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

Source: R/R6_ProteinAnnotation.R
ProteinAnnotation.Rd

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 annotate_decoys()

annotate rev sequences

Usage

ProteinAnnotation$annotate_decoys()

Arguments

pattern

default "REV_"

Method annotate_contaminants()

annotate contaminants

Usage

ProteinAnnotation$annotate_contaminants()

Arguments

pattern

default "^zz|^CON"

Method get_summary()

get summary

Usage

ProteinAnnotation$get_summary()

Method nr_clean()

get number of neither contaminants nor decoys

Usage

ProteinAnnotation$nr_clean(contaminants = TRUE, decoys = TRUE)

Arguments

contaminants

remove contaminants

decoys

remove decoys return number of cleans

Method clean()

remove REV and CON sequences

Usage

ProteinAnnotation$clean(contaminants = TRUE, decoys = TRUE)

Arguments

contaminants

remove contaminants

decoys

remove decoys

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


istar <- prolfqua::sim_lfq_data_peptide_config(Nprot = 100)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
xd1 <- prolfqua::nr_obs_experiment(istar$data, istar$config, from_children = TRUE)

xd2 <- prolfqua::nr_obs_experiment(istar$data, istar$config, from_children = FALSE)
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$config)
lfqdata$data$protein_Id <- add_RevCon(lfqdata$data$protein_Id)
pids <- grep("^zz|^REV", unique(lfqdata$data$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, lfqdata$config)
xd3 <- prolfqua::nr_obs_experiment(lfqdata$data, lfqdata$config, from_children = FALSE)

pannot <- ProteinAnnotation$new(lfqdata,
  addannot,
  description = "description",
  cleaned_ids = "cleanID",
  pattern_contaminants = "^zz",
  pattern_decoys = "^REV"
)
#> Warning: no exp_nr_children column specified, computing using nr_obs_experiment function
stopifnot(pannot$annotate_decoys() == 10)
stopifnot(pannot$annotate_contaminants() == 5)
dd <- pannot$clean()
pannot$nr_clean()
#> [1] 85
pannot$get_summary()
#>   totalNrOfProteins percentOfContaminants percentOfFalsePositives
#> 1               100                     5                      10
#>   NrOfProteinsNoDecoys
#> 1                   85
stopifnot(nrow(dd) == 85)
tmp <- lfqdata$get_subset(dd)
#> Joining with `by = join_by(protein_Id)`
dx <- pannot$clean(contaminants = TRUE, decoys = FALSE)
stopifnot(nrow(dx) == 95)
dx <- pannot$clean(contaminants = FALSE, decoys = TRUE)
stopifnot(nrow(dx) == 90)
dx2 <- pannot$filter_by_nr_children(exp_nr_children = 2)
dx3 <- pannot$filter_by_nr_children(exp_nr_children = 3)
stopifnot(nrow(dx2) >= nrow(dx3))