Check and summarize relationship between prixor knowledge to measured

features

Usage

checkmatch_pk_to_data(
  data,
  input_pk,
  metadata_info = c(InputID = "HMDB", PriorID = "HMDB", grouping_variable = "term"),
  save_table = "csv",
  path = NULL,
  delimiter = ", "
)

Arguments

data: dataframe with at least one column with the detected metabolite IDs (e.g. HMDB). If there are multiple IDs per detected peak, please separate them by comma ("," or ", " or chr list). If there is a main ID and additional IDs, please provide them in separate columns.
input_pk: dataframe with at least one column with the metabolite ID (e.g. HMDB) that need to match data metabolite IDs "source" (e.g. term). If there are multiple IDs, as the original pathway IDs (e.g. KEGG) where translated (e.g. to HMDB), please separate them by comma ("," or ", " or chr list).
metadata_info: Colum name of Metabolite IDs in data and input_pk as well as column name of grouping_variable in input_pk. Default = c(InputID="HMDB", PriorID="HMDB", grouping_variable="term")
save_table: Optional: File types for the analysis results are: "csv", "xlsx", "txt". Default = "csv"
path: Optional: Path to the folder the results should be saved at. Default = NULL
delimiter: Optional: Character string separating multiple IDs within one cell of the InputID column in data, e.g. ", " or ";". Whitespace after the delimiter is ignored. Only applies to data; multiple IDs in input_pk are expected to be separated by ", ". Default = ", "

Value

A list with three elements:

data_summary — a data frame summarising matching results per input ID, including counts, conflicts, and recommended actions.
GroupingVariable_summary — a detailed data frame showing matches grouped by the specified variable, with conflict annotations.
data_long — a merged data frame of prior knowledge IDs and detected IDs in long format.

Examples

if (FALSE) { # \dontrun{
data(cellular_meta)
DetectedIDs <- cellular_meta %>%
    dplyr::select("Metabolite", "HMDB") %>%
    tidyr::drop_na()
input_pathway <- translate_id(
    data = metsigdb_kegg(),
    metadata_info = c(
        InputID = "MetaboliteID",
        grouping_variable = "term"
    ),
    from = c("kegg"),
    to = c("hmdb")
)[["TranslatedDF"]] %>% tidyr::drop_na()
Res <- checkmatch_pk_to_data(
    data = DetectedIDs,
    input_pk = input_pathway,
    metadata_info = c(
        InputID = "HMDB",
        PriorID = "hmdb",
        grouping_variable = "term"
    )
)
} # }