Help for package flowTraceR

Title:

Tracing Information Flow for Inter-Software Comparisons in Mass Spectrometry-Based Bottom-Up Proteomics

Version:

0.1.1

Description:

Useful functions to standardize software outputs from ProteomeDiscoverer, Spectronaut, DIA-NN and MaxQuant on precursor, modified peptide and proteingroup level and to trace software differences for identifications such as varying proteingroup denotations for common precursor.

License:

MIT + file LICENSE

Depends:

R (≥ 2.10)

Imports:

comprehenr, dplyr, ggplot2, magrittr, stringr, tibble, tidyr

Suggests:

data.table, kableExtra, knitr, rmarkdown, testthat (≥ 3.0.0)

VignetteBuilder:

knitr

Config/testthat/edition:

Encoding:

UTF-8

URL:

https://github.com/OKdll/flowTraceR

RoxygenNote:

7.3.1

NeedsCompilation:

Packaged:

2025-04-29 14:10:38 UTC; Olive

Author:

Oliver Kardell [aut, cre]

Maintainer:

Oliver Kardell <Okdll@gmx.net>

Repository:

CRAN

Date/Publication:

2025-04-29 14:20:05 UTC

flowTraceR: a package for standardization of level information and tracking inter-software differences in bottom-up label-free proteomics

Description

Author(s)

Maintainer: Oliver Kardell Okdll@gmx.net

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 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.

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)

Analysis of unknown modifications

Description

Analysis of the traceR_precursor_unknownMods or traceR_mod.peptides_unknownMods column

Usage

analyze_unknown_mods(
  input_df,
  level = c("precursor, modified_peptides"),
  plot = TRUE,
  plot_characteristic = c("absolute", "relative")
)

Arguments

input_df

A tibble with the traceR_precursor_unknownMods or traceR_mod.peptides_unknownMods column.

level

Choose either precursor for traceR_precursor_unknownMods or modified_peptides for traceR_mod.peptides_unknownMods. Default is precursor.

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.

Details

Shows the absolute and relative counts of TRUE/FALSE of the traceR_precursor_unknownMods or traceR_mod.peptides_unknownMods column - as data frame or plot. Duplicate traceR_mod.peptides entries or traceR_precursor entries are removed, respectively.

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)

# Generate data
data <- tibble::tibble(
 "traceR_mod.peptides" = c("AACLLPK",
  "ALTDM(UniMod:35)PQM(UniMod:35)R",
  "ALTDM(DummyModification)PQMK",
  "ALTDM(UniMod:35)PQM(UniMod:35)R",
  "ALTDM(DummyModification)PQMK"),
 "traceR_mod.peptides_unknownMods" = c(FALSE, FALSE, TRUE, FALSE, TRUE),
 "traceR_precursor" = c("AACLLPK2",
  "ALTDM(UniMod:35)PQM(UniMod:35)R2",
  "ALTDM(DummyModification)PQMK3",
  "ALTDM(UniMod:35)PQM(UniMod:35)R2",
  "ALTDM(DummyModification)PQMK3"),
 "traceR_precursor_unknownMods" = c(FALSE, FALSE, TRUE, FALSE, TRUE)
)

# Generate Report - precursor level
analyze_unknown_mods(
 input_df = data,
 level = "precursor",
 plot = FALSE
)

# Generate relative Plot - peptide level
analyze_unknown_mods(
 input_df = data,
 level = "modified_peptides",
 plot = TRUE,
 plot_characteristic = "relative"
)

Connects traced levels

Description

Connects two levels after categorization in unique and common entries.

Usage

connect_traceR_levels(
  input_df,
  level = c("proteinGroups", "modified_peptides")
)

Arguments

input_df

A tibble with flowTraceR´s traced level information e.g. traceR_traced_proteinGroups.

level

Choose between proteinGroups or modified_peptides. Connection between proteinGroups/modified_peptides and precursor categorization. Default is proteinGroups.

Details

Based on flowTraceR´s categorization in unique and common identifications two levels are connected. Possible connections are proteinGroup or modified peptide with precursor categorization.

Value

This function returns a tibble with one of the following columns depending on chosen level:

traceR_connected_pg_prec - connection between proteinGroup categorization and precursor categorization.
traceR_connected_mod.pep_prec - connection between modified peptide categorization and precursor categorization.

Author(s)

Oliver Kardell

Examples

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

# DIA-NN example data
diann <- tibble::tibble(
   "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_mod.peptides" = c("AAC(UniMod:4)LLPK", "RLEVDIDIK", "EGIVEYPR"),
   "traceR_mod.peptides_unknownMods" = c(FALSE, FALSE, FALSE),
   "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "RLEVDIDIK2", "EGIVEYPR2"),
   "traceR_precursor_unknownMods" = c(FALSE, FALSE, FALSE)
)

spectronaut <- tibble::tibble(
   "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", "Q02985"),
   "traceR_mod.peptides" = c("AAC(UniMod:4)LLPK", "M(UniMod:35)KPVPDLVPGNFK", "EGIVEYPR"),
   "traceR_mod.peptides_unknownMods" = c(FALSE, FALSE, FALSE),
   "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "M(UniMod:35)KPVPDLVPGNFK2", "EGIVEYPR2"),
   "traceR_precursor_unknownMods" = c(FALSE, FALSE, FALSE)
)

# Connect Precursor and ProteinGroup level
diann_connected <- connect_traceR_levels(input_df = diann, level = "proteinGroups")

spectronaut_connected <- connect_traceR_levels(input_df = spectronaut, level = "proteinGroups")

Conversion of software specific levels

Description

Conversion of precursor, modified peptide and proteinGroup entries to standardized format.

Usage

convert_all_levels(
  input_df,
  input_MQ_pg,
  software = c("MaxQuant", "DIA-NN", "Spectronaut", "PD")
)

Arguments

input_df

A tibble with precursor, modified peptide and proteinGroup level information. For MaxQuant: evidence.txt and proteinGroups.txt, for PD: PSMs.txt with R-friendly headers enabled, for DIA-NN and Spectronaut default output reports.

input_MQ_pg

For MaxQuant: A tibble with proteinGroup level information - proteinGroups.txt.

software

The used analysis software - MaxQuant, PD, DIA-NN or Spectronaut. Default is MaxQuant.

Details

The input entries are converted to a software independent format. The generated entries are appended to the submitted dataframe.

Value

This function returns the original submitted tibble - input_df - including the following new columns:

traceR_precursor - software-independent standardized text for precursor entries.
traceR_precursor_unknownMods - logical value, if TRUE: a modification is detected, which is not converted to a standardized format.
traceR_mod.peptides - software-independent standardized text for modified peptide entries.
traceR_mod.peptides_unknownMods - logical value, if TRUE: a modification is detected, which is not converted to a standardized format.
traceR_proteinGroups - software-independent standardized text for proteinGroups.

Author(s)

Oliver Kardell

Examples

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

# MaxQuant example data
evidence <- tibble::tibble(
  "Modified sequence" = c("_AACLLPK_",
   "_ALTDM(Oxidation (M))PQM(Oxidation (M))R_",
   "ALTDM(Dummy_Modification)PQMK"),
  Charge = c(2,2,3),
  "Protein group IDs" = c("26", "86;17", "86;17")
)

proteingroups <- tibble::tibble(
"Protein IDs" = c("A0A075B6P5;P01615;A0A087WW87;P01614;A0A075B6S6", "P02671", "P02672"),
id = c(26, 86, 17)
)

# Conversion
convert_all_levels(
 input_df = evidence,
 input_MQ_pg = proteingroups,
 software = "MaxQuant"
)

Conversion of software specific modified peptide entries

Description

Modified peptide entries are converted to a common text representation

Usage

convert_modified_peptides(
  input_df,
  software = c("MaxQuant", "PD", "DIA-NN", "Spectronaut")
)

Arguments

input_df

A tibble with modified peptide level information. For MaxQuant: evidence.txt, for PD: PSMs.txt with R-friendly headers enabled, for DIA-NN and Spectronaut default output reports.

software

The used analysis software for the input_df - MaxQuant, PD, DIA-NN or Spectronaut. Default is MaxQuant.

Details

The input entries are converted to a software independent format. The generated entries are appended to the submitted dataframe. Conversion of modifications is currently only available for UniMod:35 and UniMod:4. Other modifications will not be converted to standardized format.

Value

This function returns the original submitted tibble - input_df - including two new columns:

traceR_mod.peptides - software-independent standardized text for modified peptide entries.
traceR_mod.peptides_unknownMods - logical value, if TRUE: a modification is detected, which is not converted to a standardized text.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(dplyr)
library(stringr)
library(tidyr)
library(tibble)

# MaxQuant example data
data <- tibble::tibble(
 "Modified sequence" = c("_AACLLPK_",
  "_ALTDM(Oxidation (M))PQM(Oxidation (M))R_",
  "ALTDM(Dummy_Modification)PQMK"),
  Charge = c(2,2,3)
)

# Conversion
convert_modified_peptides(
 input_df = data,
 software = "MaxQuant"
)

Conversion of software specific precursor entries

Description

Precursor entries are converted to a common text representation

Usage

convert_precursor(
  input_df,
  software = c("MaxQuant", "PD", "DIA-NN", "Spectronaut")
)

Arguments

input_df

A tibble with precursor level information. For MaxQuant: evidence.txt, for PD: PSMs.txt with R-friendly headers enabled, for DIA-NN and Spectronaut default output reports.

software

The used analysis software for the input_df - MaxQuant, PD, DIA-NN or Spectronaut. Default is MaxQuant.

Details

Value

This function returns the original submitted tibble - input_df - including two new columns:

traceR_precursor - software-independent standardized text for precursor entries.
traceR_precursor_unknownMods - logical value, if TRUE: a modification is detected, which is not converted to a standardized text.

Author(s)

Oliver Kardell

Examples

# Load libraries
library(dplyr)
library(stringr)
library(tidyr)
library(tibble)

# MaxQuant example data
data <- tibble::tibble(
 "Modified sequence" = c("_AACLLPK_",
 "_ALTDM(Oxidation (M))PQM(Oxidation (M))R_",
 "ALTDM(Dummy_Modification)PQMK"),
  Charge = c(2,2,3)
)

# Conversion
convert_precursor(
 input_df = data,
 software = "MaxQuant"
)

Conversion of software specific proteinGroups

Description

ProteinGroups are converted to a common text representation

Usage

convert_proteingroups(
  input_df,
  software = c("MaxQuant", "DIA-NN", "Spectronaut", "PD")
)

Arguments

input_df

A tibble with proteinGroup level information. For MaxQuant: proteinGroups.txt, for PD: PSMs.txt with R-friendly headers enabled, for DIA-NN and Spectronaut default output reports.

software

The used analysis software for the input_df - MaxQuant, PD, DIA-NN or Spectronaut. Default is MaxQuant.

Details

The input entries are converted to a software independent format. The generated entries are appended to the submitted dataframe.

Value

This function returns the original submitted tibble - input_df - including one new column:

traceR_proteinGroups - software-independent standardized text for proteinGroups.

Author(s)

Oliver Kardell

Examples

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

# MaxQuant example data
data <- tibble::tibble(
"Protein IDs" = c("A0A075B6P5;P01615;A0A087WW87;P01614;A0A075B6S6", "P02671", "P02672"),
id = c(26, 86, 17)
)

# Conversion
convert_proteingroups(
 input_df = data,
 software = "MaxQuant"
)

Create example data

Description

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

Usage

get_example(
  example = c("MaxQuant", "DIA-NN", "Spectronaut", "PD", "RetentionTime")
)

Arguments

example

Choose between "ProteomeDiscoverer", "Spectronaut", "DIA-NN" and "MaxQuant" or for an example for downstream analysis "RetentionTime". Default is MaxQuant.

Details

Data for each software for testing functions of flowTraceR. Additional example data for Spectronaut and DIA-NN for analyzing retention time distribution on precursor level.

Value

This function returns example data as dataframe for the respective chosen example. For "MaxQuant" a list with evidence/proteingroup dataframe. For "RetentionTime" a list with Spectronaut/DIA-NN data including retention time information.

Author(s)

Oliver Kardell

Examples


# Spectronaut example data
Spectronaut_data <- get_example(example = "Spectronaut")

Check of converted modifications

Description

Check if conversion to UniMod-format of identified modifications is successful.

Usage

get_unknown_mods(input_string, pattern_start, pattern_end)

Arguments

input_string

character column traceR_precursor as string.

pattern_start

character of software-dependent beginning of representation of modifications.

pattern_end

character of software-dependent end of representation of modifications.

Details

After conversion to standardized format by convert_precursor or convert_modified_peptides, entries with modifications are checked for a successful conversion. Conversion of modifications is currently only available for UniMod:35 and UniMod:4. Other modifications will not be converted to standardized format.

Value

This function returns vector with logical values. This function is incorporated in the functions convert_precursor and convert_modified_peptides; used to generate the unknownMods column : if TRUE: a modification is detected, which is not converted to a standardized text.

Author(s)

Oliver Kardell

Examples

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

# Generate data
data <- tibble::tibble(
  "traceR_precursor" = c("AACLLPK",
   "ALTDM(UniMod:35)PQM(UniMod:35)R2",
   "ALTDM(DummyModification)PQMK3")
)

# Unknown modifications present?
get_unknown_mods(input_string = data$traceR_precursor, pattern_start= "(", pattern_end = ")")

Trace common and unique identifications between different software outputs for all levels

Description

Identifications of two input data frames are compared and categorized in unique and common entries for each level.

Usage

trace_all_levels(
  input_df1,
  input_df2,
  analysis_name1 = "input_df1",
  analysis_name2 = "input_df2",
  filter_unknown_mods = TRUE
)

Arguments

input_df1

A tibble with flowTraceR's standardized precursor, modified peptide and proteinGroup level information.

input_df2

A tibble with flowTraceR's standardized precursor, modified peptide and proteinGroup level information.

analysis_name1

output tibble name for input_df1 - default is "input_df1".

analysis_name2

output tibble name for input_df2 - default is "input_df2".

filter_unknown_mods

Logical value, default is TRUE. If TRUE, unknown modifications are filtered out - requires "traceR_precursor_unknownMods" or "traceR_mod.peptides_unknownMods" column.

Details

Based on flowTraceR's standardized output format two software outputs can be compared and categorized into common and unique identifications - for precursor, modified peptide and proteinGroup level.

Value

This function returns a list with both original submitted tibbles - input_df1 and input_df2 - with the following new columns:

traceR_traced_precursor - categorization on precursor level in common and unique entries.
traceR_traced_mod.peptides - categorization on modified peptide level in common and unique entries.
traceR_traced_proteinGroups - categorization on proteinGroups level in common and unique entries.

Author(s)

Oliver Kardell

Examples

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

# DIA-NN example data
diann <- tibble::tibble(
  "traceR_proteinGroups" = c("P02768", "P02671", "Q92496", "DummyProt"),
  "traceR_mod.peptides" = c("AAC(UniMod:4)LLPK", "RLEVDIDIK",
   "EGIVEYPR", "ALTDM(DummyModification)PQMK"),
  "traceR_mod.peptides_unknownMods" = c(FALSE, FALSE, FALSE, TRUE),
  "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "RLEVDIDIK2",
   "EGIVEYPR2", "ALTDM(DummyModification)PQMK3" ),
  "traceR_precursor_unknownMods" = c(FALSE, FALSE, FALSE, TRUE)
)
# Spectronaut example data
spectronaut <- tibble::tibble(
  "traceR_proteinGroups" = c("P02768", "Q02985", "P02671"),
  "traceR_mod.peptides" = c("AAC(UniMod:4)LLPK", "EGIVEYPR", "M(UniMod:35)KPVPDLVPGNFK"),
  "traceR_mod.peptides_unknownMods" = c(FALSE, FALSE, FALSE),
  "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "EGIVEYPR2", "M(UniMod:35)KPVPDLVPGNFK2"),
  "traceR_precursor_unknownMods" = c(FALSE, FALSE, FALSE)
)

# trace all levels in one step
traced_all <- trace_all_levels(
  input_df1 = diann,
  input_df2 = spectronaut,
  analysis_name1 = "DIA-NN",
  analysis_name2 = "Spectronaut",
  filter_unknown_mods = TRUE
)

Trace common and unique identifications between different software outputs

Description

Identifications of two input data frames are compared and categorized in unique and common entries.

Usage

trace_level(
  input_df1,
  input_df2,
  analysis_name1 = "input_df1",
  analysis_name2 = "input_df2",
  level = c("precursor", "modified_peptides", "proteinGroups"),
  filter_unknown_mods = TRUE
)

Arguments

input_df1

A tibble with flowTraceR's standardized precursor, modified peptide, or proteinGroup level information - required column depends on chosen level.

input_df2

A tibble with flowTraceR's standardized precursor, modified peptide, or proteinGroup level information - required column depends on chosen level.

analysis_name1

output tibble name for input_df1 - default is "input_df1".

analysis_name2

output tibble name for input_df2 - default is "input_df2".

level

"precursor", "modified_peptides", "proteinGroups" - respective level for tracing common vs. unique entries. Default is precursor.

filter_unknown_mods

Logical value, default is TRUE. If TRUE, unknown modifications are filtered out - requires "traceR_precursor_unknownMods" or "traceR_mod.peptides_unknownMods" column; depends on chosen level.

Details

Based on flowTraceR's standardized output format two software outputs can be compared and categorized into common and unique identifications for a chosen level: precursor, modified peptide or proteinGroup level.

Value

This function returns a list with both original submitted tibbles - input_df1 and input_df2 - including one of the following new columns depending on chosen level :

traceR_traced_precursor - categorization on precursor level in common and unique entries.
traceR_traced_mod.peptides - categorization on modified peptide level in common and unique entries.
traceR_traced_proteinGroups - categorization on proteinGroups level in common and unique entries.

Author(s)

Oliver Kardell

Examples

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

# DIA-NN example data
diann <- tibble::tibble(
  "traceR_proteinGroups" = c("P02768", "P02671", "Q92496", "DummyProt"),
  "traceR_mod.peptides" = c("AAC(UniMod:4)LLPK", "RLEVDIDIK",
   "EGIVEYPR", "ALTDM(DummyModification)PQMK"),
  "traceR_mod.peptides_unknownMods" = c(FALSE, FALSE, FALSE, TRUE),
  "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "RLEVDIDIK2",
   "EGIVEYPR2", "ALTDM(DummyModification)PQMK3" ),
  "traceR_precursor_unknownMods" = c(FALSE, FALSE, FALSE, TRUE)
)

# Spectronaut example data
spectronaut <- tibble::tibble(
  "traceR_proteinGroups" = c("P02768", "Q02985", "P02671"),
  "traceR_mod.peptides" = c("AAC(UniMod:4)LLPK", "EGIVEYPR", "M(UniMod:35)KPVPDLVPGNFK"),
  "traceR_mod.peptides_unknownMods" = c(FALSE, FALSE, FALSE),
  "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "EGIVEYPR2", "M(UniMod:35)KPVPDLVPGNFK2"),
  "traceR_precursor_unknownMods" = c(FALSE, FALSE, FALSE)
)

# trace proteinGroup level
traced_proteinGroups <- trace_level(
  input_df1 = diann,
  input_df2 = spectronaut,
  analysis_name1 = "DIA-NN",
  analysis_name2 = "Spectronaut",
  level = "proteinGroups",
  filter_unknown_mods = TRUE
)

# trace precursor level
traced_pecursor <- trace_level(
  input_df1 = diann,
  input_df2 = spectronaut,
  analysis_name1 = "DIA-NN",
  analysis_name2 = "Spectronaut",
  level = "precursor",
  filter_unknown_mods = TRUE
)

Trace unique_common categorization for proteinGroup level

Description

Unique_common categorizations are analyzed on proteinGroup level

Usage

trace_unique_common_pg(
  input_df1,
  input_df2,
  analysis_name1 = "input_df1",
  analysis_name2 = "input_df2",
  string_analysis = FALSE
)

Arguments

input_df1

A tibble with flowTraceR's unique_common categorization for the proteinGroup_precursor connection.

input_df2

A tibble which is the counter part for input_df1 - which was used to generate the unique_common categorization for the proteinGroup_precursor connection.

analysis_name1

String. Appended to input_df1's traceR_proteinGroups column - default is "input_df1".

analysis_name2

String. Appended to input_df1's traceR_proteinGroups column - default is "input_df2".

string_analysis

Logical value, default is FALSE. If TRUE, only keeps proteinGroup identifications of input_df1 in which protein denotations are not present in the counterpart - the proteinGroups of input_df2 - and vice versa.

Details

For each submitted dataframe the unique_common proteinGroup_precursor connection is analyzed to highlight potential differences in proteinGroup denotations for common precursors.

Value

This function returns a tibble with the following columns :

traceR_proteinGroups_input_df1 - proteinGroup denotations of input_df1 for common precursor between input_df1 and input_df2
traceR_precursor - common precursor between input_df1 and input_df2
traceR_proteinGroups_input_df2 - proteinGroup denotations of input_df2 for common precursor between input_df1 and input_df2

Author(s)

Oliver Kardell

Examples

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

# DIA-NN example data
diann <- tibble::tibble(
  "traceR_connected_pg_prec" = c("common_common", "common_unique",
  "unique_common", "unique_common"),
  "traceR_proteinGroups" = c("P02768", "P02671", "Q92496", "P04433"),
  "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "RLEVDIDIK2",
  "EGIVEYPR2", "ASQSVSSYLAWYQQK2"),
)

# Spectronaut example data
spectronaut <- tibble::tibble(
  "traceR_connected_pg_prec" = c("common_common", "common_unique",
  "unique_common", "unique_common"),
  "traceR_proteinGroups" = c("P02768", "P02671", "Q02985", "A0A0A0MRZ8;P04433"),
  "traceR_precursor" = c("AAC(UniMod:4)LLPK1", "M(UniMod:35)KPVPDLVPGNFK2",
  "EGIVEYPR2", "ASQSVSSYLAWYQQK2"),
)

# Find difference in pg denotation
# string_analysis = TRUE
resultA <- trace_unique_common_pg(input_df1 = diann,
 input_df2 = spectronaut,
 analysis_name1 = "DIA-NN",
 analysis_name2 = "Spectronaut",
 string_analysis = TRUE)

# Find difference in pg denotation
# string_analysis = FALSE
# compare with resultA
resultB <- trace_unique_common_pg(input_df1 = diann,
 input_df2 = spectronaut,
 analysis_name1 = "DIA-NN",
 analysis_name2 = "Spectronaut",
 string_analysis = FALSE)

flowTraceR: a package for standardization of level information and tracking inter-software differences in bottom-up label-free proteomics

Description

Author(s)

See Also

Analysis of connected levels

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Analysis of unknown modifications

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Connects traced levels

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Conversion of software specific levels

Description

Usage

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Value

Author(s)

Examples

Conversion of software specific modified peptide entries

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Conversion of software specific precursor entries

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Conversion of software specific proteinGroups

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Create example data

Description

Usage

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Value

Author(s)

Examples

Check of converted modifications

Description

Usage

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Details

Value

Author(s)

Examples

Trace common and unique identifications between different software outputs for all levels

Description

Usage

Arguments