Loading R packages

library(mpwR)
library(flowTraceR)
library(magrittr)
library(dplyr)
library(tidyr)
library(stringr)
library(tibble)
library(ggplot2)
library(flextable)

Import your data

Importing the output files from each software can be performed with prepare_mpwR. Please put all output files in one folder and follow the guidelines for naming the files. No other files/subfolders are allowed. Details are provided in the vignette Import.

files <- prepare_mpwR(path = "Path_to_Folder_with_files")

Examples

Some examples are provided to explore the workflow with create_example.

files <- create_example()

Report

The number of identifications can be determined with get_ID_Report.

ID_Reports <- get_ID_Report(input_list = files)

For each analysis an ID Report is generated and stored in a list. Each ID Report entry can be easily accessed:

flextable::flextable(ID_Reports[["DIA-NN"]])

Plot

Individual

Each ID Report can be plotted with plot_ID_barplot from precursor- to proteingroup-level. The generated barplots are stored in a list.

ID_Barplots <- plot_ID_barplot(input_list = ID_Reports, level = "ProteinGroup.IDs")

 

The individual barplots can be easily accessed:

ID_Barplots[["DIA-NN"]]

 

Summary

As a visual summary a boxplot can be generated with plot_ID_boxplot.

plot_ID_boxplot(input_list = ID_Reports, level = "ProteinGroup.IDs")

 

Report

Data Completeness can be determined with get_DC_Report for absolute numbers or in percentage.

DC_Reports <- get_DC_Report(input_list = files, metric = "absolute")
DC_Reports_perc <- get_DC_Report(input_list = files, metric = "percentage")

For each analysis a DC Report is generated and stored in a list. Each DC Report entry can be easily accessed:

flextable::flextable(DC_Reports[["DIA-NN"]])

Plot

Individual

Absolute

Each DC Report can be plotted with plot_DC_barplot from precursor- to proteingroup-level. The generated barplots are stored in a list.

DC_Barplots <- plot_DC_barplot(input_list = DC_Reports, level = "ProteinGroup.IDs", label = "absolute")

 

The individual barplots can be easily accessed:

DC_Barplots[["DIA-NN"]]

 

Percentage

plot_DC_barplot(input_list = DC_Reports_perc, level = "ProteinGroup.IDs", label = "percentage")[["DIA-NN"]]

 

Summary

As a visual summary a stacked barplot can be generated with plot_DC_stacked_barplot.

Absolute

plot_DC_stacked_barplot(input_list = DC_Reports, level = "ProteinGroup.IDs", label = "absolute")

 

Percentage

plot_DC_stacked_barplot(input_list = DC_Reports_perc, level = "ProteinGroup.IDs", label = "percentage")

 

Report

A report for Missed Cleavages can be generated with get_MC_Report for absolute numbers or in percentage.

MC_Reports <- get_MC_Report(input_list = files, metric = "absolute")
MC_Reports_perc <- get_MC_Report(input_list = files, metric = "percentage")

For each analysis a MC Report is generated and stored in a list. Each MC Report entry can be easily accessed:

flextable::flextable(MC_Reports[["Spectronaut"]])

Plot

Individual

Absolute

Each MC Report can be plotted with plot_MC_barplot from precursor- to proteingroup-level. The generated barplots are stored in a list.

MC_Barplots <- plot_MC_barplot(input_list = MC_Reports, label = "absolute")

 

The individual barplots can be easily accessed:

MC_Barplots[["Spectronaut"]]

 

Percentage

plot_MC_barplot(input_list = MC_Reports_perc, label = "percentage")[["Spectronaut"]]

 

Summary

As a visual summary a stacked barplot can be generated with plot_MC_stacked_barplot.

Absolute

plot_MC_stacked_barplot(input_list = MC_Reports, label = "absolute")

 

Percentage

plot_MC_stacked_barplot(input_list = MC_Reports_perc, label = "percentage")

 

Preparation

The coefficient of variation (CV) can be calculated with get_CV_RT. Only complete profiles are used.

CV_RT <- get_CV_RT(input_list = files)

Plot

As a visual summary a density plot for all analyses can be accessed via plot_CV_density.

plot_CV_density(input_list = CV_RT, cv_col = "RT")

Peptide-level

CV_LFQ_Pep <- get_CV_LFQ_pep(input_list = files)
#> For DIA-NN no quantitative LFQ data on peptide-level.
#> For PD no quantitative LFQ data on peptide-level.

plot_CV_density(input_list = CV_LFQ_Pep, cv_col = "Pep_quant")

Proteingroup-level

CV_LFQ_PG <- get_CV_LFQ_pg(input_list = files)
#> For PD no quantitative LFQ data on proteingroup-level.

Plot

As a visual summary a density plot for all analyses can be accessed via plot_CV_density.

plot_CV_density(input_list = CV_LFQ_PG, cv_col = "PG_quant")

 

Preparation

Use get_Upset_list to prepare for Upset plotting.

Upset_prepared <- get_Upset_list(input_list = files, level = "ProteinGroup.IDs")

Plot

The Upset plot can be generated with plot_Upset.

plot_Upset(input_list = Upset_prepared, label = "ProteinGroup.IDs")

Inter-software Comparison - flowTraceR

Functions of the package flowTraceR are incorporated in mpwR for inter-software comparisons. Software outputs are standardized and easily comparable.

get_Upset_list(input_list = files, level = "Peptide.IDs") %>% #prepare Upset
  plot_Upset(label = "Peptide.IDs") #plot
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?

Precursor-level with flowTraceR

By enabling flowTraceR the precursor-level information is standardized and common identifications can be inferred.

get_Upset_list(input_list = files, level = "Peptide.IDs", flowTraceR = TRUE) %>% #prepare Upset
  plot_Upset(label = "Peptide.IDs") #plot

 

Report

A summary report can be generated with get_summary_Report.

Summary_Report <- get_summary_Report(input_list = files)

Plot

As a visual summary a radar chart for all analyses can be accessed via plot_radarchart.

plot_radarchart(input_df = Summary_Report)

#Focus on Data Completeness
Summary_Report %>%
  dplyr::select(Analysis, contains("Full")) %>% #Analysis column and at least one category column is required
  plot_radarchart()