Prepare an entity table from MSstats results — prepare

Builds the table that convert_ids() grounds, select_entities() flags, and get_network() queries. It has one row per analyte, and keeps every analyte, not only the significant ones, so that nodes returned by a backend can be recognized as being in the input.

Usage

prepare_entities(
  df,
  id_column = "Protein",
  entity_type,
  id_type,
  organism = "9606",
  label = NULL,
  logfc_column = NULL
)

Arguments

df: output of groupComparison()'s ComparisonResult table, or any table with one row per analyte.
id_column: name of the column holding the analyte identifiers.
entity_type: one of the entity types ("protein", "gene", "transcript", "ptm_site", "metabolite", "lipid", "drug", "complex", "family", "other"), or the name of a column of df holding one per row.
id_type: the identifier system of id_column ("uniprot", "uniprot_mnemonic", "hgnc_symbol", "ensembl_protein", "ensembl_gene", "entrez", "chemical_name", "inchikey", "hmdb", "chebi", "chembl"), or the name of a column of df holding one per row. Which of them a backend can convert is listed by backend_capabilities(backend)$id_conversions. For PTM sites, the identifier system of the parent protein. "chemical_name" is a metabolite, lipid, or drug name, common or IUPAC (e.g. "glucose"), grounded by text matching.
organism: NCBI taxon ID, as a string.
label: the comparison to keep, when df has a Label column with more than one value.
logfc_column: the fold-change column to copy to logFC. NULL uses whichever of log2FC, log10FC, or logFC df has. Values are copied unchanged, in the log base of the input.

Value

data.frame with columns id, entity_type, id_type, namespace, entity_id, entity_name (all NA until convert_ids()), included_in_query (TRUE until select_entities()), site and parent_id (for ptm_site rows), organism, and logFC and adj.pvalue when df has them. Other columns of df are not copied; join them back on id.

Details

For PTM sites (entity_type = "ptm_site"), the site is parsed from the end of each identifier, e.g. "P00533_S1039_S1042" gives parent "P00533" and site "S1039_S1042". A GlobalProtein column, as MSstatsPTM writes, overrides the parsed parent.

Examples

input <- data.table::fread(system.file(
    "extdata/groupComparisonModel.csv",
    package = "MSstatsBioNet"
))
entities <- prepare_entities(input, entity_type = "protein",
                             id_type = "uniprot")
head(entities)
#>       id entity_type id_type namespace entity_id entity_name included_in_query
#> 1 O00217     protein uniprot      <NA>      <NA>        <NA>              TRUE
#> 2 O00330     protein uniprot      <NA>      <NA>        <NA>              TRUE
#> 3 O60313     protein uniprot      <NA>      <NA>        <NA>              TRUE
#> 4 O60879     protein uniprot      <NA>      <NA>        <NA>              TRUE
#> 5 O75306     protein uniprot      <NA>      <NA>        <NA>              TRUE
#> 6 P05023     protein uniprot      <NA>      <NA>        <NA>              TRUE
#>   site parent_id organism      logFC  adj.pvalue
#> 1 <NA>      <NA>     9606  2.0285031 0.013821932
#> 2 <NA>      <NA>     9606  1.3000941 0.004457034
#> 3 <NA>      <NA>     9606  0.9299641 0.019584180
#> 4 <NA>      <NA>     9606 -1.9484511 0.001635934
#> 5 <NA>      <NA>     9606  2.4745040 0.004457034
#> 6 <NA>      <NA>     9606  1.8391155 0.003251073

# MSstatsPTM results: the parent protein and site are parsed from the id
ptm <- data.frame(Protein = c("P00533_S1039_S1042", "P04637_S15"),
                  log2FC = c(1.2, -0.8), adj.pvalue = c(0.01, 0.2))
prepare_entities(ptm, entity_type = "ptm_site", id_type = "uniprot")
#>                   id entity_type id_type namespace entity_id entity_name
#> 1 P00533_S1039_S1042    ptm_site uniprot      <NA>      <NA>        <NA>
#> 2         P04637_S15    ptm_site uniprot      <NA>      <NA>        <NA>
#>   included_in_query        site parent_id organism logFC adj.pvalue
#> 1              TRUE S1039_S1042    P00533     9606   1.2       0.01
#> 2              TRUE         S15    P04637     9606  -0.8       0.20