Decorates LFQData with a row annotation and some protein specific functions.

Description

Decorates LFQData with a row annotation and some protein specific functions.

Decorates LFQData with a row annotation and some protein specific functions.

Public fields

row_annot
data.frame containing further information
pID
column with protein ids
full_id
column with protein id e.g. sp| can be same as pID
description
name of column containing descriptions
cleaned_ids
vector with columns containing addition IDs
exp_nr_children
name of columns with the number of peptides
pattern_contaminants
pattern_contaminants
pattern_decoys
pattern_decoys

Methods

Public methods

Method new()

initialize

Usage 
ProteinAnnotation\$new(
  lfqdata,
  row_annot = NULL,
  description = NULL,
  cleaned_ids = NULL,
  full_id = NULL,
  exp_nr_children = "nr_peptides",
  pattern_contaminants = NULL,
  pattern_decoys = NULL
)
Arguments
lfqdata
data frame from setup_analysis
row_annot
data frame with row annotation. Must have columns matching config$hierarchy_keys_depth()
description
name of column with description
cleaned_ids
names of columns with cleaned Ids
full_id
column with full protein ID
exp_nr_children
column with the number of children
pattern_contaminants
pattern_contaminants
pattern_decoys
pattern_decoys

Method get_rev_pattern()

configured decoy pattern, or NULL when none was set

Usage 
ProteinAnnotation\$get_rev_pattern()

Method annotate_contaminants()

annotate contaminants

Sets the logical CON column via the shared prolfqua::is_contaminant detector (configured pattern unioned with the built-in defaults; an empty / NULL / “a^” pattern falls back to the defaults only – never grepl(““, x), which would flag every protein). This is the same detector the quant layer uses.

Usage 
ProteinAnnotation\$annotate_contaminants()

Method get_summary()

get summary (contaminants only; decoys are removed at construction)

Usage 
ProteinAnnotation\$get_summary()

Method nr_clean()

number of proteins kept after clean()

Usage 
ProteinAnnotation\$nr_clean(contaminants = TRUE)
Arguments
contaminants
remove contaminants

Method clean()

remove contaminants (always) and, when a decoy pattern was configured, decoy proteins from the annotation

Usage 
ProteinAnnotation\$clean(contaminants = TRUE)
Arguments
contaminants
remove contaminants

Method filter_by_nr_children()

filter by number children

Usage 
ProteinAnnotation\$filter_by_nr_children(exp_nr_children = 2)
Arguments
exp_nr_children
minimum number of children required

Method clone()

The objects of this class are cloneable with this method.

Usage 
ProteinAnnotation\$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.

Examples

library("prolfquapp")


istar <- prolfqua::sim_lfq_data_peptide_config(Nprot = 100)
lfq0 <- prolfqua::LFQData$new(istar$data, istar$config)
xd1 <- prolfqua::nr_children_experiment(lfq0$data_long(), lfq0$response(),
  lfq0$relevant_hierarchy_keys(), lfq0$file_name(), lfq0$nr_children_col())

xd2 <- prolfqua::nr_features_experiment(lfq0$data_long(), lfq0$hierarchy_keys(),
  lfq0$relevant_hierarchy_keys())
xd1$nr_child_exp |> table()

 1  2  3  4  5  6  7  8  9 10 11 12 
22 26 16 10  9  3  5  4  1  1  1  2 
lfqdata <- prolfqua::LFQData$new(istar$data, istar$config)
tmp <- lfqdata$data_long()
tmp$protein_Id <- add_RevCon(tmp$protein_Id)
lfqdata$set_data(tmp)
pids <- grep("^zz|^REV", unique(lfqdata$data_long()$protein_Id), value = TRUE, invert = TRUE)
addannot <- data.frame(
  protein_Id = pids,
  description = stringi::stri_rand_strings(length(pids), 13)
)

addannot <- addannot |> tidyr::separate(protein_Id, c("cleanID", NA), remove = FALSE)
# ProteinAnnotation$debug("initialize")
# debug(nr_obs_sample)
xd4 <- prolfqua::nr_obs_sample(lfqdata$data_long(), lfqdata$response(),
  lfqdata$relevant_hierarchy_keys(), lfqdata$file_name(), lfqdata$nr_children_col())
xd3 <- prolfqua::nr_features_experiment(lfqdata$data_long(), lfqdata$hierarchy_keys(),
  lfqdata$relevant_hierarchy_keys())

pannot <- ProteinAnnotation$new(lfqdata,
  addannot,
  description = "description",
  cleaned_ids = "cleanID",
  pattern_contaminants = "^zz",
  pattern_decoys = "^REV"
)
stopifnot(pannot$annotate_contaminants() == 5)
dd <- pannot$clean()
pannot$nr_clean()
[1] 85
pannot$get_summary()
  totalNrOfProteins percentOfContaminants
1               100                     5
stopifnot(nrow(dd) == 85)
tmp <- lfqdata$get_subset(dd)
dx2 <- pannot$filter_by_nr_children(exp_nr_children = 2)
dx3 <- pannot$filter_by_nr_children(exp_nr_children = 3)
stopifnot(nrow(dx2) >= nrow(dx3))