(c("H:UNITCM_H001", "C:UNITCM_I00001"))

## End(Not run)

Description

Retrieve the full detail for one compound-target interaction record.

Usage

get_target2np(record_id)

Arguments

Value

A named list of interaction fields.

Examples

## Not run: 
get_target2np(1)

## End(Not run)

Description

Retrieve full detail for one term from the TCM Bilingual Corpus.

Usage

get_term(term_id)

Arguments

Value

A named list with fields including chinese_name, pinyin, english_name, latin_name, description_english, etc.

Examples

## Not run: 
get_term("TCM_T001")

## End(Not run)

Description

Get a single transcriptome dataset

Usage

get_transcriptome(dataset_id)

Arguments

Value

A named list with 35+ fields.

Examples

## Not run: 
get_transcriptome("TCMtrans00001")

## End(Not run)

Description

Checks in order: (1) session value set via set_unitcm_token(), (2) environment variable UNITCM_TOKEN, (3) system keyring.

Usage

get_unitcm_token()

Value

A character string, or NULL if no token is found.

Examples

## Not run: 
get_unitcm_token()

## End(Not run)

Description

List book sources

Usage

list_book_sources()

Value

A tibble::tibble() with columns value, label, count.

Examples

## Not run: 
list_book_sources()

## End(Not run)

Description

Returns the top 50 dosage forms by frequency.

Usage

list_dosage_forms()

Value

A tibble::tibble() with columns value, label, count.

Examples

## Not run: 
list_dosage_forms()

## End(Not run)

Description

List top-level ontology categories

Usage

list_ontology_categories()

Value

A tibble::tibble() of level-1 entities.

Examples

## Not run: 
list_ontology_categories()

## End(Not run)

Description

Returns the top 50 formula origin sources by frequency.

Usage

list_origin_sources()

Value

A tibble::tibble() with columns value, label, count.

Examples

## Not run: 
list_origin_sources()

## End(Not run)

Description

List term categories

Usage

list_term_categories()

Value

A tibble::tibble() with columns value, label, count.

Examples

## Not run: 
list_term_categories()

## End(Not run)

Description

List term sources

Usage

list_term_sources()

Value

A tibble::tibble() with columns value, label, count.

Examples

## Not run: 
list_term_sources()

## End(Not run)

Description

Create a radar/spider chart of normalized physicochemical properties for a compound.

Usage

plot_compound_radar(
  compound_id,
  properties = c("mw", "clogp", "tpsa", "hbd", "hba", "qed_score")
)

Arguments

Value

A ggplot object.

Examples

## Not run: 
plot_compound_radar("UNITCM_I00001")

## End(Not run)

Description

Create a dot plot or bar plot from enrichment analysis results (e.g. from query_disease_enrichment()).

Usage

plot_enrichment(
  enrichment_result,
  type = c("dotplot", "barplot"),
  top_n = 20L,
  name_col = NULL,
  pvalue_col = NULL,
  count_col = NULL
)

Arguments

Value

A ggplot object.

Examples

## Not run: 
enrich <- query_disease_enrichment(c("TP53", "BRCA1", "EGFR"))
plot_enrichment(enrich)

## End(Not run)

Description

Visualize an igraph or tbl_graph object using ggraph.

Usage

plot_network(
  graph,
  layout = "fr",
  color_by = "type",
  node_size = 3,
  edge_alpha = 0.3,
  show_labels = NULL
)

Arguments

Value

A ggplot object.

Examples

## Not run: 
g <- build_hct_network("UNITCM_H001")
plot_network(g)

## End(Not run)

Description

Perform Fisher's exact test enrichment analysis to identify diseases significantly associated with a gene list.

Usage

query_disease_enrichment(
  gene_list,
  gene_id_type = "symbol",
  sources = NULL,
  min_sources = 1L,
  background_gene_count = 20000L,
  p_value_cutoff = 0.05,
  correction_method = "fdr",
  min_hit_count = 2L,
  page = 1L,
  page_size = 20L
)

Arguments

Value

A tibble::tibble() of enrichment results with attributes "total_significant", "total_tested", and "input_gene_count".

Examples

## Not run: 
query_disease_enrichment(c("TP53", "BRCA1", "EGFR", "VEGFA"))

## End(Not run)

Description

Given a disease query, find associated genes across multiple evidence sources.

Usage

query_disease_genes(
  disease_query,
  disease_id_type = "name",
  sources = NULL,
  min_sources = 1L,
  scoring_method = "max",
  page = 1L,
  page_size = 20L
)

Arguments

Value

A tibble::tibble() of disease-gene associations with attribute "matched_diseases".

Examples

## Not run: 
query_disease_genes("breast cancer")

## End(Not run)

Description

Find genes shared across multiple diseases.

Usage

query_disease_intersection(disease_queries, sources = NULL)

Arguments

Value

A named list with elements: ⁠$diseases⁠, ⁠$per_source⁠, ⁠$targets⁠, ⁠$total_intersection_genes⁠.

Examples

## Not run: 
query_disease_intersection(c("breast cancer", "lung cancer"))

## End(Not run)

Description

Given a list of gene symbols or IDs, find associated diseases across multiple evidence sources.

Usage

query_gene_diseases(
  gene_list,
  gene_id_type = "symbol",
  sources = NULL,
  min_sources = 1L,
  min_score = 0,
  evidence_types = NULL,
  scoring_method = "max",
  page = 1L,
  page_size = 20L
)

Arguments

Value

A tibble::tibble() of gene-disease associations with attribute "gene_mapping" containing the gene ID resolution mapping.

Examples

## Not run: 
query_gene_diseases(c("TP53", "BRCA1"))

## End(Not run)

Description

Compare how different evidence sources cover a gene list, producing Venn-diagram-ready set data.

Usage

query_source_comparison(
  gene_list,
  sources = NULL,
  mode = c("union", "intersection")
)

Arguments

Value

A named list with elements: ⁠$mode⁠, ⁠$sources⁠, ⁠$sets⁠ (named list of gene vectors), ⁠$intersections⁠, ⁠$exclusives⁠, ⁠$genes_used⁠.

Examples

## Not run: 
query_source_comparison(c("TP53", "BRCA1"), mode = "union")

## End(Not run)

Description

Query the UniTCM Ingredient Explorer with optional text search and physicochemical property filters.

Usage

search_compounds(
  q = NULL,
  mw_min = NULL,
  mw_max = NULL,
  clogp_min = NULL,
  clogp_max = NULL,
  tpsa_min = NULL,
  tpsa_max = NULL,
  qed_min = NULL,
  qed_max = NULL,
  ring_count_min = NULL,
  ring_count_max = NULL,
  lipinski = NULL,
  is_drug = NULL,
  sort = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of compounds with attribute "total".

Examples

## Not run: 
search_compounds(q = "quercetin")
search_compounds(mw_min = 200, mw_max = 500, lipinski = "pass")

## End(Not run)

Description

Query the TCMomics multi-omics database with optional text search and faceted filters.

Usage

search_datasets(
  q = NULL,
  tcm = NULL,
  omics = NULL,
  source = NULL,
  organism = NULL,
  tissue = NULL,
  disease = NULL,
  repo = NULL,
  year_min = NULL,
  year_max = NULL,
  sort = NULL,
  search_mode = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of datasets with attribute "total".

Examples

## Not run: 
search_datasets(q = "ginseng", omics = "Transcriptomics")

## End(Not run)

Description

Query the Disease-Formula Atlas with optional text search and ICD-11 disease classification filters. Multi-value parameters accept character vectors and are collapsed to comma-separated strings internally.

Usage

search_formulas(
  q = NULL,
  level1 = NULL,
  level2 = NULL,
  level3 = NULL,
  level4 = NULL,
  book_sources = NULL,
  origin_sources = NULL,
  dosage_forms = NULL,
  mapping_confidence = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of formulas with attribute "total".

Examples

## Not run: 
search_formulas(q = "insomnia")
search_formulas(level1 = "Neoplasms", mapping_confidence = "high")

## End(Not run)

Description

Query the UniTCM Herb Explorer with optional text search and faceted filters. Multi-value filter parameters accept character vectors and are collapsed to semicolon-separated strings internally.

Usage

search_herbs(
  q = NULL,
  therapeutic_en = NULL,
  therapeutic_cn = NULL,
  family = NULL,
  toxicity = NULL,
  source = NULL,
  flavors = NULL,
  properties = NULL,
  meridians = NULL,
  medicinal_part = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of herbs with attribute "total".

Examples

## Not run: 
search_herbs(q = "ginseng")
search_herbs(flavors = c("sweet", "bitter"), page_size = 50)

## End(Not run)

Description

Query the Terms Molecular Mechanisms database with optional filters.

Usage

search_mechanisms(
  search = NULL,
  category = NULL,
  omics_type = NULL,
  evidence_level = NULL,
  confidence_level = NULL,
  study_type = NULL,
  species = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of mechanism terms with attribute "total".

Examples

## Not run: 
search_mechanisms(search = "Qi deficiency")

## End(Not run)

Description

Search NetVis nodes

Usage

search_netvis(q, type = "all", limit = 20L)

Arguments

Value

A tibble::tibble() with columns: id, type, label, label_cn, degree.

Examples

## Not run: 
search_netvis("ginseng", type = "herb")

## End(Not run)

Description

Full-text search across TCM ontology entities.

Usage

search_ontology(q, limit = 20L, level = NULL, category = NULL)

Arguments

Value

A tibble::tibble() with columns: tcm_id, name, name_cn, level, path, match_field, highlight.

Examples

## Not run: 
search_ontology("Qi stagnation")

## End(Not run)

Description

Find TCM entities mapped to an external database identifier.

Usage

search_ontology_mapping(database, external_id)

Arguments

Value

A tibble::tibble() of matched TCM entities.

Examples

## Not run: 
search_ontology_mapping("MeSH", "D008516")

## End(Not run)

Description

Query the UniTCM Target2NP database of natural-product to protein-target interactions, combining records from experimental sources such as BindingDB, HERB2, NPASS, BATMAN, and others.

Usage

search_target2np(
  search = NULL,
  search_field = c("all", "gene_symbol", "compound_name", "uniprot_id", "inchikey",
    "pubchem_cid", "chembl_id"),
  search_mode = c("exact", "fuzzy"),
  source_db = NULL,
  evidence_level = NULL,
  evidence_label = NULL,
  target_organism = NULL,
  interaction_type = NULL,
  activity_type = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of interaction records with attribute "total".

Examples

## Not run: 
search_target2np(search = "quercetin")
search_target2np(search = "TP53", search_field = "gene_symbol",
                 source_db = "BindingDB")

## End(Not run)

Description

Query DrugCLIP deep-learning predictions, with optional confidence filtering by predicted score.

Usage

search_target2np_drugclip(
  search = NULL,
  search_field = c("all", "gene_symbol", "compound_name", "inchikey"),
  search_mode = c("exact", "fuzzy"),
  min_score = NULL,
  confidence = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of DrugCLIP predictions with attribute "total".

Examples

## Not run: 
search_target2np_drugclip(search = "quercetin", confidence = "high")

## End(Not run)

Description

Query SEA-style predictions derived from ChEMBL similarity scoring, with optional adjusted p-value filtering.

Usage

search_target2np_sea(
  search = NULL,
  search_field = c("all", "gene_symbol", "compound_name", "uniprot_id", "inchikey"),
  search_mode = c("exact", "fuzzy"),
  max_pvalue = NULL,
  confidence = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of SEA predictions with attribute "total".

Examples

## Not run: 
search_target2np_sea(search = "quercetin", confidence = "high")

## End(Not run)

Description

Query the TCM Bilingual Corpus with optional text search and filters.

Usage

search_terms(
  q = NULL,
  sources = NULL,
  category = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of terms with attribute "total".

Examples

## Not run: 
search_terms(q = "ginseng")

## End(Not run)

Description

Query the TCM Transcriptome Hub. This endpoint uses Style-B pagination (count/results instead of total/items).

Usage

search_transcriptomes(
  search = NULL,
  tcm_classification = NULL,
  organism = NULL,
  model_type = NULL,
  experiment_type = NULL,
  disease_classification = NULL,
  cell_line = NULL,
  comparison_type = NULL,
  confidence = NULL,
  sequence_type = NULL,
  page = 1L,
  page_size = 20L,
  all_pages = FALSE
)

Arguments

Value

A tibble::tibble() of datasets with attribute "total".

Examples

## Not run: 
search_transcriptomes(search = "ginseng")

## End(Not run)

Description

Stores the API key in session memory. Optionally also stores it in the system keyring (requires the keyring package).

Usage

set_api_key(api_key, keyring = FALSE)

Arguments

Value

Invisible NULL.

Examples

## Not run: 
set_api_key("unitcm_your_key_here")

## End(Not run)

Description

Set the UniTCM API base URL

Usage

set_base_url(url)

Arguments

Value

Invisible previous URL value.

Examples

## Not run: 
set_base_url("https://unitcm.qfxulab.com/api/v1")

## End(Not run)

Description

Stores the token in session memory. Optionally also stores it in the system keyring (requires the keyring package).

Usage

set_unitcm_token(token, keyring = FALSE)

Arguments

Value

Invisible NULL.

Examples

## Not run: 
set_unitcm_token("my-secret-token")

## End(Not run)

Description

Combine experimental records, DrugCLIP predictions, and SEA predictions for one query, returning source counts, target/compound overlap sets, confidence distributions, cross-validated compound-target pairs, and an interpretive suggestion string.

Usage

target2np_multi_source_summary(
  search = NULL,
  sources = c("experimental", "drugclip", "sea"),
  search_field = c("all", "gene_symbol", "compound_name", "uniprot_id", "inchikey",
    "pubchem_cid", "chembl_id"),
  search_mode = c("exact", "fuzzy")
)

Arguments

Value

A named list with fields source_counts, target_overlap, compound_overlap, confidence_distribution, cross_validated, and suggestion_text.

Examples

## Not run: 
summary <- target2np_multi_source_summary(
  search = "TP53", search_field = "gene_symbol"
)
summary$source_counts
summary$suggestion_text

## End(Not run)

Description

Clears any memoized API results. Currently a no-op placeholder for future caching support.

Usage

unitcm_cache_clear()

Value

Invisible NULL.