# PROCESSING ----------------------------------------------------------------------------------------------------------
# INPUT: internal peptide-level formatted data
#
# OUTPUT: tibble with the following columns:
# experiment,
# overall_labeling_efficiency, lys_labeling_efficiency, nterm_labeling_efficiency,
# num_fully_labeled, num_partially_labeled, num_unlabeled, num_no_sites_available, num_overlabeled,
# percent_fully_labeled, percent_partially_labeled, percent_unlabeled, percent_no_sites_available, percent_overlabeled,
# num_lys_labeled, num_lys_unlabeled, num_lys_blocked,
# num_nterm_labeled, num_nterm_unlabeled, num_nterm_blocked
#
# and the following rows:
# The first row should be the values for all experiments/fractions combined.
# After that, there should be a row for each experiment/fraction.
#
calc_labeling_efficiency <- function(filtered.data)
{
#calculate overall label efficiency
calculate.labeling.efficiency <- sum(filtered.data$detected_tags)/sum(filtered.data$expected_tags)
#calculate K label efficiency
lysine.labeling.efficiency <- sum(filtered.data$detected_lysine)/sum(filtered.data$expected_lysine)
#calculate N term label efficiency
nterm.labeling.efficiency <- sum(filtered.data$detected_nterm)/sum(filtered.data$expected_nterm)
# num_fully_labeled, num_partially_labeled, num_unlabeled, num_no_sites_available, num_overlabeled,
num.fully.labeled <- sum(filtered.data$labeling_efficiency == "Fully Labeled")
num.partially.labeled <- sum(filtered.data$labeling_efficiency == "Partially Labeled")
num.unlabeled <- sum(filtered.data$labeling_efficiency == "Unlabeled")
num.no.sites.available <- sum(filtered.data$labeling_efficiency == "No Sites Available")
num.overlabeled <- sum(filtered.data$labeling_efficiency == "Overlabeled")
#percent_fully_labeled, percent_partially_labeled, percent_unlabeled, percent_no_sites_available, percent_overlabeled,
per.fully.labeled <- (num.fully.labeled/nrow(filtered.data))*100
per.partially.labeled <- (num.partially.labeled/nrow(filtered.data))*100
per.unlabeled <- (num.unlabeled/nrow(filtered.data))*100
per.no.sites.available <- (num.no.sites.available/nrow(filtered.data))*100
per.overlabeled <- (num.overlabeled/nrow(filtered.data))*100
#num_lys_labeled, num_lys_unlabeled, num_lys_blocked, num_nterm_labeled, num_nterm_unlabeled, num_nterm_blocked
num.lys.labeled <- sum(filtered.data$detected_lysine)
num.lys.unlabeled <- sum(filtered.data$expected_lysine) - sum(filtered.data$detected_lysine)
num.nterm.labeled <- sum(filtered.data$detected_nterm)
num.nterm.unlabeled <- sum(filtered.data$expected_nterm) - sum(filtered.data$detected_nterm)
#create a tibble with these three values inside of it
efficiency.calculations <- tibble("Labeling Efficiency" = calculate.labeling.efficiency,
"Lysine Labeling Efficiency" = lysine.labeling.efficiency,
"N term Labeling Efficiency" = nterm.labeling.efficiency,
"Number Fully Labeled" = num.fully.labeled,
"Number Partially Labeled" = num.partially.labeled,
"Number Unlabeled" = num.unlabeled,
"Number No Sites Available" = num.no.sites.available,
"Number Overlabeled" = num.overlabeled,
"Percent Fully Labeled" = per.fully.labeled,
"Percent Partially Labeled" = per.partially.labeled,
"Percent Unlabeled" = per.unlabeled,
"Percent No Sites Available" = per.no.sites.available,
"Percent Overlabeled" = per.overlabeled)
efficiency.calculations
}
#
calc_mixing_correction <- function()
{
}
# VISUALIZATION -------------------------------------------------------------------------------------------------------
#
plot_labeling_efficiency <- function()
{
}
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