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Analysis of connected levels

Source: R/analyze_connected_levels.R
analyze_connected_levels.Rd

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 proteinGroup_precursor or mod.peptides_precursor for the corresponding traceR connection. Default is proteinGroup_precursor.

count_level

Counts appearances per possible connections. Choose either upper or lower - lower is always precursor level; upper is either proteingroup or mod.peptide level depending on chosen connected_levels. Default is upper. Duplicate entries are removed.

plot

Logical value, default is TRUE. If TRUE barplot is generated, if FALSE report as output.

plot_characteristic

if absolute the absolute count is displayed in barplot, if relative the relative count is displayed in barplot. Default is absolute. plot_characteristic has no influence on report.

Value

This function returns a plot - absolute/relative counts - or a data frame.

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.

Author

Oliver Kardell

Examples

# Load libraries
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(stringr)
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.0.5
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)
#> # A tibble: 3 x 3
#>   Connected_proteinGroups_precursor absolute_count relative_count
#> * <chr>                                      <int>          <dbl>
#> 1 common_common                                  1           33.3
#> 2 common_unique                                  1           33.3
#> 3 unique_common                                  1           33.3