To functions

Functions to convert one object type to another.

dt_to_matrix(dat, omit_cols = NULL, rownames = NULL, as_sparse = TRUE)

graph_to_dt(
  g,
  what = c("nodes", "edges"),
  id_col = c("id", "name"),
  prefixes = c("subject", "object")
)

graph_to_ggnetwork(g, ...)

ontology_to(
  ont,
  to = c("adjacency", "adjacency_dist", "adjacency_dist_hclust",
    "adjacency_dist_hclust_dendrogram", "adjacency_dist_hclust_clusters", "similarity",
    "dendrogram", "igraph", "dot", "adjacency_igraph", "igraph_dist",
    "igraph_dist_hclust", "igraph_dist_hclust_dendrogram", "tbl_graph", "data.frame",
    "data.table", "list"),
  terms = ont@terms,
  remove_terms = grep(":", terms, invert = TRUE, value = TRUE),
  as_sparse = FALSE,
  ...
)

ontology_to_graph(ont, ...)

to_graph(g, ...)

Arguments

as_sparse

Return the object as a sparseMatrix.

g

ggnetwork object (or an igraph/tbl_graph to be converted to ggnetwork format).

id_col

Column containing the unique identifier for each node in a graph (e.g. "name").

...

Arguments passed on to ggnetwork::fortify

ont

An ontology of class ontology_DAG.

to

A character string specifying the format to convert to.

terms

Term IDs to include. Can alternatively be an integer, which will be used to randomly sample N terms from the data.

remove_terms

Character vector of term IDs to exclude.

as

A character string specifying the format to convert to.

as_dt

Return the object as a data.table.

as_graph

Return the object as a tbl_graph.

as_granges

Return the object as a GRanges.

Value

Converted data.

Functions

• dt_to_matrix(): to_

• graph_to_dt(): to_

• graph_to_ggnetwork(): to_

• ontology_to(): to_ Convert ontology

  Convert an ontology_DAG to a number of other useful formats.

• ontology_to_graph(): to_

• to_graph(): to_

Examples

dt <- data.table::as.data.table(mtcars, keep.rownames = TRUE)
X <- dt_to_matrix(dt)
ont <- get_ontology("hp", terms=10)
#> ℹ All local files already up-to-date!
#> Importing cached file: /github/home/.cache/R/KGExplorer/hp-international.owl
#> Adding term metadata.
#> IC_method: IC_offspring
#> Adding ancestor metadata.
#> Getting absolute ontology level for 25,301 IDs.
#> 900 ancestors found at level 2
#> Translating all terms to names.
#> + Returning a vector of terms (same order as input).
#> Converted ontology to: adjacency 
#> Getting absolute ontology level for 25,301 IDs.
#> Randomly sampling 10 term(s).
g <- ontology_to(ont, to="tbl_graph")
#> Converted ontology to: tbl_graph 
dat <- graph_to_dt(g)
#> Converting tidygraph to data.table.
g <- igraph::graph.atlas(10)
ggn <- graph_to_ggnetwork(g)
#> Converting graph to ggnetwork.
ont <- get_ontology()
#> ℹ All local files already up-to-date!
#> Importing cached file: /github/home/.cache/R/KGExplorer/mondo.owl
#> Adding term metadata.
#> IC_method: IC_offspring
#> Adding ancestor metadata.
#> Getting absolute ontology level for 31,550 IDs.
#> 2412 ancestors found at level 2
#> Translating all terms to names.
#> + Returning a vector of terms (same order as input).
#> Converted ontology to: adjacency 
#> Getting absolute ontology level for 31,550 IDs.
obj <- ontology_to(ont=ont, to="dendrogram")
#> 
#> going through 1000 / 27730 nodes ...
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#> going through 27730 / 27730 nodes ... Done.
#> Converted ontology to: dendrogram 
g <- igraph::graph.full(10)
g2 <- to_graph(g)