Package 'mspms' reference manual

Title:	Tools for the analysis of MSP-MS data
Description:	This package provides functions for the analysis of data generated by the multiplex substrate profiling by mass spectrometry for proteases (MSP-MS) method. Data exported from upstream proteomics software is accepted as input and subsequently processed for analysis. Tools for statistical analysis, visualization, and interpretation of the data are provided.
Authors:	Charlie Bayne [aut, cre]
Maintainer:	Charlie Bayne <[email protected]>
License:	MIT + file LICENSE
Version:	0.99.6
Built:	2024-11-26 03:08:53 UTC
Source:	https://github.com/bioc/mspms

all_possible_8mers_from_228_library All possible 8mers from the standard (as of 26April2024) 228 MSP-MS peptide library (This is equivalent to the result of mspms::calculate_all_cleavages(mspms::peptide_library$real_cleavage_seq,n=4)) vector of the 14 AA peptides used in the library.

Description

all_possible_8mers_from_228_library All possible 8mers from the standard (as of 26April2024) 228 MSP-MS peptide library (This is equivalent to the result of mspms::calculate_all_cleavages(mspms::peptide_library$real_cleavage_seq,n=4)) vector of the 14 AA peptides used in the library.

Usage

all_possible_8mers_from_228_library
all_possible_8mers_from_228_library

Format

## 'all_possible_8mers_from_228_library' A vector with 2964 entries

Source

calculate_all_cleavages calculate all possible cleavages for a defined peptide library containing peptides of the same length.

Description

calculate_all_cleavages calculate all possible cleavages for a defined peptide library containing peptides of the same length.

Usage

calculate_all_cleavages(peptide_library_seqs, n_AA_after_cleavage = 4)
calculate_all_cleavages(peptide_library_seqs, n_AA_after_cleavage = 4)

Arguments

`peptide_library_seqs`	The sequences of each peptide in the peptide library. They should all be the same length.
`n_AA_after_cleavage`	The number of AA after (and before) the cleavage site to consider.

Value

a vector of all the possible cleavages for the peptide library sequences

Examples

calculate_all_cleavages(mspms::peptide_library$library_real_sequence,
  n_AA_after_cleavage = 4
)
calculate_all_cleavages(mspms::peptide_library$library_real_sequence,
  n_AA_after_cleavage = 4
)

check_file_is_valid_fragpipe Check to make sure the input data looks like the expected FragPipe file.

Description

check_file_is_valid_fragpipe Check to make sure the input data looks like the expected FragPipe file.

Usage

check_file_is_valid_fragpipe(fragpipe_data)
check_file_is_valid_fragpipe(fragpipe_data)

Arguments

fragpipe_data

combined_peptide.tsv file generated by FragPipe read into R.

Value

a stop command with a informative message if file looks unexpected. otherwise, nothing.

check_file_is_valid_pd Check to make sure the input data looks like the expected ProteomeDiscoverer file.

Description

check_file_is_valid_pd Check to make sure the input data looks like the expected ProteomeDiscoverer file.

Usage

check_file_is_valid_pd(pd_data)
check_file_is_valid_pd(pd_data)

Arguments

pd_data

PeptideGroups.txt file generated by ProteomeDiscover and read into R.

Value

a stop command with a informative message if file looks unexpected. otherwise, nothing.

colData A tibble containing the colData associated with an experiment to proc

Description

colData A tibble containing the colData associated with an experiment to proc

Usage

colData
colData

Format

## 'colData' A tibble: 42 × 4

Source

colData corresponding to cathepsin A-D MSP-MS experiment

generate_report

Description

wrapper function to generate an automatic .html report of a basic mspms analysis.

Usage

generate_report(
  prepared_data,
  peptide_library = mspms::peptide_library,
  n_residues = 4,
  outdir = getwd(),
  output_file = paste0(Sys.Date(), "_mspms_report.html")
)
generate_report(
  prepared_data,
  peptide_library = mspms::peptide_library,
  n_residues = 4,
  outdir = getwd(),
  output_file = paste0(Sys.Date(), "_mspms_report.html")
)

Arguments

`prepared_data`	a QFeatures object containing a SummarizedExperiment named "peptides".
`peptide_library`	peptide library used with experiment. Contains columns "library_id", "library_match_sequence", and "library_real_sequence".
`n_residues`	the number of amino acid residues before and after the cleavage site to generate a cleavage seq for.
`outdir`	the output directory you would like to render the report to.
`output_file`	the file name to export.

Value

a knited .html report of the mspms analysis.

Examples


generate_report(mspms::peaks_prepared_data)

generate_report(mspms::peaks_prepared_data)

log2fc_t_test

Description

Calculates the log2 fold change and t-test statistics given a user specified reference variable and value.

Usage

log2fc_t_test(processed_qf, reference_variable = "time", reference_value = 0)
log2fc_t_test(processed_qf, reference_variable = "time", reference_value = 0)

Arguments

`processed_qf`	mspms data in a QFeatures object.
`reference_variable`	the colData variable to use as reference
`reference_value`	the value of the colData variable to use as reference

Value

a tibble containing log2fc and t test statistics

Examples

log2fc_and_t_test <- log2fc_t_test(mspms::processed_qf)
log2fc_and_t_test <- log2fc_t_test(mspms::processed_qf)

log2fc_t_test_data A tibble containing the results of t-tests and log2fc compared to time 0 14,497 × 19

Description

log2fc_t_test_data A tibble containing the results of t-tests and log2fc compared to time 0 14,497 × 19

Usage

log2fc_t_test_data
log2fc_t_test_data

Format

## 'peaks_prepared_data' A tibble: 14,497 × 19

Source

mspms_tidy Convert a SummarizedExperiment object within a QFeatures object into a tidy tibble.

Description

mspms_tidy Convert a SummarizedExperiment object within a QFeatures object into a tidy tibble.

Usage

mspms_tidy(processed_qf, se_name = "peptides_norm")
mspms_tidy(processed_qf, se_name = "peptides_norm")

Arguments

`processed_qf`	a QFeature object containing rowData and colData.
`se_name`	the name of the SummarizedExperiment you would like to extract

Value

a tibble containing all the rowData, colData, and assay data for the specified SummarizedExperiment.

Examples

mspms_data <- mspms_tidy(mspms::processed_qf)
mspms_data <- mspms_tidy(mspms::processed_qf)

mspms_tidy_data A tibble containing tidy data derived from QFeatures object

Description

mspms_tidy_data A tibble containing tidy data derived from QFeatures object

Usage

mspms_tidy_data
mspms_tidy_data

Format

## 'mspms_tidy_data' A tibble:

Source

processed_qf

peaks_prepared_data A QFeatures object prepared from PEAKS data of cathepsin data/.

Description

peaks_prepared_data A QFeatures object prepared from PEAKS data of cathepsin data/.

Usage

peaks_prepared_data
peaks_prepared_data

Format

## 'peaks_prepared_data' An instance of class QFeatures containing 1 assays: [1] peptides: SummarizedExperiment with 2071 rows and 42 columns

peptides: Peptide Sequence Detected

...

Source

peptide_library

Description

This is the 228 peptide library used by the O’Donoghue lab as of 26April2024.

Usage

peptide_library
peptide_library

Format

## 'peptide_library' A data frame with 228 rows and 3 columns:

library_reference_id: reference id of the detected peptide as put in upstream software
library_match_sequence: the sequence match to the peptide library, methionine is replaced with norleucine,which should function the same as methionine for proteases but has the same mass as L
library_real_sequence: Ls corresponding to norleucine are replaced back with n (for norleucine )

...

Source

<O’Donoghue lab as of 26April2024 >

plot_all_icelogos

Description

Easily plot a iceLogo corresponding to peptides of interest across each condition of an experiment.

Usage

plot_all_icelogos(
  sig_cleavage_data,
  type = "percent_difference",
  pval = 0.05,
  background_universe = mspms::all_possible_8mers_from_228_library
)
plot_all_icelogos(
  sig_cleavage_data,
  type = "percent_difference",
  pval = 0.05,
  background_universe = mspms::all_possible_8mers_from_228_library
)

Arguments

`sig_cleavage_data`	a tibble of data of interest containing a column labeled peptide, cleavage_seq, and condition
`type`	this is the type of iceLogo you would like to generate, can be either "percent_difference" or "fold_change".
`pval`	this is the pvalue threshold (<=) to consider significant when determining the significance of the sig_cleavages relative to the background at each position of the iceLogo.
`background_universe`	this is a list cleavages you would like to compare to as background of the iceLogo

Value

a ggplot object that shows the motif of the cleavage sequences

Examples

# Determining cleavages of interest
sig_cleavage_data <- mspms::log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3)
# Plotting a iceLogo for each condition.
plot_all_icelogos(sig_cleavage_data)
# Determining cleavages of interest
sig_cleavage_data <- mspms::log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3)
# Plotting a iceLogo for each condition.
plot_all_icelogos(sig_cleavage_data)

plot_cleavages_per_pos

Description

plot the number of cleavages at each

Usage

plot_cleavages_per_pos(sig_cleavage_data, ncol = NULL)
plot_cleavages_per_pos(sig_cleavage_data, ncol = NULL)

Arguments

`sig_cleavage_data`	a tibble of data of interest containing a column labeled peptide, cleavage_seq, condition, and cleavage_pos.
`ncol`	the number of columns to plot.

Value

a ggplot2 object

Examples

# Defining the significant peptides
sig_cleavage_data <- log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3)
# Plotting
p1 <- mspms::plot_cleavages_per_pos(sig_cleavage_data)
p1
# Defining the significant peptides
sig_cleavage_data <- log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3)
# Plotting
p1 <- mspms::plot_cleavages_per_pos(sig_cleavage_data)
p1

plot_heatmap

Description

This produces a heatmaply interactive heatmap of the QFeatures object with color bars representing the condition and time for each sample in each row.

Usage

plot_heatmap(
  mspms_tidy_data,
  value_colname = "peptides_norm",
  scale = "column",
  plot_method = "plotly"
)
plot_heatmap(
  mspms_tidy_data,
  value_colname = "peptides_norm",
  scale = "column",
  plot_method = "plotly"
)

Arguments

`mspms_tidy_data`	tidy mspms data (prepared from QFeatures object by mspms_tidy())
`value_colname`	the name of the column containing values.
`scale`	how would you like the data scaled? default is none, but can also be "row", "column", or "none"
`plot_method`	what plot method would you like to use, can use plotly or ggplot2.

Details

Each column has a colored bar representing whether the peptide is a cleavage product or a full length member of the peptide library.

Value

a heatmaply interactive heatmap

Examples

plot_heatmap(mspms::mspms_tidy_data)
plot_heatmap(mspms::mspms_tidy_data)

plot_icelogo

Description

This function plots the cleavage motifs that were enriched relative to background as implemented in the iceLogo method. https://iomics.ugent.be/icelogoserver/resources/manual.pdf

Usage

plot_icelogo(
  cleavage_seqs,
  background_universe = mspms::all_possible_8mers_from_228_library,
  pval = 0.05,
  type = "percent_difference"
)
plot_icelogo(
  cleavage_seqs,
  background_universe = mspms::all_possible_8mers_from_228_library,
  pval = 0.05,
  type = "percent_difference"
)

Arguments

`cleavage_seqs`	these are the cleavage sequences of interest
`background_universe`	this is a list of cleavage sequences to use as the background in building the iceLogo.
`pval`	this is the pvalue threshold (<=) to consider significant when determining the significance of the sig_cleavages relative to the background at each position of the iceLogo.
`type`	this is the type of visualization you would like to perform, accepted values are either "percent_difference" or "fold_change".

Value

a ggplot2 object

Examples

# Determining significant cleavages for catA
catA_sig_cleavages <- mspms::log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3) %>%
  dplyr::filter(condition == "CatA") %>%
  dplyr::pull(cleavage_seq) %>%
  unique()

# Plotting icelogo
plot_icelogo(catA_sig_cleavages,
  background_universe = all_possible_8mers_from_228_library
)
# Determining significant cleavages for catA
catA_sig_cleavages <- mspms::log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3) %>%
  dplyr::filter(condition == "CatA") %>%
  dplyr::pull(cleavage_seq) %>%
  unique()

# Plotting icelogo
plot_icelogo(catA_sig_cleavages,
  background_universe = all_possible_8mers_from_228_library
)

plot_nd_peptides

Description

plot the percentage of samples each peptide from library was undetected in (if the percentage is > 0).

Usage

plot_nd_peptides(
  processed_qf,
  peptide_library_ids = mspms::peptide_library$library_id
)
plot_nd_peptides(
  processed_qf,
  peptide_library_ids = mspms::peptide_library$library_id
)

Arguments

`processed_qf`	a QFeatures object containing a SummarizedExperiment named "peptides"
`peptide_library_ids`	a vector of all peptide library ids in the experiment.

Value

a ggplot2 object

Examples

plot_nd_peptides(mspms::processed_qf)
plot_nd_peptides(mspms::processed_qf)

plot_pca

Description

Easily create a PCA plot from a QFeatures object containing mspms data. Ellipses are drawn around the points at a 95 Shape and colors are user specified.

Usage

plot_pca(
  mspms_tidy_data,
  value_colname = "peptides_norm",
  color = "time",
  shape = "condition"
)
plot_pca(
  mspms_tidy_data,
  value_colname = "peptides_norm",
  color = "time",
  shape = "condition"
)

Arguments

`mspms_tidy_data`	tidy mspms data (prepared from QFeatures object by mspms_tidy)
`value_colname`	the name of the column containing values.
`color`	the name of the variable you would like to color by.
`shape`	the name of the variable that you would like to determine shape by.

Value

a ggplot2 object

Examples

plot_pca(mspms::mspms_tidy_data)
plot_pca(mspms::mspms_tidy_data)

plot_qc_check plot the the percentage of the peptide library undetected in each sample per each sample group.

Description

plot_qc_check plot the the percentage of the peptide library undetected in each sample per each sample group.

Usage

plot_qc_check(
  processed_qf,
  peptide_library = mspms::peptide_library$library_id,
  full_length_threshold = NULL,
  cleavage_product_threshold = NULL,
  ncol = 2
)
plot_qc_check(
  processed_qf,
  peptide_library = mspms::peptide_library$library_id,
  full_length_threshold = NULL,
  cleavage_product_threshold = NULL,
  ncol = 2
)

Arguments

`processed_qf`	QFeatures object containing a SummarizedExperiment named "peptides"
`peptide_library`	a vector of all peptide library ids in the experiment.
`full_length_threshold`	percent to use as threshold visualized as a vertical blue dashed line
`cleavage_product_threshold`	percent to use as a threshold visualized as a red dashed line
`ncol`	n columns.

Value

a ggplot2 object.

Examples

plot_qc_check(mspms::processed_qf)
plot_qc_check(mspms::processed_qf)

plot_time_course

Description

Easily plot a time course of all peptides in a QFeatures object by peptide.

Usage

plot_time_course(
  mspms_tidy_data,
  value_colname = "peptides_norm",
  summarize_by_mean = FALSE
)
plot_time_course(
  mspms_tidy_data,
  value_colname = "peptides_norm",
  summarize_by_mean = FALSE
)

Arguments

`mspms_tidy_data`	tidy mspms data (prepared from QFeatures object by mspms_tidy())
`value_colname`	the name of the column containing values.
`summarize_by_mean`	whether to summarise by mean (TRUE- show error bars +- 1 standard deviation) or not (FALSE)

Value

a ggplot2 object

Examples

# Determining peptide of interest
max_log2fc_pep <- mspms::log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3) %>%
  dplyr::filter(log2fc == max(log2fc)) %>%
  dplyr::pull(peptide)

# Defining QFeatures filter
filtered <- mspms::mspms_tidy_data %>%
  dplyr::filter(peptide == max_log2fc_pep) %>%
  plot_time_course()
# Determining peptide of interest
max_log2fc_pep <- mspms::log2fc_t_test_data %>%
  dplyr::filter(p.adj <= 0.05, log2fc > 3) %>%
  dplyr::filter(log2fc == max(log2fc)) %>%
  dplyr::pull(peptide)

# Defining QFeatures filter
filtered <- mspms::mspms_tidy_data %>%
  dplyr::filter(peptide == max_log2fc_pep) %>%
  plot_time_course()

plot_volcano

Description

create a volcano plot to generate log2fc and adjusted p values for experimental conditions

Usage

plot_volcano(
  log2fc_t_test_data,
  log2fc_threshold = 3,
  padj_threshold = 0.05,
  facets = "grid",
  ncol = 1
)
plot_volcano(
  log2fc_t_test_data,
  log2fc_threshold = 3,
  padj_threshold = 0.05,
  facets = "grid",
  ncol = 1
)

Arguments

`log2fc_t_test_data`	a tibble containing the log2fc and adjusted p values
`log2fc_threshold`	the log2fc threshold that you want displayed on plot
`padj_threshold`	the padj threshold that you want displayed on plot
`facets`	how facets should be displayed. Accepted values are grid and wrap
`ncol`	ncol to include if facets = "wrap"

Value

a ggplot2 object

Examples

p1 <- mspms::plot_volcano(mspms::log2fc_t_test_data, log2fc_threshold = 3)
p1
p1 <- mspms::plot_volcano(mspms::log2fc_t_test_data, log2fc_threshold = 3)
p1

prepare_fragpipe

Description

Prepare a label free quantification file exported from Fragpipe for subsequent mspms analysis.

Usage

prepare_fragpipe(
  combined_peptide_filepath,
  colData_filepath,
  peptide_library = mspms::peptide_library,
  n_residues = 4
)
prepare_fragpipe(
  combined_peptide_filepath,
  colData_filepath,
  peptide_library = mspms::peptide_library,
  n_residues = 4
)

Arguments

`combined_peptide_filepath`	file path the combined_peptide.tsv file generated by FragPipe.
`colData_filepath`	file path to .csv file containing colData. Must have columns named "quantCols","group","condition",and "time".
`peptide_library`	peptide library used with experiment. Contains columns "library_id", "library_match_sequence", and "library_real_sequence".
`n_residues`	the number of amino acid residues before and after the cleavage site to generate a cleavage seq for.

Value

a QFeatures object containing a summarizedExperiment named "peptides"

Examples

fragpipe_combined_peptide <- system.file("extdata/fragpipe_combined_peptide.tsv", package = "mspms")
colData_filepath <- system.file("extdata/colData.csv", package = "mspms")
# Prepare the data
fragpipe_prepared_data <- mspms::prepare_fragpipe(fragpipe_combined_peptide, colData_filepath)
fragpipe_combined_peptide <- system.file("extdata/fragpipe_combined_peptide.tsv", package = "mspms")
colData_filepath <- system.file("extdata/colData.csv", package = "mspms")
# Prepare the data
fragpipe_prepared_data <- mspms::prepare_fragpipe(fragpipe_combined_peptide, colData_filepath)

prepare_pd Prepare a label free quantification file exported from Proteome Discoverer for subsequent mspms analysis.

Description

prepare_pd Prepare a label free quantification file exported from Proteome Discoverer for subsequent mspms analysis.

Usage

prepare_pd(
  peptide_groups_filepath,
  colData_filepath,
  peptide_library = mspms::peptide_library,
  n_residues = 4
)
prepare_pd(
  peptide_groups_filepath,
  colData_filepath,
  peptide_library = mspms::peptide_library,
  n_residues = 4
)

Arguments

`peptide_groups_filepath`	filepath to PeptideGroups.txt file exported from proteome discoverer.
`colData_filepath`	file path to .csv file containing colData. Must have columns named "quantCols","group","condition",and "time".
`peptide_library`	peptide library used with experiment. Contains columns "library_id", "library_match_sequence", and "library_real_sequence".
`n_residues`	the number of amino acid residues before and after the cleavage site to generate a cleavage seq for.

Value

a QFeatures object containing a summarizedExperiment named "peptides"

Examples

peptide_groups_filepath <- system.file(
  "extdata/proteome_discoverer_PeptideGroups.txt",
  package = "mspms"
)
colData_filepath <- system.file("extdata/colData.csv", package = "mspms")
peptide_groups_filepath <- system.file(
  "extdata/proteome_discoverer_PeptideGroups.txt",
  package = "mspms"
)
colData_filepath <- system.file("extdata/colData.csv", package = "mspms")

prepare_peaks Prepare a label free quantification file exported from PEAKS for subsequent mspms analysis.

Description

prepare_peaks Prepare a label free quantification file exported from PEAKS for subsequent mspms analysis.

Usage

prepare_peaks(
  lfq_filepath,
  colData_filepath,
  quality_threshold = 0.3,
  peptide_library = mspms::peptide_library,
  n_residues = 4
)
prepare_peaks(
  lfq_filepath,
  colData_filepath,
  quality_threshold = 0.3,
  peptide_library = mspms::peptide_library,
  n_residues = 4
)

Arguments

`lfq_filepath`	this is the file path to a .csv file exported from PEAKS
`colData_filepath`	file path to .csv file containing colData. Must have columns named "quantCols","group","condition",and "time".
`quality_threshold`	only consider peptides with quality scores > than this threshold.
`peptide_library`	peptide library used in the experiment.
`n_residues`	the number of amino acid residues before and after the cleavage site to generate a cleavage seq for.

Value

a QFeatures object containing a summarizedExperiment named "peptides"

Examples

lfq_filepath <- system.file("extdata/peaks_protein-peptides-lfq.csv", package = "mspms")
colData_filepath <- system.file("extdata/colData.csv", package = "mspms")
# Prepare the data
peaks_prepared_data <- mspms::prepare_peaks(lfq_filepath, colData_filepath)
lfq_filepath <- system.file("extdata/peaks_protein-peptides-lfq.csv", package = "mspms")
colData_filepath <- system.file("extdata/colData.csv", package = "mspms")
# Prepare the data
peaks_prepared_data <- mspms::prepare_peaks(lfq_filepath, colData_filepath)

process_qf

Description

process_qf

Usage

process_qf(prepared_qf)
process_qf(prepared_qf)

Arguments

prepared_qf

this is a QFeatures object containing a SummarizedExperiment named "peptides"

Value

a QFeatures object containing a SummarizedExperiments named "peptides","peptides_log","peptides_log_norm", "peptides_log_impute_norm",and "peptides_norm"

Examples

processed_qf <- process_qf(mspms::peaks_prepared_data)
processed_qf <- process_qf(mspms::peaks_prepared_data)

processed_qf A QFeatures object prepared from PEAKS data of Cathepsin data that has been processed (imputation/normalization)

Description

processed_qf A QFeatures object prepared from PEAKS data of Cathepsin data that has been processed (imputation/normalization)

Usage

processed_qf
processed_qf

Format

## 'peaks_prepared_data' An instance of class QFeatures containing 5 assays: [1] peptides: SummarizedExperiment with 2071 rows and 42 columns [2] peptides_log: SummarizedExperiment with 2071 rows and 42 columns [3] peptides_log_norm: SummarizedExperiment with 2071 rows and 42 columns [4] peptides_log_impute_norm: SummarizedExperiment with 2071 rows and 42 columns [5] peptides_norm: SummarizedExperiment with 2071 rows and 42 columns

peptides: Peptide Sequence Detected

...

Source

Package 'mspms'

Help Index

all_possible_8mers_from_228_library All possible 8mers from the standard (as of 26April2024) 228 MSP-MS peptide library (This is equivalent to the result of mspms::calculate_all_cleavages(mspms::peptide_library$real_cleavage_seq,n=4)) vector of the 14 AA peptides used in the library.

Description

Usage

Format

Source

calculate_all_cleavages calculate all possible cleavages for a defined peptide library containing peptides of the same length.

Description

Usage

Arguments

Value

Examples

check_file_is_valid_fragpipe Check to make sure the input data looks like the expected FragPipe file.

Description

Usage

Arguments

Value

check_file_is_valid_pd Check to make sure the input data looks like the expected ProteomeDiscoverer file.

Description

Usage

Arguments

Value

colData A tibble containing the colData associated with an experiment to proc

Description

Usage

Format

Source

generate_report

Description

Usage

Arguments

Value

Examples

log2fc_t_test

Description

Usage

Arguments

Value

Examples

log2fc_t_test_data A tibble containing the results of t-tests and log2fc compared to time 0 14,497 × 19

Description

Usage

Format

Source

mspms_tidy Convert a SummarizedExperiment object within a QFeatures object into a tidy tibble.

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mspms_tidy_data A tibble containing tidy data derived from QFeatures object

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peaks_prepared_data A QFeatures object prepared from PEAKS data of cathepsin data/.

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peptide_library

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plot_all_icelogos

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plot_cleavages_per_pos

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plot_heatmap

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