Create example data

Description

Example data for ProteomeDiscoverer, Spectronaut, DIA-NN and MaxQuant.

Usage

create_example()

Details

Example data is generated for each software for testing functions of mpwR. Each column is created in a randomized fashion. Connections between columns are not necessarily valid. E.g. column of Precursor Charges might not reflect charges of Precuror.IDs column.

Value

This function returns list with example data. Each list entry has filename and software information as well as a corresponding data set.

Author(s)

Oliver Kardell

Examples

data <- create_example()

Peptide-level: Quantitative precision

Description

Calculate quantitative precision on peptide-level

Usage

get_CV_LFQ_pep(input_list)

Arguments

Details

For each submitted data the coefficient of variation is calculated on peptide-level for LFQ intensities. Only full profiles are included.

Value

This function returns the original submitted data of the input_list including a new output column:

• CV_Peptide_LFQ_mpwR - coefficient of variation in percentage.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(stringr)
library(magrittr)
library(tibble)

# Example data
set.seed(123)
data <- list(
  Spectronaut = list(
     filename = "C",
     software = "Spectronaut",
     data = list(
        "Spectronaut" = tibble::tibble(
           Run_mpwR = rep(c("A","B"), times = 5),
           Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 2),
           Stripped.Sequence_mpwR = rep(c("A", "B", "C", "D", "E"), each = 2),
           Peptide.IDs_mpwR = rep(c("A", "B", "C", "D", "E"), each = 2),
           ProteinGroup.IDs_mpwR = rep(c("A", "B", "C", "D", "E"), each = 2),
           Retention.time_mpwR = sample(1:20, 10),
           Peptide_LFQ_mpwR = sample(1:30, 10),
           ProteinGroup_LFQ_mpwR = sample(1:30, 10))
     )
  )
)

# Result
output <- get_CV_LFQ_pep(
  input_list = data
)

Proteingroup-level: Quantitative precision

Description

Calculate quantitative precision on proteingroup-level

Usage

get_CV_LFQ_pg(input_list)

Arguments

Details

For each submitted data the coefficient of variation is calculated on proteingroup-level for LFQ intensities. Only full profiles are included.

Value

This function returns the original submitted data of the input_list including a new output column:

• CV_ProteinGroup_LFQ_mpwR - coefficient of variation in percentage.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(stringr)
library(magrittr)
library(tibble)

# Example data
set.seed(123)
data <- list(
  Spectronaut = list(
     filename = "C",
     software = "Spectronaut",
     data = list(
        "Spectronaut" = tibble::tibble(
           Run_mpwR = rep(c("A","B"), times = 5),
           Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 2),
           Peptide.IDs_mpwR = rep(c("A", "B", "C", "D", "E"), each = 2),
           ProteinGroup.IDs_mpwR = rep(c("A", "B", "C", "D", "E"), each = 2),
           Retention.time_mpwR = sample(1:20, 10),
           Peptide_LFQ_mpwR = sample(1:30, 10),
           ProteinGroup_LFQ_mpwR = sample(1:30, 10))
     )
  )
)

# Result
output <- get_CV_LFQ_pg(
  input_list = data
)

Retention time precision

Description

Calculate retention time precision

Usage

get_CV_RT(input_list)

Arguments

Details

For each submitted data the coefficient of variation is calculated on precursor-level for retention time. Only full profiles are included.

Value

This function returns the original submitted data of the input_list including a new output column:

• CV_Retention.time_mpwR - coefficient of variation in percentage.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(tibble)

# Example data
set.seed(123)
data <- list(
  Spectronaut = list(
     filename = "C",
     software = "Spectronaut",
     data = list(
        "Spectronaut" = tibble::tibble(
           Run_mpwR = rep(c("A","B"), times = 5),
           Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 2),
           Peptide.IDs_mpwR = rep(c("A", "B", "C", "D", "E"), each = 2),
           ProteinGroup.IDs_mpwR = rep(c("A", "B", "C", "D", "E"), each = 2),
           Retention.time_mpwR = sample(1:20, 10),
           Peptide_LFQ_mpwR = sample(1:30, 10),
           ProteinGroup_LFQ_mpwR = sample(1:30, 10))
     )
  )
)

# Result
output <- get_CV_RT(
  input_list = data
)

Data Completeness Report

Description

Generates a data completeness report from precursor to proteingroup-level

Usage

get_DC_Report(input_list, metric = c("absolute", "percentage"))

Arguments

Details

For each submitted data a data completeness report is generated highlighting missing values on precursor-, peptide-, protein- and proteingroup-level.

Value

This function returns a list. For each analysis a respective data frame including missing value information per level is stored in the generated list.

• Analysis - analysis name.

• Nr.Missing.Values - number of missing values.

• Precursor.IDs - number of precursor identification per missing value entry - absolute or in percentage.

• Peptide.IDs - number of peptide identification per missing value entry - absolute or in percentage.

• Protein.IDs - number of protein identification per missing value entry - absolute or in percentage.

• ProteinGroup.IDs - number of proteingroup identification per missing value entry - absolute or in percentage.

• Profile - categorical entries: "unique", "sparse", "shared with at least 50%" or "complete".

Author(s)

Oliver Kardell

Examples

# Load libraries
library(tibble)
library(stringr)

# Example data
data <- list(
DIANN = list(
  filename = "B",
  software = "DIA-NN",
  data = list(
     "DIA-NN" = tibble::tibble(
        Run_mpwR = rep(c("A","B"), times = 10),
        Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
        Protein.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
        Peptide.IDs_mpwR = rep(c("A", "A", "B", "B", "C"), each = 4),
        ProteinGroup.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4)
     )
  )
)
)

# Result
output <- get_DC_Report(
  input_list = data,
  metric = "absolute"
)

Report for identifications

Description

Generates a report for identifications

Usage

get_ID_Report(input_list)

Arguments

Details

For each submitted data a report with achieved number of identifications is generated on precursor-, peptide-, protein- and proteingroup-level.

Value

This function returns a list. For each analysis a respective data frame including number of identifications per run is stored in the generated list.

• Analysis - analysis name.

• Run - run information.

• Precursor.IDs - number of precursor identification.

• Peptide.IDs - number of peptide identification.

• Protein.IDs - number of protein identification.

• ProteinGroup.IDs - number of proteingroup identification.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(tibble)
library(stringr)

# Example data
data <- list(
DIANN = list(
  filename = "B",
  software = "DIA-NN",
  data = list(
     "DIA-NN" = tibble::tibble(
        Run_mpwR = rep(c("A","B"), times = 10),
        Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
        Protein.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
        Peptide.IDs_mpwR = rep(c("A", "A", "B", "B", "C"), each = 4),
        ProteinGroup.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4)
     )
  )
)
)

# Result
output <- get_ID_Report(
  input_list = data
)

Report about Missed Cleavages

Description

Generates report with information about number of missed cleavages

Usage

get_MC_Report(input_list, metric = c("absolute", "percentage"))

Arguments

Details

For each submitted data a report is generated with information about the number of missed cleavages.

Value

This function returns a list. For each analysis a respective data frame including information of missed cleavages is stored in the generated list.

• Analysis - analysis name.

• Missed.Cleavage - categorical entry with number of missed cleavages.

• mc_count - number of missed cleavages per categorical missed cleavage entry - absolute or in percentage.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(tibble)
library(magrittr)
library(stringr)

# Example data
data <- list(
Spectronaut = list(
  filename = "C",
  software = "Spectronaut",
  data = list(
     "Spectronaut" = tibble::tibble(
        Stripped.Sequence_mpwR = c("A", "B", "C", "D", "E"),
        Missed.Cleavage_mpwR = c(0, 1, 1, 2, 2)
     )
  )
)
)

# Result
output <- get_MC_Report(
  input_list = data,
  metric = "absolute"
)

Generate Upset list

Description

Generate a list as input for Upset plot

Usage

get_Upset_list(
  input_list,
  level = c("Precursor.IDs", "Peptide.IDs", "Protein.IDs", "ProteinGroup.IDs"),
  percentage_runs = 100,
  flowTraceR = FALSE,
  remove_traceR_unknownMods = FALSE
)

Arguments

Details

An input is generated for Upset plotting for either precursor-, peptide-, protein- or proteingroup-level. For inter-software comparisons flowTraceR is integrated.

Value

This function returns a list for each analysis with respective level information.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(tibble)
library(magrittr)
library(stringr)

# Example data
data <- list(
DIANN = list(
 filename = "B",
 software = "DIA-NN",
 data = list(
   "DIA-NN" = tibble::tibble(
     Run_mpwR = rep(c("A","B"), times = 10),
     Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
     Protein.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
     Peptide.IDs_mpwR = rep(c("A", "A", "B", "B", "C"), each = 4),
     ProteinGroup.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4)
   )
 )
),
Spectronaut = list(
 filename = "C",
 software = "Spectronaut",
 data = list(
   "Spectronaut" = tibble::tibble(
     Run_mpwR = rep(c("A","B"), times = 15),
     Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 6),
     Peptide.IDs_mpwR = rep(c("A", "A", "B", "B", "C"), each = 6),
     ProteinGroup.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 6)
   )
 )
)
)

# Result
output <- get_Upset_list(
  input_list = data,
  level = "Precursor.IDs"
)

Summary report

Description

Generates a summary report

Usage

get_summary_Report(
  input_list,
  CV_RT_th_hold = 5,
  CV_LFQ_Pep_th_hold = 20,
  CV_LFQ_PG_th_hold = 20
)

Arguments

Details

For each submitted data a summary report including information about achieved identifications (ID), data completeness (DC), missed cleavages (MC), and both quantitative (LFQ) and retention time (RT) precision is generated.

Value

This function returns a list. For each analysis a respective data frame is stored in the list with the following information:

• Analysis - analysis name.

• "Median ProteinGroup.IDs abs." - median number of proteingroup identifications.

• "Median Protein.IDs abs." - median number of protein identifications.

• "Median Peptide.IDs abs." - median number of peptide identifications.

• "Median Precursor.IDs abs." - median number of precursor identifications.

• "Full profile - Precursor.IDs abs." - number of precursor identifications for full profiles.

• "Full profile - Peptide.IDs abs." - number of peptide identifications for full profiles.

• "Full profile - Protein.IDs abs." - number of protein identifications for full profiles.

• "Full profile - ProteinGroup.IDs abs." - number of proteingroup identifications for full profiles.

• "Full profile - Precursor.IDs %" - number of precursor identifications for full profiles in percentage.

• "Full profile - Peptide.IDs %" - number of peptide identifications for full profiles in percentage.

• "Full profile - Protein.IDs %" - number of protein identifications for full profiles in percentage.

• "Full profile - ProteinGroup.IDs %" - number of proteinGroup identifications for full profiles in percentage.

• "Precursor.IDs abs. with a CV Retention time < X %" - number of precursor identifications with a CV value for retention time precision under user-specified threshold X.

• "Proteingroup.IDs abs. with a CV LFQ < X %" - number of proteingroup identifications with a CV value for quantitative precision under user-specified threshold X.

• "Peptide.IDs abs. with a CV LFQ < X %" - number of peptide identifications with a CV value for quantitative precision under user-specified threshold X.

• "Peptide IDs with zero missed cleavages abs." - number of peptide identifications with zero missed cleavages.

• "Peptide IDs with zero missed cleavages %" - number of peptide identifications with zero missed cleavages in percentage.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(tibble)

# Example data
data <- list(
DIANN = list(
 filename = "B",
 software = "DIA-NN",
 data = list(
   "DIA-NN" = tibble::tibble(
     "Run_mpwR" = c("R01", "R01", "R02", "R03", "R01"),
     "Precursor.IDs_mpwR" = c("A1", "A1", "A1", "A1", "B2"),
     "Retention.time_mpwR" = c(3, 3.5, 4, 5, 4),
     "ProteinGroup_LFQ_mpwR" = c(3, 4, 5, 4, 4),
     "Peptide.IDs_mpwR" = c("A", "A", "A", "A", "B"),
     "Protein.IDs_mpwR" = c("A", "A", "A", "A", "B"),
     "ProteinGroup.IDs_mpwR" = c("A", "A", "A", "A", "B"),
     "Stripped.Sequence_mpwR" = c("ABCR", "AKCR", "ABKCK", "ARKAR", "ABCDR")
   )
 )
)
)

# Result
output <- get_summary_Report(
  input_list = data
)

Load and Prepare the input data with experimental design file

Description

Based on submitted experimental design file the input data will be imported, renamed and default filtering will be applied. An experimental design template is available with write_experimental_design.

Usage

load_experimental_design(path)

Arguments

Details

Function for easily importing the default software outputs and preparing for downstream analysis with mpwR from multiple analysis folders. As default for MaxQuant "Reverse", "Potential contaminants" and "Only identified by site" are filtered out. As default for PD only "High" confidence identifications are included and for Found in Sample column(s) also only "High" identifications. Contaminants are filtered out. As default for Spectronaut only EG.Identified equals TRUE are included.

Value

A list - each list entry has filename and software info as well as stored data.

Author(s)

Oliver Kardell

Examples

## Not run: 
#get template with write_experimental_design and adjust inputs
write_experimental_design("DIRECTORY_TO_FILE")

#load in data
files <- load_experimental_design(path = "DIRECTORY_TO_FILE/your_exp_design.csv")

## End(Not run)

Density plot

Description

Plot cumulative density for precision results

Usage

plot_CV_density(
  input_list,
  xaxes_limit = 50,
  cv_col = c("RT", "Pep_quant", "PG_quant")
)

Arguments

Details

Quantitative or retention time precision are plotted as cumulative density.

Value

This function returns a density plot.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(dplyr)
library(comprehenr)
library(tibble)

# Example data
set.seed(123)
data <- list(
  "A" = tibble::tibble(
    Analysis_mpwR = rep("A", times = 10),
    CV_Retention.time_mpwR = sample(1:20, 10),
    CV_Peptide_LFQ_mpwR = sample(1:30, 10),
    CV_ProteinGroup_LFQ_mpwR = sample(1:30, 10)),
 "B" = tibble::tibble(
     Analysis_mpwR = rep("B", times = 10),
     CV_Retention.time_mpwR = sample(1:20, 10),
     CV_Peptide_LFQ_mpwR = sample(1:30, 10),
     CV_ProteinGroup_LFQ_mpwR = sample(1:30, 10))
)

# Plot
plot_CV_density(
  input_list = data,
  cv_col = "Pep_quant"
)

Individual Barplots - Data Completeness

Description

Plot number of identifications per missing values for each analysis.

Usage

plot_DC_barplot(
  input_list,
  level = c("Precursor.IDs", "Peptide.IDs", "Protein.IDs", "ProteinGroup.IDs"),
  label = c("absolute", "percentage")
)

Arguments

Details

For each submitted individual analysis a detailed barplot is generated with information about the number of achieved identifications per missing values.

Value

This function returns a list with a barplot for each analysis.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(magrittr)
library(comprehenr)
library(tibble)

# Example data
data <- list(
 "A" = tibble::tibble(
   Analysis = c("A", "A", "A"),
   Nr.Missing.Values = c(2, 1, 0),
   Precursor.IDs = c(50, 200, 4500),
   Peptide.IDs = c(30, 190, 3000),
   Protein.IDs = c(20, 40, 600),
   ProteinGroup.IDs = c(15, 30, 450),
   Profile = c("unique", "shared with at least 50%", "complete")
 ),
 "B" = tibble::tibble(
   Analysis = c("B", "B", "B"),
   Nr.Missing.Values = c(2, 1, 0),
   Precursor.IDs = c(50, 180, 4600),
   Peptide.IDs = c(50, 170, 3200),
   Protein.IDs = c(20, 40, 500),
   ProteinGroup.IDs = c(15, 30, 400),
   Profile = c("unique", "shared with at least 50%", "complete")
 )
)

# Plot
plot_DC_barplot(
  input_list = data,
  level = "Precursor.IDs",
  label = "absolute"
)

Summary Barplot - Data Completeness

Description

Plot number of identifications per missing values as stacked barplot.

Usage

plot_DC_stacked_barplot(
  input_list,
  level = c("Precursor.IDs", "Peptide.IDs", "Protein.IDs", "ProteinGroup.IDs"),
  label = c("absolute", "percentage")
)

Arguments

Details

The analyses are summarized in a stacked barplot displaying information about the number of achieved identifications per missing values.

Value

This function returns a stacked barplot.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(magrittr)
library(dplyr)
library(tibble)

# Example data
data <- list(
 "A" = tibble::tibble(
   Analysis = c("A", "A", "A"),
   Nr.Missing.Values = c(2, 1, 0),
   Precursor.IDs = c(50, 200, 4500),
   Peptide.IDs = c(30, 190, 3000),
   Protein.IDs = c(20, 40, 600),
   ProteinGroup.IDs = c(15, 30, 450),
   Profile = c("unique", "shared with at least 50%", "complete")
 ),
 "B" = tibble::tibble(
   Analysis = c("B", "B", "B"),
   Nr.Missing.Values = c(2, 1, 0),
   Precursor.IDs = c(50, 180, 4600),
   Peptide.IDs = c(50, 170, 3200),
   Protein.IDs = c(20, 40, 500),
   ProteinGroup.IDs = c(15, 30, 400),
   Profile = c("unique", "shared with at least 50%", "complete")
 )
)

# Plot
plot_DC_stacked_barplot(
  input_list = data,
  level = "Precursor.IDs",
  label = "absolute"
)

Individual Barplots - Identifications

Description

Plot number of achieved identifications per analysis.

Usage

plot_ID_barplot(
  input_list,
  level = c("Precursor.IDs", "Peptide.IDs", "Protein.IDs", "ProteinGroup.IDs")
)

Arguments

Details

For each submitted individual analysis a detailed barplot is generated with information about the number of achieved identifications per run.

Value

This function returns a list with a barplot for each analysis.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(magrittr)
library(comprehenr)
library(tibble)

# Example data
data <- list(
 "A" = tibble::tibble(
   Analysis = c("A", "A", "A"),
   Run = c("R01", "R02", "R03"),
   Precursor.IDs = c(4800, 4799, 4809),
   Peptide.IDs = c(3194, 3200, 3185),
   Protein.IDs = c(538, 542, 538),
   ProteinGroup.IDs = c(487, 490, 486)
 ),
 "B" = tibble::tibble(
   Analysis = c("B", "B", "B"),
   Run = c("R01", "R02", "R03"),
   Precursor.IDs = c(4597, 4602, 4585),
   Peptide.IDs = c(3194, 3200, 3185),
   Protein.IDs = c(538, 542, 538),
   ProteinGroup.IDs = c(487, 490, 486)
 )
)

# Plot
plot_ID_barplot(
  input_list = data,
  level = "Precursor.IDs"
)

Summary Boxplot - Identifications

Description

Plot summary of number of identifications in boxplot.

Usage

plot_ID_boxplot(
  input_list,
  level = c("Precursor.IDs", "Peptide.IDs", "Protein.IDs", "ProteinGroup.IDs")
)

Arguments

Details

The analyses are summarized in a boxplot displaying information about the number of achieved identifications.

Value

This function returns a boxplot.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(magrittr)
library(dplyr)
library(tibble)

# Example data
data <- list(
 "A" = tibble::tibble(
   Analysis = c("A", "A", "A"),
   Run = c("R01", "R02", "R03"),
   Precursor.IDs = c(7000, 6100, 4809),
   Peptide.IDs = c(3194, 3200, 3185),
   Protein.IDs = c(538, 542, 538),
   ProteinGroup.IDs = c(487, 490, 486)
 ),
 "B" = tibble::tibble(
   Analysis = c("B", "B", "B"),
   Run = c("R01", "R02", "R03"),
   Precursor.IDs = c(3000, 3500, 4585),
   Peptide.IDs = c(3194, 3200, 3185),
   Protein.IDs = c(538, 542, 538),
   ProteinGroup.IDs = c(487, 490, 486)
 )
)

# Plot
plot_ID_boxplot(
  input_list = data,
  level = "Precursor.IDs"
)

Individual Barplots - Missed Cleavages

Description

Plot number of missed cleavages for each analysis.

Usage

plot_MC_barplot(input_list, label = c("absolute", "percentage"))

Arguments

Details

For each submitted individual analysis a detailed barplot is generated with information about the number of missed cleavages.

Value

This function returns a list with a barplot for each analysis.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(comprehenr)
library(tibble)

# Example data
data <- list(
 "A" = tibble::tibble(
   Analysis = c("A", "A", "A", "A", "A"),
   Missed.Cleavage = c("0", "1", "2", "3", "No R/K cleavage site"),
   mc_count = c("2513", "368", "23", "38", "10")
 ),
 "B" = tibble::tibble(
   Analysis = c("B", "B", "B", "B", "B"),
   Missed.Cleavage = c("0", "1", "2", "3", "No R/K cleavage site"),
   mc_count = c("2300", "368", "23", "38", "10")
 )
)

# Plot
plot_MC_barplot(
  input_list = data,
  label = "absolute"
)

Summary Barplot - Missed Cleavages

Description

Plot number of missed cleavages as stacked barplot.

Usage

plot_MC_stacked_barplot(input_list, label = c("absolute", "percentage"))

Arguments

Details

The analyses are summarized in a stacked barplot displaying information about the number of missed cleavages.

Value

This function returns a stacked barplot.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(dplyr)
library(tibble)

# Example data
data <- list(
 "A" = tibble::tibble(
   Analysis = c("A", "A", "A", "A", "A"),
   Missed.Cleavage = c("0", "1", "2", "3", "No R/K cleavage site"),
   mc_count = c("2513", "368", "23", "38", "10")
 ),
 "B" = tibble::tibble(
   Analysis = c("B", "B", "B", "B", "B"),
   Missed.Cleavage = c("0", "1", "2", "3", "No R/K cleavage site"),
   mc_count = c("2300", "368", "23", "38", "10")
 )
)

# Plot
plot_MC_stacked_barplot(
  input_list = data,
  label = "absolute"
)

Upset Plot

Description

Plot intersections of analyses for different levels.

Usage

plot_Upset(
  input_list,
  label = c("Precursor.IDs", "Peptide.IDs", "Protein.IDs", "ProteinGroup.IDs"),
  nr_intersections = 10,
  highlight_overlap = FALSE
)

Arguments

Details

Identifications per level of each analysis are compared and possible intersections visualized.

Value

This function returns a Upset plot.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(UpSetR)
library(tibble)

# Example data
data <- list(
 "A" = c("A", "B", "C", "D"),
 "B" = c("A", "B", "C", "F"),
 "C" = c("A", "B", "G", "E")
)

# Plot
plot_Upset(
  input_list = data,
  label = "Peptide.IDs"
)

Radar chart

Description

Plot radar chart of summary statistics.

Usage

plot_radarchart(input_df)

Arguments

Details

Summary results are displayed via radar chart. Each analysis has its own trace.

Value

This function returns a radar chart as htmlwidget.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(plotly)
library(tibble)

# Example data
data <- tibble::tibble(
 Analysis = c("A", "B"),
 "Median ProteinGroup.IDs [abs.]" = c(5, 10),
 "Median Protein.IDs [abs.]" = c(5, 10),
 "Median Peptide.IDs [abs.]" = c(5, 10),
 "Median Precursor.IDs [abs.]" = c(5, 10),
 "Full profile - Precursor.IDs [abs.]" = c(5, 10),
 "Full profile - Peptide.IDs [abs.]" = c(5, 10),
 "Full profile - Protein.IDs [abs.]" = c(5, 10),
 "Full profile - ProteinGroup.IDs [abs.]" = c(5, 10),
 "Full profile - Precursor.IDs [%]" = c(5, 10),
 "Full profile - Peptide.IDs [%]" = c(5, 10),
 "Full profile - Protein.IDs [%]" = c(5, 10),
 "Full profile - ProteinGroup.IDs [%]" = c(5, 10),
 "Precursor.IDs [abs.] with a CV Retention time < 5 [%]" = c(5, 10),
 "Proteingroup.IDs [abs.] with a CV LFQ < 20 [%]" = c(NA, 10),
 "Peptide.IDs [abs.] with a CV LFQ < 20 [%]" = c(NA, 10),
 "Peptide IDs with zero missed cleavages [abs.]" = c(5, 10),
 "Peptide IDs with zero missed cleavages [%]" = c(5, 10)
)

# Plot
plot_radarchart(
  input_df = data
)

Load and Prepare the input data

Description

Input data will be imported, renamed and default filtering will be applied

Usage

prepare_mpwR(path, diann_addon_pg_qval = 0.01, diann_addon_prec_qval = 0.01)

Arguments

Details

Function for easily importing the default software outputs and preparing for downstream analysis with mpwR within one folder. As default for MaxQuant "Reverse", "Potential contaminants" and "Only identified by site" are filtered out. As default for PD only "High" confidence identifications are included and for Found in Sample column(s) also only "High" identifications. Contaminants are filtered out. As default for Spectronaut only EG.Identified equals TRUE are included.

Value

A list - each list entry has filename and software info as well as stored data.

Author(s)

Oliver Kardell

Examples

## Not run: 
prepare_mpwR(path = "DIRECTORY_TO_FILES")

## End(Not run)

Create template for experimental design

Description

Generation of a exp_design.csv file for using the import option with load_experimental_design.

Usage

write_experimental_design(path)

Arguments

Details

The generated exp_design.csv file can be used as starting point for importing with the load_experimental_design option for mpwR. Example entries are provided. The template file - exp_design.csv - is generated under the specified path.

Value

This function returns a csv-file with the following columns:

• analysis_name - name of your analysis.

• software - name of used software: DIA-NN, MaxQuant, PD, Spectronaut, Generic.

• path_to_folder - path to analysis folder.

Author(s)

Oliver Kardell

Examples

## Not run: 
write_experimental_design(path = "DIRECTORY_WHERE_FILE_IS_GENERATED")

## End(Not run)

Create generic template

Description

Generation of a template.csv file for generic input data. The template is provided in long-format.

Usage

write_generic_template(path_filename)

Arguments

Details

The generated template.csv file can be used to create a software-independent input file for mpwR. Example entries are provided. The template file - filename_Report.csv - is generated. The appendix "_Report" is required for importing with mpwR. Note that the template is in long-format, so each ProteinGroup.ID has possible multiple entries depending on the number of Precursor.IDs.

Value

This function returns a csv-file with the following columns:

• Run_mpwR - name of file(s).

• ProteinGroup.IDs_mpwR - ProteinGroup with identifier(s) of protein(s) contained in the protein group.

• Protein.IDs_mpwR - Protein identifier(s).

• Peptide.IDs_mpwR - Sequence representation plus possible post-translational modifications.

• Precursor.IDs_mpwR - Sequence representation plus possible post-translational modifications including charge state.

• Stripped.Sequence_mpwR - The amino acid sequence of the identified peptide without modifications.

• Precursor.Charge_mpwR - Charge state of the precursor.

• Missed.Cleavage_mpwR - Number of missed enzymatic cleavages.

• Retention.time_mpwR - Retention time in minutes in the elution profile of the precursor ion.

• ProteinGroup_LFQ_mpwR - LFQ intensity column on proteingroup-level

• Peptide_LFQ_mpwR - LFQ intensity column on petide-level

Author(s)

Oliver Kardell

Examples

## Not run: 
write_generic_template(path = "DIRECTORY_WHERE_FILE_IS_GENERATED/filename")

## End(Not run)