analyze_connected_levels {flowTraceR} | R Documentation |
Analysis of connected levels
Description
Analysis of the traceR_connected_pg_prec or traceR_connected_mod.pep_prec column
Usage
analyze_connected_levels(
input_df,
connected_levels = c("proteinGroup_precursor", "mod.peptides_precursor"),
count_level = c("upper", "lower"),
plot = TRUE,
plot_characteristic = c("absolute", "relative")
)
Arguments
input_df |
A tibble with flowTraceR´s connected level information e.g. traceR_connected_pg_prec. |
connected_levels |
Choose either |
count_level |
Counts appearances per possible connections. Choose either |
plot |
Logical value, default is TRUE. If |
plot_characteristic |
if |
Details
Shows the absolute and relative counts of possible connections - unique_unique/unique_common/common_unique/common_common of the respective column - as report or plot.
Value
This function returns a plot - absolute/relative counts - or a data frame.
Author(s)
Oliver Kardell
Examples
# Load libraries
library(dplyr)
library(stringr)
library(ggplot2)
library(tibble)
# DIA-NN example data
data <- tibble::tibble(
"traceR_connected_pg_prec" = c("common_common", "common_unique", "unique_common"),
"traceR_traced_proteinGroups" = c("common", "common", "unique"),
"traceR_traced_mod.peptides" = c("common", "unique", "common"),
"traceR_traced_precursor" = c("common", "unique", "common"),
"traceR_proteinGroups" = c("P02768", "P02671", "Q92496"),
"traceR_precursor" = c("AAC(UniMod:4)LLPK1", "RLEVDIDIK2", "EGIVEYPR2")
)
# Upper level - proteingroup level - how many proteingroups have a specific categorization
# Plot
analyze_connected_levels(input_df = data,
connected_levels = "proteinGroup_precursor",
count_level = "upper",
plot = TRUE,
plot_characteristic = "relative")
#Report
analyze_connected_levels(input_df = data,
connected_levels = "proteinGroup_precursor",
count_level = "upper",
plot = FALSE)