Package 'MSstatsConvert'

Clean raw Proteome Discoverer data

Description

Clean raw Proteome Discoverer data

Usage

.cleanRawPD(
  msstats_object,
  quantification_column,
  protein_id_column,
  sequence_column,
  remove_shared,
  remove_protein_groups = TRUE,
  intensity_columns_regexp = "Abundance"
)

Arguments

msstats_object	an object of class MSstatsSpectroMineFiles.
quantification_column	chr, name of a column used for quantification.
protein_id_column	chr, name of a column with protein IDs.
sequence_column	chr, name of a column with peptide sequences.
remove_shared	lgl, if TRUE, shared peptides will be removed.
remove_protein_groups	if TRUE, proteins with numProteins > 1 will be removed.
intensity_columns_regexp	regular expressions that defines intensity columns. Defaults to "Abundance", which means that columns that contain the word "Abundance" will be treated as corresponding to intensities for different channels.

Value

data.table

---

Convert output of converters to data.frame

Description

Convert output of converters to data.frame

Usage

## S3 method for class 'MSstatsValidated'
as.data.frame(x, ...)

Arguments

x	object of class MSstatsValidated
...	Additional arguments to be passed to or from other methods.

Value

data.frame

---

Convert output of converters to data.table

Description

Convert output of converters to data.table

Usage

## S3 method for class 'MSstatsValidated'
as.data.table(x, ...)

Arguments

x	object of class MSstatsValidated
...	Additional arguments to be passed to or from other methods.

Value

data.tables

---

Takes as input the output of the SpectronauttoMSstatsFormat function and calculates various quality metrics to assess the health of the data. Requires Anomaly Detection model to be fit.

Description

Takes as input the output of the SpectronauttoMSstatsFormat function and calculates various quality metrics to assess the health of the data. Requires Anomaly Detection model to be fit.

Usage

CheckDataHealth(input)

Arguments

input

MSstats input which is the output of Spectronaut converter

Value

list of two data.tables

---

Import Diann files

Description

Import Diann files

Usage

DIANNtoMSstatsFormat(
  input,
  annotation = NULL,
  global_qvalue_cutoff = 0.01,
  qvalue_cutoff = 0.01,
  pg_qvalue_cutoff = 0.01,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeOxidationMpeptides = TRUE,
  removeProtein_with1Feature = TRUE,
  MBR = TRUE,
  labeledAminoAcids = NULL,
  quantificationColumn = "FragmentQuantCorrected",
  calculateAnomalyScores = FALSE,
  anomalyModelFeatures = c(),
  anomalyModelFeatureTemporal = c(),
  removeMissingFeatures = 0.5,
  anomalyModelFeatureCount = 100,
  runOrder = NULL,
  n_trees = 100,
  max_depth = "auto",
  numberOfCores = 1,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of MSstats input report from Diann, which includes fragment-level data. Output fragment data with –export-quant flag in DIA-NN 2.0
annotation	name of 'annotation.txt' data which includes Condition, BioReplicate, Run.
global_qvalue_cutoff	The qvalue cutoff for the Q.Value column, i.e. the run-specific precursor q-value. Default is 0.01.
qvalue_cutoff	If MBR is false, the qvalue cutoff for the Global.Q.Value column, i.e. global precursor q-value. If MBR is true, the qvalue cutoff for the Lib.Q.Value column, i.e. the q-value for the library created after the first MBR pass. Default is 0.01.
pg_qvalue_cutoff	If MBR is false, the qvalue cutoff for the Global.PG.Q.Value column, i.e. the global q-value for the protein group. If MBR is true, the qvalue cutoff for the Lib.PG.Q.Value column, i.e. the protein group q-value for the library created after the first MBR pass. Default is 0.01.
useUniquePeptide	should unique peptides be removed
removeFewMeasurements	should proteins with few measurements be removed
removeOxidationMpeptides	should peptides with oxidation be removed
removeProtein_with1Feature	should proteins with a single feature be removed
MBR	True if analysis was done with match between runs
labeledAminoAcids	Character vector of single-letter amino acid codes that carry the SILAC label in protein turnover experiments, e.g. c("K") or c("K", "R"). Supplying this vector opts in to protein-turnover mode; the exact amino acids determine behaviour only in the ModifiedSequence-parsing path described below. Channel-based path (DIA-NN 2.x exports that include a Channel column): when labeledAminoAcids is non-NULL and the input contains a Channel column, Channel values are mapped directly to IsotopeLabelType ("H" → "H", "L" → "L", anything else → NA). The amino acid codes in labeledAminoAcids are not used to validate or filter ModifiedSequence in this path. ModifiedSequence-parsing path (DIA-NN 1.x exports without a Channel column): when labeledAminoAcids is non-NULL and no Channel column is present, each ModifiedSequence is inspected for SILAC suffixes of the form (SILAC-<AA>-H) or (SILAC-<AA>-L), where <AA> is one of the supplied amino acid codes. Matching sequences are classified as "H" or "L"; sequences carrying neither suffix receive IsotopeLabelType = NA. The SILAC suffix is then stripped from PeptideSequence. When NULL (default), protein-turnover mode is disabled and all peptides receive IsotopeLabelType = "Light".
quantificationColumn	Use 'FragmentQuantCorrected'(default) column for quantified intensities for DIANN 1.8.x. Use 'FragmentQuantRaw' for quantified intensities for DIANN 1.9.x. Use 'auto' for quantified intensities for DIANN 2.x where each fragment intensity is a separate column, e.g. Fr0Quantity.
calculateAnomalyScores	Default is FALSE. If TRUE, will run anomaly detection model and calculate anomaly scores for each feature. Used downstream to weigh measurements in differential analysis.
anomalyModelFeatures	character vector of quality metric column names to be used as features in the anomaly detection model. List must not be empty if calculateAnomalyScores=TRUE.
anomalyModelFeatureTemporal	character vector of temporal direction corresponding to columns passed to anomalyModelFeatures. Values must be one of: mean_decrease, mean_increase, dispersion_increase, or NULL (to perform no temporal feature engineering). Default is empty vector. If calculateAnomalyScores=TRUE, vector must have as many values as anomalyModelFeatures (even if all NULL).
removeMissingFeatures	Remove features with missing values in more than this fraction of runs. Default is 0.5. Only used if calculateAnomalyScores=TRUE.
anomalyModelFeatureCount	Feature selection for anomaly model. Anomaly detection works on the precursor-level and can be much slower if all features used. We will by default filter to the top-100 highest intensity features. This can be adjusted as necessary. To turn feature-selection off, set this value to a high number (e.g. 10000). Only used if calculateAnomalyScores=TRUE.
runOrder	Temporal order of MS runs. Should be a two column data.table with columns Run and Order, where Run matches the run name output by DIA-NN and Order is an integer. Used to engineer the temporal features defined in anomalyModelFeatureTemporal.
n_trees	Number of trees to use in isolation forest when calculateAnomalyScores=TRUE. Default is 100.
max_depth	Max tree depth to use in isolation forest when calculateAnomalyScores=TRUE. Default is "auto" which calculates depth as log2(N) where N is the number of runs. Otherwise must be an integer.
numberOfCores	Number of cores for parallel processing anomaly detection model. When > 1, a logfile named 'MSstats_anomaly_model_progress.log' is created to track progress. Only works for Linux & Mac OS. Default is 1.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Elijah Willie

Examples

input_file_path = system.file("tinytest/raw_data/DIANN/diann_input.tsv", 
                                package="MSstatsConvert")
annotation_file_path = system.file("tinytest/raw_data/DIANN/annotation.csv", 
                                package = "MSstatsConvert")
input = data.table::fread(input_file_path)
annot = data.table::fread(annotation_file_path)
output = DIANNtoMSstatsFormat(input, annotation = annot, MBR = FALSE, 
                                use_log_file = FALSE)
head(output)

# For DIANN 2.0, set quantificationColumn = 'auto'
input_file_path_2_0 = system.file("tinytest/raw_data/DIANN/diann_2.0.parquet", 
                                package="MSstatsConvert")
annotation_file_path_2_0 = system.file("tinytest/raw_data/DIANN/annotation_diann_2.0.csv", 
                                package = "MSstatsConvert")
input_2_0 = arrow::read_parquet(input_file_path_2_0)
annot_2_0 = data.table::fread(annotation_file_path_2_0)
output_2_0 = DIANNtoMSstatsFormat(input_2_0, annotation = annot_2_0, MBR = FALSE, 
                                use_log_file = FALSE, quantificationColumn = 'auto')
head(output_2_0)

---

Import DIA-Umpire files

Description

Import DIA-Umpire files

Usage

DIAUmpiretoMSstatsFormat(
  raw.frag,
  raw.pep,
  raw.pro,
  annotation,
  useSelectedFrag = TRUE,
  useSelectedPep = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

raw.frag	name of FragSummary_date.xls data, which includes feature-level data.
raw.pep	name of PeptideSummary_date.xls data, which includes selected fragments information.
raw.pro	name of ProteinSummary_date.xls data, which includes selected peptides information.
annotation	name of annotation data which includes Condition, BioReplicate, Run information.
useSelectedFrag	TRUE will use the selected fragment for each peptide. 'Selected_fragments' column is required.
useSelectedPep	TRUE will use the selected peptide for each protein. 'Selected_peptides' column is required.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Meena Choi, Olga Vitek

Examples

diau_frag = system.file("tinytest/raw_data/DIAUmpire/dia_frag.csv", 
                             package = "MSstatsConvert")
diau_pept = system.file("tinytest/raw_data/DIAUmpire/dia_pept.csv", 
                             package = "MSstatsConvert")
diau_prot = system.file("tinytest/raw_data/DIAUmpire/dia_prot.csv", 
                             package = "MSstatsConvert")
annot = system.file("tinytest/raw_data/DIAUmpire/annot_diau.csv", 
                    package = "MSstatsConvert")
diau_frag = data.table::fread(diau_frag) 
diau_pept = data.table::fread(diau_pept) 
diau_prot = data.table::fread(diau_prot) 
annot = data.table::fread(annot)
diau_frag = diau_frag[, lapply(.SD, function(x) if (is.integer(x)) as.numeric(x) else x)]
# In case numeric columns are not interpreted correctly

diau_imported = DIAUmpiretoMSstatsFormat(diau_frag, diau_pept, diau_prot, 
                                         annot, use_log_file = FALSE)
head(diau_imported)

---

Import FragPipe files

Description

Import FragPipe files

Usage

FragPipetoMSstatsFormat(
  input,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of FragPipe msstats.csv export. ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity are required.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Devon Kohler

Examples

fragpipe_raw = system.file("tinytest/raw_data/FragPipe/fragpipe_input.csv",
                              package = "MSstatsConvert")
fragpipe_raw = data.table::fread(fragpipe_raw)
fragpipe_imported = FragPipetoMSstatsFormat(fragpipe_raw, use_log_file = FALSE)
head(fragpipe_imported)

---

Get one of files contained in an instance of MSstatsInputFiles class.

Description

Get one of files contained in an instance of MSstatsInputFiles class.

Usage

getInputFile(msstats_object, file_type)

## S4 method for signature 'MSstatsInputFiles'
getInputFile(msstats_object, file_type = "input")

## S4 method for signature 'MSstatsPhilosopherFiles'
getInputFile(msstats_object, file_type = "input")

Arguments

msstats_object	object that inherits from MSstatsPhilosopherFiles class.
file_type	character name of a type file. Usually equal to "input".

Value

data.table

data.table

data.table

Examples

evidence_path = system.file("tinytest/raw_data/MaxQuant/mq_ev.csv", 
                            package = "MSstatsConvert")
pg_path = system.file("tinytest/raw_data/MaxQuant/mq_pg.csv", 
                      package = "MSstatsConvert")
evidence = read.csv(evidence_path)
pg = read.csv(pg_path)
imported = MSstatsImport(list(evidence = evidence, protein_groups = pg),
                         "MSstats", "MaxQuant")
class(imported)
head(getInputFile(imported, "evidence"))

---

Import MaxQuant files

Description

Import MaxQuant files

Usage

MaxQtoMSstatsFormat(
  evidence,
  annotation,
  proteinGroups,
  proteinID = "Proteins",
  useUniquePeptide = TRUE,
  summaryforMultipleRows = max,
  removeFewMeasurements = TRUE,
  removeMpeptides = FALSE,
  removeOxidationMpeptides = FALSE,
  removeProtein_with1Peptide = FALSE,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

evidence	name of 'evidence.txt' data, which includes feature-level data.
annotation	name of 'annotation.txt' data which includes Raw.file, Condition, BioReplicate, Run, IsotopeLabelType information.
proteinGroups	name of 'proteinGroups.txt' data. It needs to matching protein group ID. If proteinGroups=NULL, use 'Proteins' column in 'evidence.txt'.
proteinID	'Proteins'(default) or 'Leading.razor.protein' for Protein ID.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeMpeptides	TRUE will remove the peptides including 'M' sequence. FALSE is default.
removeOxidationMpeptides	TRUE will remove the peptides including 'oxidation (M)' in modification. FALSE is default.
removeProtein_with1Peptide	TRUE will remove the proteins which have only 1 peptide and charge. FALSE is default.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Note

Warning: MSstats does not support for metabolic labeling or iTRAQ experiments.

Author(s)

Meena Choi, Olga Vitek.

Examples

mq_ev = data.table::fread(system.file("tinytest/raw_data/MaxQuant/mq_ev.csv",
                                      package = "MSstatsConvert"))
mq_pg = data.table::fread(system.file("tinytest/raw_data/MaxQuant/mq_pg.csv",
                                      package = "MSstatsConvert"))
annot = data.table::fread(system.file("tinytest/raw_data/MaxQuant/annotation.csv",
                                      package = "MSstatsConvert"))
maxq_imported = MaxQtoMSstatsFormat(mq_ev, annot, mq_pg, use_log_file = FALSE)
head(maxq_imported)

---

Generate MSstatsTMT required input format from MaxQuant output

Description

Generate MSstatsTMT required input format from MaxQuant output

Usage

MaxQtoMSstatsTMTFormat(
  evidence,
  proteinGroups,
  annotation,
  which.proteinid = "Proteins",
  rmProt_Only.identified.by.site = FALSE,
  useUniquePeptide = TRUE,
  rmPSM_withfewMea_withinRun = TRUE,
  rmProtein_with1Feature = FALSE,
  summaryforMultipleRows = sum,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

evidence	name of 'evidence.txt' data, which includes feature-level data.
proteinGroups	name of 'proteinGroups.txt' data.
annotation	data frame which contains column Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition. Refer to the example 'annotation.mq' for the meaning of each column.
which.proteinid	Use 'Proteins' (default) column for protein name. 'Leading.proteins' or 'Leading.razor.proteins' or 'Gene.names' can be used instead to get the protein ID with single protein. However, those can potentially have the shared peptides.
rmProt_Only.identified.by.site	TRUE will remove proteins with '+' in 'Only.identified.by.site' column from proteinGroups.txt, which was identified only by a modification site. FALSE is the default.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
rmPSM_withfewMea_withinRun	TRUE (default) will remove the features that have 1 or 2 measurements within each Run.
rmProtein_with1Feature	TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame of class "MSstatsTMT"

Examples

evidence = data.table::fread(system.file("tinytest/raw_data/MaxQuantTMT/mq_ev.csv",
                                      package = "MSstatsConvert"))
proteinGroups = data.table::fread(system.file("tinytest/raw_data/MaxQuantTMT/mq_pg.csv",
                                      package = "MSstatsConvert"))
annotation.mq = data.table::fread(system.file("tinytest/raw_data/MaxQuantTMT/mq_annotation.csv",
                                      package = "MSstatsConvert"))
input.mq <- MaxQtoMSstatsTMTFormat(evidence, proteinGroups, annotation.mq)
head(input.mq)

---

Import Metamorpheus files

Description

Import Metamorpheus files

Usage

MetamorpheusToMSstatsFormat(
  input,
  annotation = NULL,
  MBR = TRUE,
  qvalue_cutoff = 0.05,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of Metamorpheus output file, which is tabular format. Use the AllQuantifiedPeaks.tsv file from the Metamorpheus output.
annotation	name of 'annotation.txt' data which includes Condition, BioReplicate.
MBR	If TRUE, the function will include peaks detected by MBR
qvalue_cutoff	The q-value cutoff for filtering peaks detected by MBR
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Anthony Wu

Examples

input = system.file("tinytest/raw_data/Metamorpheus/QuantifiedPeaks.tsv", 
                                package = "MSstatsConvert")
input = data.table::fread(input)
annot = system.file("tinytest/raw_data/Metamorpheus/annotation.csv", 
                                package = "MSstatsConvert")
annot = data.table::fread(annot)
metamorpheus_imported = MSstatsConvert:::MetamorpheusToMSstatsFormat(input, annotation = annot)
head(metamorpheus_imported)

---

Run Anomaly Model

Description

Detects anomalous measurements in mass spectrometry data using an isolation forest algorithm. This function identifies unusual precursor measurements based on quality metrics and their temporal patterns. For features with insufficient quality metric data, it assigns anomaly scores based on the median score of similar features (same peptide and charge combination). The model supports parallel processing for improved performance on large datasets.

Usage

MSstatsAnomalyScores(
  input,
  quality_metrics,
  temporal_direction,
  missing_run_count,
  n_feat,
  run_order,
  n_trees,
  max_depth,
  cores
)

Arguments

input	data.table preprocessed by the MSstatsBalancedDesign function
quality_metrics	character vector of quality metrics to use in the model
temporal_direction	character vector of same length as quality_metrics indicating temporal feature to create.
missing_run_count	numeric, maximum allowed fraction of missing runs per feature.
n_feat	numeric, maximum number of features per protein to use in the model.
run_order	data.frame with two columns: Run and Order. Order should be numeric and indicate the order of runs.
n_trees	numeric, number of trees to use in the isolation forest model. Default is 100.
max_depth	numeric or "auto", maximum depth of each tree. Default is "auto" which sets depth to log2(N) where N is the number of runs.
cores	numeric, number of cores to use for parallel processing. Default is 1.

Value

data.table

---

Creates balanced design by removing overlapping fractions and filling incomplete rows

Description

Creates balanced design by removing overlapping fractions and filling incomplete rows

Usage

MSstatsBalancedDesign(
  input,
  feature_columns,
  fill_incomplete = TRUE,
  handle_fractions = TRUE,
  fix_missing = NULL,
  remove_few = TRUE,
  anomaly_metrics = c()
)

Arguments

input	data.table processed by the MSstatsPreprocess function
feature_columns	str, names of columns that define spectral features
fill_incomplete	if TRUE (default), ensures that rows with missing data for specific features are added as NA. For example, if the y10 ion of peptideA is measured in the "disease" samples but entirely missing for the "healthy" samples, rows with NA values will be created for the y10 ion of peptideA in the "healthy" group. This process increases the number of rows to account for all possible feature-sample combinations.
handle_fractions	if TRUE (default), overlapping fractions will be resolved
fix_missing	str, optional. Defaults to NULL, which means no action. If not NULL, must be one of the options: "zero_to_na" or "na_to_zero". If "zero_to_na", Intensity values equal exactly to 0 will be converted to NA. If "na_to_zero", missing values will be replaced by zeros.
remove_few	lgl, if TRUE, features with one or two measurements across runs will be removed.
anomaly_metrics	character vector of names of columns with quality metrics

Value

data.frame of class MSstatsValidated

Examples

unbalanced_data = system.file("tinytest/raw_data/unbalanced_data.csv", 
                              package = "MSstatsConvert")
unbalanced_data = data.table::as.data.table(read.csv(unbalanced_data))
balanced = MSstatsBalancedDesign(unbalanced_data, 
                                 c("PeptideSequence", "PrecursorCharge",
                                   "FragmentIon", "ProductCharge"))
dim(balanced) # Now balanced has additional rows (with Intensity = NA)
# for runs that were not included in the unbalanced_data table

---

Clean files generated by a signal processing tools.

Description

Clean files generated by a signal processing tools.

Clean DIAUmpire files

Clean MaxQuant files

Clean OpenMS files

Clean OpenSWATH files

Clean Progenesis files

Clean ProteomeDiscoverer files

Clean Skyline files

Clean SpectroMine files

Clean Spectronaut files

Clean Philosopher files

Clean DIA-NN files

Clean Metamorpheus files

Clean Protein Prospector files

Clean MZMine files

Usage

MSstatsClean(msstats_object, ...)

## S4 method for signature 'MSstatsDIAUmpireFiles'
MSstatsClean(msstats_object, use_frag, use_pept)

## S4 method for signature 'MSstatsMaxQuantFiles'
MSstatsClean(
  msstats_object,
  protein_id_col,
  remove_by_site = FALSE,
  channel_columns = "Reporterintensitycorrected"
)

## S4 method for signature 'MSstatsOpenMSFiles'
MSstatsClean(msstats_object)

## S4 method for signature 'MSstatsOpenSWATHFiles'
MSstatsClean(msstats_object)

## S4 method for signature 'MSstatsProgenesisFiles'
MSstatsClean(msstats_object, runs, fix_colnames = TRUE)

## S4 method for signature 'MSstatsProteomeDiscovererFiles'
MSstatsClean(
  msstats_object,
  quantification_column,
  protein_id_column,
  sequence_column,
  remove_shared,
  remove_protein_groups = TRUE,
  intensity_columns_regexp = "Abundance"
)

## S4 method for signature 'MSstatsSkylineFiles'
MSstatsClean(msstats_object)

## S4 method for signature 'MSstatsSpectroMineFiles'
MSstatsClean(msstats_object)

## S4 method for signature 'MSstatsSpectronautFiles'
MSstatsClean(
  msstats_object,
  intensity,
  calculateAnomalyScores,
  anomalyModelFeatures,
  peptideSequenceColumn = "EG.ModifiedSequence",
  heavyLabels = NULL
)

## S4 method for signature 'MSstatsPhilosopherFiles'
MSstatsClean(
  msstats_object,
  protein_id_col,
  peptide_id_col,
  channels,
  remove_shared_peptides
)

## S4 method for signature 'MSstatsDIANNFiles'
MSstatsClean(
  msstats_object,
  MBR = TRUE,
  quantificationColumn = "FragmentQuantCorrected",
  global_qvalue_cutoff = 0.01,
  qvalue_cutoff = 0.01,
  pg_qvalue_cutoff = 0.01,
  calculateAnomalyScores = FALSE,
  anomalyModelFeatures = c(),
  labeledAminoAcids = NULL
)

## S4 method for signature 'MSstatsMetamorpheusFiles'
MSstatsClean(msstats_object, MBR = TRUE, qvalue_cutoff = 0.05)

## S4 method for signature 'MSstatsProteinProspectorFiles'
MSstatsClean(msstats_object)

## S4 method for signature 'MSstatsMZMineFiles'
MSstatsClean(msstats_object, mzmine_annotations)

Arguments

msstats_object	object that inherits from MSstatsInputFiles class.
...	additional parameter to specific cleaning functions.
use_frag	TRUE will use the selected fragment for each peptide. 'Selected_fragments' column is required.
use_pept	TRUE will use the selected fragment for each protein 'Selected_peptides' column is required.
protein_id_col	character, name of a column with names of proteins.
remove_by_site	logical, if TRUE, proteins only identified by site will be removed.
channel_columns	character, regular expression that identifies channel columns in TMT data.
runs	chr, vector of Run labels.
fix_colnames	lgl, if TRUE, one of the rows will be used as colnames.
quantification_column	chr, name of a column used for quantification.
protein_id_column	chr, name of a column with protein IDs.
sequence_column	chr, name of a column with peptide sequences.
remove_shared	lgl, if TRUE, shared peptides will be removed.
remove_protein_groups	if TRUE, proteins with numProteins > 1 will be removed.
intensity_columns_regexp	regular expressions that defines intensity columns. Defaults to "Abundance", which means that columns that contain the word "Abundance" will be treated as corresponding to intensities for different channels.
intensity	Intensity column to use. Accepts legacy enum values 'PeakArea' (default, uses F.PeakArea), 'NormalizedPeakArea' (uses F.NormalizedPeakArea). Can also be any raw Spectronaut column name passed as a string (e.g. "FG.MS1Quantity"); the column name is standardized internally. For protein turnover workflows the recommended default is "FG.MS1Quantity".
calculateAnomalyScores	Default is FALSE. If TRUE, will run anomaly detection model and calculate anomaly scores for each feature. Used downstream to weigh measurements in differential analysis.
anomalyModelFeatures	character vector of quality metric column names to be used as features in the anomaly detection model. List must not be empty if calculateAnomalyScores=TRUE.
peptideSequenceColumn	Name of the Spectronaut column that contains the peptide sequence. Defaults to "EG.ModifiedSequence". The value is standardized internally (dots and spaces removed) before column lookup.
heavyLabels	Character list identifying the heavy isotope labels as it appears inside square brackets in the peptide sequence column, e.g. c("Lys6") matches peptides containing [Lys6]. c("Lys6", "Arg10") matches peptides containing either [Lys6] or [Arg10]. Supports any novel label name reported by Spectronaut (e.g. "Leu6", "Phe10"). When provided, peptides are classified as heavy (IsotopeLabelType = "H"), light (IsotopeLabelType = "L"), or unlabeled (IsotopeLabelType = NA) based on its labeled sequence. When NULL (default) all peptides receive IsotopeLabelType = "L". Useful for protein turnover experiments.
peptide_id_col	character name of a column that identifies peptides
channels	character vector of channel labels
remove_shared_peptides	logical, if TRUE, shared peptides will be removed based on the IsUnique column from Philosopher output
MBR	True if analysis was done with match between runs
quantificationColumn	Use 'FragmentQuantCorrected'(default) column for quantified intensities for DIANN 1.8.x. Use 'FragmentQuantRaw' for quantified intensities for DIANN 1.9.x. Use 'auto' for quantified intensities for DIANN 2.x where each fragment intensity is a separate column, e.g. Fr0Quantity.
global_qvalue_cutoff	The qvalue cutoff for the Q.Value column, i.e. the run-specific precursor q-value. Default is 0.01.
qvalue_cutoff	If MBR is false, the qvalue cutoff for the Global.Q.Value column, i.e. global precursor q-value. If MBR is true, the qvalue cutoff for the Lib.Q.Value column, i.e. the q-value for the library created after the first MBR pass. Default is 0.01.
pg_qvalue_cutoff	If MBR is false, the qvalue cutoff for the Global.PG.Q.Value column, i.e. the global q-value for the protein group. If MBR is true, the qvalue cutoff for the Lib.PG.Q.Value column, i.e. the protein group q-value for the library created after the first MBR pass. Default is 0.01.
labeledAminoAcids	Character vector of single-letter amino acid codes that carry the SILAC label in protein turnover experiments, e.g. c("K") or c("K", "R"). Supplying this vector opts in to protein-turnover mode; the exact amino acids determine behaviour only in the ModifiedSequence-parsing path described below. Channel-based path (DIA-NN 2.x exports that include a Channel column): when labeledAminoAcids is non-NULL and the input contains a Channel column, Channel values are mapped directly to IsotopeLabelType ("H" → "H", "L" → "L", anything else → NA). The amino acid codes in labeledAminoAcids are not used to validate or filter ModifiedSequence in this path. ModifiedSequence-parsing path (DIA-NN 1.x exports without a Channel column): when labeledAminoAcids is non-NULL and no Channel column is present, each ModifiedSequence is inspected for SILAC suffixes of the form (SILAC-<AA>-H) or (SILAC-<AA>-L), where <AA> is one of the supplied amino acid codes. Matching sequences are classified as "H" or "L"; sequences carrying neither suffix receive IsotopeLabelType = NA. The SILAC suffix is then stripped from PeptideSequence. When NULL (default), protein-turnover mode is disabled and all peptides receive IsotopeLabelType = "Light".
mzmine_annotations	data.frame of MZMine spectral-library annotations with columns id, compound_name, score. Required; passing NULL raises an error. The highest-scoring compound_name per feature is used as ProteinName, and features in the quant table with no matching annotation row are dropped from the output. These are MSI Level 2 annotations (putative identification via MS/MS spectral matching). See the public MZMinetoMSstatsFormat docstring for the full scope discussion.

Value

data.table

data.table

data.table

data.table

data.table

data.table

data.table

data.table

data.table

data.table

data.table

data.table

data.table

data.table

Examples

evidence_path = system.file("tinytest/raw_data/MaxQuant/mq_ev.csv", 
                            package = "MSstatsConvert")
pg_path = system.file("tinytest/raw_data/MaxQuant/mq_pg.csv", 
                      package = "MSstatsConvert")
evidence = read.csv(evidence_path)
pg = read.csv(pg_path)
imported = MSstatsImport(list(evidence = evidence, protein_groups = pg),
                         "MSstats", "MaxQuant")
cleaned_data = MSstatsClean(imported, protein_id_col = "Proteins")
head(cleaned_data)

---

Import files from signal processing tools.

Description

Import files from signal processing tools.

Usage

MSstatsImport(input_files, type, tool, tool_version = NULL, ...)

Arguments

input_files	list of paths to input files or data.frame objects. Interpretation of this parameter depends on values of parameters type and tool.
type	chr, "MSstats" or "MSstatsTMT".
tool	chr, name of a signal processing tool that generated input files.
tool_version	not implemented yet. In the future, this parameter will allow handling different versions of each signal processing tools.
...	optional additional parameters to data.table::fread.

Value

an object of class MSstatsInputFiles.

Examples

evidence_path = system.file("tinytest/raw_data/MaxQuant/mq_ev.csv", 
                            package = "MSstatsConvert")
pg_path = system.file("tinytest/raw_data/MaxQuant/mq_pg.csv", 
                      package = "MSstatsConvert")
evidence = read.csv(evidence_path)
pg = read.csv(pg_path)
imported = MSstatsImport(list(evidence = evidence, protein_groups = pg),
                         "MSstats", "MaxQuant")
class(imported)
head(getInputFile(imported, "evidence"))

---

Set how MSstats will log information from data processing

Description

Set how MSstats will log information from data processing

Usage

MSstatsLogsSettings(
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  base = "MSstats_log_",
  pkg_name = "MSstats"
)

Arguments

use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing wil be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
base	start of the file name.
pkg_name	currently "MSstats", "MSstatsPTM" or "MSstatsTMT". Each package can use its own separate log settings.

Value

TRUE invisibly in case of successful logging setup.

Examples

# No logging and no messages
MSstatsLogsSettings(FALSE, FALSE, FALSE)
# Log, but do not display messages
MSstatsLogsSettings(TRUE, FALSE, FALSE)
# Log to an existing file
file.create("new_log.log")
MSstatsLogsSettings(TRUE, TRUE, log_file_path = "new_log.log")
# Do not log, but display messages
MSstatsLogsSettings(FALSE)

---

Create annotation

Description

Create annotation

Usage

MSstatsMakeAnnotation(input, annotation, ...)

Arguments

input	data.table preprocessed by the MSstatsClean function
annotation	data.table
...	key-value pairs, where keys are names of columns of annotation

Value

data.table

Examples

evidence_path = system.file("tinytest/raw_data/MaxQuant/mq_ev.csv", 
                            package = "MSstatsConvert")
pg_path = system.file("tinytest/raw_data/MaxQuant/mq_pg.csv", 
                      package = "MSstatsConvert")
evidence = read.csv(evidence_path)
pg = read.csv(pg_path)
imported = MSstatsImport(list(evidence = evidence, protein_groups = pg),
                         "MSstats", "MaxQuant")
cleaned_data = MSstatsClean(imported, protein_id_col = "Proteins")
annot_path = system.file("tinytest/raw_data/MaxQuant/annotation.csv", 
                         package = "MSstatsConvert")
mq_annot = MSstatsMakeAnnotation(cleaned_data, read.csv(annot_path),
                                 Run = "Rawfile")
head(mq_annot)

---

Preprocess outputs from MS signal processing tools for analysis with MSstats

Description

Preprocess outputs from MS signal processing tools for analysis with MSstats

Usage

MSstatsPreprocess(
  input,
  annotation,
  feature_columns,
  remove_shared_peptides = TRUE,
  remove_single_feature_proteins = TRUE,
  feature_cleaning = list(remove_features_with_few_measurements = TRUE,
    summarize_multiple_psms = max),
  score_filtering = list(),
  exact_filtering = list(),
  pattern_filtering = list(),
  columns_to_fill = list(),
  aggregate_isotopic = FALSE,
  anomaly_metrics = c(),
  ...
)

Arguments

input	data.table processed by the MSstatsClean function.
annotation	annotation file generated by a signal processing tool.
feature_columns	character vector of names of columns that define spectral features.
remove_shared_peptides	logical, if TRUE shared peptides will be removed.
remove_single_feature_proteins	logical, if TRUE, proteins that only have one feature will be removed.
feature_cleaning	named list with maximum two (for MSstats converters) or three (for MSstatsTMT converter) elements. If handle_few_measurements is set to "remove", feature with less than three measurements will be removed (otherwise it should be equal to "keep"). summarize_multiple_psms is a function that will be used to aggregate multiple feature measurements in a run. It should return a scalar and accept an na.rm parameter. For MSstatsTMT converters, setting remove_psms_with_any_missing will remove features which have missing values in a run from that run.
score_filtering	a list of named lists that specify filtering options. Details are provided in the vignette.
exact_filtering	a list of named lists that specify filtering options. Details are provided in the vignette.
pattern_filtering	a list of named lists that specify filtering options. Details are provided in the vignette.
columns_to_fill	a named list of scalars. If provided, columns with names defined by the names of this list and values corresponding to its elements will be added to the output data.frame.
aggregate_isotopic	logical. If TRUE, isotopic peaks will by summed.
anomaly_metrics	character vector of names of columns with quality metrics. Default is missing and is not required if anomaly model not run.
...	additional parameters to data.table::fread.

Value

data.table

Examples

evidence_path = system.file("tinytest/raw_data/MaxQuant/mq_ev.csv", 
                            package = "MSstatsConvert")
pg_path = system.file("tinytest/raw_data/MaxQuant/mq_pg.csv", 
                      package = "MSstatsConvert")
evidence = read.csv(evidence_path)
pg = read.csv(pg_path)
imported = MSstatsImport(list(evidence = evidence, protein_groups = pg),
                         "MSstats", "MaxQuant")
cleaned_data = MSstatsClean(imported, protein_id_col = "Proteins")
annot_path = system.file("tinytest/raw_data/MaxQuant/annotation.csv", 
                         package = "MSstatsConvert")
mq_annot = MSstatsMakeAnnotation(cleaned_data, read.csv(annot_path),
                                 Run = "Rawfile")
                               
# To filter M-peptides and oxidatin peptides 
m_filter = list(col_name = "PeptideSequence", pattern = "M", 
                filter = TRUE, drop_column = FALSE)
oxidation_filter = list(col_name = "Modifications", pattern = "Oxidation", 
                        filter = TRUE, drop_column = TRUE)
msstats_format = MSstatsPreprocess(
cleaned_data, mq_annot, 
feature_columns = c("PeptideSequence", "PrecursorCharge"),
columns_to_fill = list(FragmentIon = NA, ProductCharge = NA),
pattern_filtering = list(oxidation = oxidation_filter, m = m_filter)
)
# Output in the standard MSstats format
head(msstats_format)

---

Save session information

Description

Save session information

Usage

MSstatsSaveSessionInfo(
  path = NULL,
  append = TRUE,
  base = "MSstats_session_info_"
)

Arguments

path	optional path to output file. If not provided, "MSstats_session_info" and current timestamp will be used as a file name
append	if TRUE and file given by the path parameter already exists, session info will be appended to the file
base	beginning of a file name

Value

TRUE invisibly after session info was saved

Examples

MSstatsSaveSessionInfo("session_info.txt")
MSstatsSaveSessionInfo("session_info.txt", base = "MSstatsTMT_session_info_")

---

Import MZMine files

Description

Import MZMine files

Usage

MZMinetoMSstatsFormat(
  input,
  annotation = NULL,
  mzmine_annotations,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	MZMine feature-quantification table (wide format; one row per feature). Must include the metadata columns ⁠row ID⁠, ⁠row m/z⁠, ⁠row retention time⁠, and per-sample peak-area columns named "<run> Peak area" (e.g. "sampleA.mzML Peak area").
annotation	data.frame with columns Run, Condition, BioReplicate. Run values must match MSstatsConvert-standardized sample names (after column-name normalization removes spaces and dots) with the trailing "Peakarea" suffix removed. For example, a quant-file column "sampleA.mzML Peak area" becomes "sampleAmzML" after standardization, so the corresponding Run value must be sampleAmzML.
mzmine_annotations	data.frame of MZMine spectral-library annotations with columns id, compound_name, score. Required: the highest-scoring compound_name per feature is used as ProteinName, and features in the quant table with no matching annotation row are dropped from the output. These are MSI Level 2 annotations (putative identification via MS/MS spectral matching against a reference library). Higher- confidence Level 1 identifications require pure reference standards and are out of scope here. Lower-confidence annotations such as Level 3 (SIRIUS, MS2Query) or Level 4 (molecular formula via CANOPUS) are not currently supported – features without a Level 2 annotation row are filtered out.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when there are multiple measurements for certain feature and certain run, use highest or sum of multiple intensities. Default is max for label-free converters and sum for TMT converters.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.table in the MSstats required format.

Examples

input_path = system.file("tinytest/raw_data/MZMine/mzmine_input.csv",
                         package = "MSstatsConvert")
annot_path = system.file("tinytest/raw_data/MZMine/annotation.csv",
                         package = "MSstatsConvert")
lib_path   = system.file("tinytest/raw_data/MZMine/mzmine_annotations.csv",
                         package = "MSstatsConvert")
input = data.table::fread(input_path)
annot = data.table::fread(annot_path)
lib   = data.table::fread(lib_path)
output = MZMinetoMSstatsFormat(input, annotation = annot,
                               mzmine_annotations = lib,
                               use_log_file = FALSE)
head(output)

---

Import OpenMS files

Description

Import OpenMS files

Usage

OpenMStoMSstatsFormat(
  input,
  annotation = NULL,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of MSstats input report from OpenMS, which includes feature(peptide ion)-level data.
annotation	name of 'annotation.txt' data which includes Condition, BioReplicate, Run. Run should be the same as filename.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Meena Choi, Olga Vitek.

Examples

openms_raw = data.table::fread(system.file("tinytest/raw_data/OpenMS/openms_input.csv", 
                                           package = "MSstatsConvert"))
openms_imported = OpenMStoMSstatsFormat(openms_raw, use_log_file = FALSE)
head(openms_imported)

---

Generate MSstatsTMT required input format for OpenMS output

Description

Generate MSstatsTMT required input format for OpenMS output

Usage

OpenMStoMSstatsTMTFormat(
  input,
  useUniquePeptide = TRUE,
  rmPSM_withfewMea_withinRun = TRUE,
  rmProtein_with1Feature = FALSE,
  summaryforMultiplePSMs = sum,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	MSstatsTMT report from OpenMS
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
rmPSM_withfewMea_withinRun	TRUE (default) will remove the features that have 1 or 2 measurements within each Run.
rmProtein_with1Feature	TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.
summaryforMultiplePSMs	sum(default) or max - when there are multiple measurements for certain feature in certain run, select the feature with the largest summation or maximal value.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame of class MSstatsTMT.

Examples

raw.om = data.table::fread(system.file("tinytest/raw_data/OpenMSTMT/openmstmt_input.csv",
                                      package = "MSstatsConvert"))
input.om <- OpenMStoMSstatsTMTFormat(raw.om)
head(input.om)

---

Import OpenSWATH files

Description

Import OpenSWATH files

Usage

OpenSWATHtoMSstatsFormat(
  input,
  annotation,
  filter_with_mscore = TRUE,
  mscore_cutoff = 0.01,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of MSstats input report from OpenSWATH, which includes feature-level data.
annotation	name of 'annotation.txt' data which includes Condition, BioReplicate, Run. Run should be the same as filename.
filter_with_mscore	TRUE(default) will filter out the features that have greater than mscore_cutoff in m_score column. Those features will be removed.
mscore_cutoff	Cutoff for m_score. Default is 0.01.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Meena Choi, Olga Vitek.

Examples

os_raw = system.file("tinytest/raw_data/OpenSWATH/openswath_input.csv", 
                             package = "MSstatsConvert")
annot = system.file("tinytest/raw_data/OpenSWATH/annot_os.csv", 
                    package = "MSstatsConvert")
os_raw = data.table::fread(os_raw) 
annot = data.table::fread(annot)

os_imported = OpenSWATHtoMSstatsFormat(os_raw, annot, use_log_file = FALSE)
head(os_imported)

---

Import Proteome Discoverer files

Description

Import Proteome Discoverer files

Usage

PDtoMSstatsFormat(
  input,
  annotation,
  useNumProteinsColumn = FALSE,
  useUniquePeptide = TRUE,
  summaryforMultipleRows = max,
  removeFewMeasurements = TRUE,
  removeOxidationMpeptides = FALSE,
  removeProtein_with1Peptide = FALSE,
  which.quantification = "Precursor.Area",
  which.proteinid = "Protein.Group.Accessions",
  which.sequence = "Sequence",
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	PD report or a path to it.
annotation	name of 'annotation.txt' or 'annotation.csv' data which includes Condition, BioReplicate, Run information. 'Run' will be matched with 'Spectrum.File'.
useNumProteinsColumn	TRUE removes peptides which have more than 1 in # Proteins column of PD output.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeOxidationMpeptides	TRUE will remove the peptides including 'oxidation (M)' in modification. FALSE is default.
removeProtein_with1Peptide	TRUE will remove the proteins which have only 1 peptide and charge. FALSE is default.
which.quantification	Use 'Precursor.Area'(default) column for quantified intensities. 'Intensity' or 'Area' can be used instead.
which.proteinid	Use 'Protein.Accessions'(default) column for protein name. 'Master.Protein.Accessions' can be used instead.
which.sequence	Use 'Sequence'(default) column for peptide sequence. 'Annotated.Sequence' can be used instead.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Meena Choi, Olga Vitek

Examples

pd_raw = system.file("tinytest/raw_data/PD/pd_input.csv", 
                     package = "MSstatsConvert")
annot = system.file("tinytest/raw_data/PD/annot_pd.csv", 
                    package = "MSstatsConvert")
pd_raw = data.table::fread(pd_raw)
annot = data.table::fread(annot)

pd_imported = PDtoMSstatsFormat(pd_raw, annot, use_log_file = FALSE)
head(pd_imported)

---

Convert Proteome Discoverer output to MSstatsTMT format.

Description

Convert Proteome Discoverer output to MSstatsTMT format.

Usage

PDtoMSstatsTMTFormat(
  input,
  annotation,
  which.proteinid = "Protein.Accessions",
  useNumProteinsColumn = TRUE,
  useUniquePeptide = TRUE,
  rmPSM_withfewMea_withinRun = TRUE,
  rmProtein_with1Feature = FALSE,
  summaryforMultipleRows = sum,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	PD report or a path to it.
annotation	annotation with Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition columns or a path to file. Refer to the example 'annotation' for the meaning of each column.
which.proteinid	Use 'Protein.Accessions'(default) column for protein name. 'Master.Protein.Accessions' can be used instead to get the protein name with single protein.
useNumProteinsColumn	logical, TRUE (default) removes shared peptides by information of # Proteins column in PSM sheet.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
rmPSM_withfewMea_withinRun	TRUE (default) will remove the features that have 1 or 2 measurements within each Run.
rmProtein_with1Feature	TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame of class MSstatsTMT

Examples

raw.pd = data.table::fread(system.file("tinytest/raw_data/PDTMT/pdtmt_input.csv",
                                      package = "MSstatsConvert"))
annotation.pd = data.table::fread(system.file("tinytest/raw_data/PDTMT/pd_annotation.csv",
                                      package = "MSstatsConvert"))
head(raw.pd)
head(annotation.pd)
input.pd <- PDtoMSstatsTMTFormat(raw.pd, annotation.pd)
head(input.pd)

---

Convert Philosopher (Fragpipe) output to MSstatsTMT format.

Description

Convert Philosopher (Fragpipe) output to MSstatsTMT format.

Usage

PhilosophertoMSstatsTMTFormat(
  input,
  annotation,
  protein_id_col = "Protein",
  peptide_id_col = "Peptide.Sequence",
  Purity_cutoff = 0.6,
  PeptideProphet_prob_cutoff = 0.7,
  useUniquePeptide = TRUE,
  rmPSM_withfewMea_withinRun = TRUE,
  rmPeptide_OxidationM = TRUE,
  rmProtein_with1Feature = FALSE,
  summaryforMultipleRows = sum,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	data.frame of msstats.csv file produced by Philosopher
annotation	annotation with Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition columns or a path to file. Refer to the example 'annotation' for the meaning of each column. Channel column should be consistent with the channel columns (Ignore the prefix "Channel ") in msstats.csv file. Run column should be consistent with the Spectrum.File columns in msstats.csv file.
protein_id_col	Use 'Protein'(default) column for protein name. 'Master.Protein.Accessions' can be used instead to get the protein ID with single protein.
peptide_id_col	Use 'Peptide.Sequence'(default) column for peptide sequence. 'Modified.Peptide.Sequence' can be used instead to get the modified peptide sequence.
Purity_cutoff	Cutoff for purity. Default is 0.6
PeptideProphet_prob_cutoff	Cutoff for the peptide identification probability. Default is 0.7. The probability is confidence score determined by PeptideProphet and higher values indicate greater confidence.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
rmPSM_withfewMea_withinRun	TRUE (default) will remove the features that have 1 or 2 measurements within each Run.
rmPeptide_OxidationM	TRUE (default) will remove the peptides including oxidation (M) sequence.
rmProtein_with1Feature	TRUE will remove the proteins which have only 1 peptide and charge. Default is FALSE.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame of class MSstatsTMT

Examples

input_file_path = system.file("tinytest/raw_data/Philosopher/msstats.csv", 
                     package = "MSstatsConvert")
annotation_file_path = system.file("tinytest/raw_data/Philosopher/MSstatsTMT_annotation.csv", 
                    package = "MSstatsConvert")
input = data.table::fread(input_file_path)
annotation = data.table::fread(annotation_file_path)
msstats_format = PhilosophertoMSstatsTMTFormat(input, annotation)
head(msstats_format)

---

Import Progenesis files

Description

Import Progenesis files

Usage

ProgenesistoMSstatsFormat(
  input,
  annotation,
  useUniquePeptide = TRUE,
  summaryforMultipleRows = max,
  removeFewMeasurements = TRUE,
  removeOxidationMpeptides = FALSE,
  removeProtein_with1Peptide = FALSE,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of Progenesis output, which is wide-format. 'Accession', 'Sequence', 'Modification', 'Charge' and one column for each run are required.
annotation	name of 'annotation.txt' or 'annotation.csv' data which includes Condition, BioReplicate, Run information. It will be matched with the column name of input for MS runs.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeOxidationMpeptides	TRUE will remove the peptides including 'oxidation (M)' in modification. FALSE is default.
removeProtein_with1Peptide	TRUE will remove the proteins which have only 1 peptide and charge. FALSE is default.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Meena Choi, Olga Vitek, Ulrich Omasits

Examples

progenesis_raw = system.file("tinytest/raw_data/Progenesis/progenesis_input.csv", 
                             package = "MSstatsConvert")
annot = system.file("tinytest/raw_data/Progenesis/progenesis_annot.csv", 
                    package = "MSstatsConvert")
progenesis_raw = data.table::fread(progenesis_raw) 
annot = data.table::fread(annot)

progenesis_imported = ProgenesistoMSstatsFormat(progenesis_raw, annot,
                                                use_log_file = FALSE)
head(progenesis_imported)

---

Generate MSstatsTMT required input format from Protein Prospector output

Description

Generate MSstatsTMT required input format from Protein Prospector output

Usage

ProteinProspectortoMSstatsTMTFormat(
  input,
  annotation,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = sum,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

input	Input txt peptide report file from Protein Prospector with "Keep Replicates", "Mods in Peptide", and "Protein Mods" options selected.
annotation	data frame which contains column Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.

Value

data.frame of class "MSstatsTMT"

Examples

input = system.file("tinytest/raw_data/ProteinProspector/Prospector_TotalTMT.txt",
    package = "MSstatsConvert")
input = data.table::fread(input)
annot = system.file("tinytest/raw_data/ProteinProspector/Annotation.csv",
                                package = "MSstatsConvert")
annot = data.table::fread(annot)
output <- ProteinProspectortoMSstatsTMTFormat(input, annot)
head(output)

---

Import Skyline files

Description

Import Skyline files

Usage

SkylinetoMSstatsFormat(
  input,
  annotation = NULL,
  removeiRT = TRUE,
  filter_with_Qvalue = TRUE,
  qvalue_cutoff = 0.01,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeOxidationMpeptides = FALSE,
  removeProtein_with1Feature = FALSE,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of MSstats input report from Skyline, which includes feature-level data.
annotation	name of 'annotation.txt' data which includes Condition, BioReplicate, Run. If annotation is already complete in Skyline, use annotation=NULL (default). It will use the annotation information from input.
removeiRT	TRUE (default) will remove the proteins or peptides which are labeled 'iRT' in 'StandardType' column. FALSE will keep them.
filter_with_Qvalue	TRUE(default) will filter out the intensities that have greater than qvalue_cutoff in DetectionQValue column. Those intensities will be replaced with zero and will be considered as censored missing values for imputation purpose.
qvalue_cutoff	Cutoff for DetectionQValue. default is 0.01.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeOxidationMpeptides	TRUE will remove the peptides including 'oxidation (M)' in modification. FALSE is default.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Meena Choi, Olga Vitek

Examples

skyline_raw = system.file("tinytest/raw_data/Skyline/skyline_input.csv",
                          package = "MSstatsConvert")
skyline_raw = data.table::fread(skyline_raw)
skyline_imported = SkylinetoMSstatsFormat(skyline_raw)
head(skyline_imported)

---

Import data from SpectroMine

Description

Import data from SpectroMine

Usage

SpectroMinetoMSstatsTMTFormat(
  input,
  annotation,
  filter_with_Qvalue = TRUE,
  qvalue_cutoff = 0.01,
  useUniquePeptide = TRUE,
  rmPSM_withfewMea_withinRun = TRUE,
  rmProtein_with1Feature = FALSE,
  summaryforMultipleRows = sum,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	data name of SpectroMine PSM output. Read PSM sheet.
annotation	data frame which contains column Run, Fraction, TechRepMixture, Mixture, Channel, BioReplicate, Condition. Refer to the example 'annotation.mine' for the meaning of each column.
filter_with_Qvalue	TRUE(default) will filter out the intensities that have greater than qvalue_cutoff in EG.Qvalue column. Those intensities will be replaced with NA and will be considered as censored missing values for imputation purpose.
qvalue_cutoff	Cutoff for EG.Qvalue. default is 0.01.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
rmPSM_withfewMea_withinRun	TRUE (default) will remove the features that have 1 or 2 measurements within each Run.
rmProtein_with1Feature	TRUE will remove the proteins which have only 1 peptide and charge. Defaut is FALSE.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame of class MSstatsTMT

Examples

raw.mine = data.table::fread(system.file("tinytest/raw_data/SpectroMine/spectromine_input.csv",
                                      package = "MSstatsConvert"))
annotation.mine = data.table::fread(system.file("tinytest/raw_data/SpectroMine/spectromine_annotation.csv",
                                      package = "MSstatsConvert"))
head(raw.mine)
head(annotation.mine)
input.mine <- SpectroMinetoMSstatsTMTFormat(raw.mine, annotation.mine)
head(input.mine)

---

Import Spectronaut files

Description

Import Spectronaut files

Usage

SpectronauttoMSstatsFormat(
  input,
  annotation = NULL,
  intensity = "PeakArea",
  peptideSequenceColumn = "EG.ModifiedSequence",
  heavyLabels = NULL,
  excludedFromQuantificationFilter = TRUE,
  filter_with_Qvalue = FALSE,
  qvalue_cutoff = 0.01,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  calculateAnomalyScores = FALSE,
  anomalyModelFeatures = c(),
  anomalyModelFeatureTemporal = c(),
  removeMissingFeatures = 0.5,
  anomalyModelFeatureCount = 100,
  runOrder = NULL,
  n_trees = 100,
  max_depth = "auto",
  numberOfCores = 1,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  ...
)

Arguments

input	name of Spectronaut output, which is long-format. ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity, F.ExcludedFromQuantification are required. Rows with F.ExcludedFromQuantification=True will be removed.
annotation	name of 'annotation.txt' data which includes Condition, BioReplicate, Run. If annotation is already complete in Spectronaut, use annotation=NULL (default). It will use the annotation information from input.
intensity	Intensity column to use. Accepts legacy enum values 'PeakArea' (default, uses F.PeakArea), 'NormalizedPeakArea' (uses F.NormalizedPeakArea). Can also be any raw Spectronaut column name passed as a string (e.g. "FG.MS1Quantity"); the column name is standardized internally. For protein turnover workflows the recommended default is "FG.MS1Quantity".
peptideSequenceColumn	Name of the Spectronaut column that contains the peptide sequence. Defaults to "EG.ModifiedSequence". The value is standardized internally (dots and spaces removed) before column lookup.
heavyLabels	Character list identifying the heavy isotope labels as it appears inside square brackets in the peptide sequence column, e.g. c("Lys6") matches peptides containing [Lys6]. c("Lys6", "Arg10") matches peptides containing either [Lys6] or [Arg10]. Supports any novel label name reported by Spectronaut (e.g. "Leu6", "Phe10"). When provided, peptides are classified as heavy (IsotopeLabelType = "H"), light (IsotopeLabelType = "L"), or unlabeled (IsotopeLabelType = NA) based on its labeled sequence. When NULL (default) all peptides receive IsotopeLabelType = "L". Useful for protein turnover experiments.
excludedFromQuantificationFilter	Remove rows with F.ExcludedFromQuantification=TRUE Default is TRUE.
filter_with_Qvalue	FALSE(default) will not perform any filtering. TRUE will filter out the intensities that have greater than qvalue_cutoff in EG.Qvalue column. Those intensities will be replaced with zero and will be considered as censored missing values for imputation purpose.
qvalue_cutoff	Cutoff for EG.Qvalue. default is 0.01.
useUniquePeptide	TRUE (default) removes peptides that are assigned for more than one proteins. We assume to use unique peptide for each protein.
removeFewMeasurements	TRUE (default) will remove the features that have 1 or 2 measurements across runs.
removeProtein_with1Feature	TRUE will remove the proteins which have only 1 feature, which is the combination of peptide, precursor charge, fragment and charge. FALSE is default.
summaryforMultipleRows	max or sum - when multiple PSMs identify the same feature within a single MS run (duplicate PSMs), use the highest (max) or sum of the duplicate intensities. Default is max for label-free converters and sum for TMT converters. Note that this parameter does NOT control collapsing across fractions of the same biological mixture.
calculateAnomalyScores	Default is FALSE. If TRUE, will run anomaly detection model and calculate anomaly scores for each feature. Used downstream to weigh measurements in differential analysis.
anomalyModelFeatures	character vector of quality metric column names to be used as features in the anomaly detection model. List must not be empty if calculateAnomalyScores=TRUE.
anomalyModelFeatureTemporal	character vector of temporal direction corresponding to columns passed to anomalyModelFeatures. Values must be one of: mean_decrease, mean_increase, dispersion_increase, or NULL (to perform no temporal feature engineering). Default is empty vector. If calculateAnomalyScores=TRUE, vector must have as many values as anomalyModelFeatures (even if all NULL).
removeMissingFeatures	Remove features with missing values in more than this fraction of runs. Default is 0.5. Only used if calculateAnomalyScores=TRUE.
anomalyModelFeatureCount	Feature selection for anomaly model. Anomaly detection works on the precursor-level and can be much slower if all features used. We will by default filter to the top-100 highest intensity features. This can be adjusted as necessary. To turn feature-selection off, set this value to a high number (e.g. 10000). Only used if calculateAnomalyScores=TRUE.
runOrder	Temporal order of MS runs. Should be a two column data.table with columns Run and Order, where Run matches the run name output by Spectronaut and Order is an integer. Used to engineer the temporal features defined in anomalyModelFeatureTemporal.
n_trees	Number of trees to use in isolation forest when calculateAnomalyScores=TRUE. Default is 100.
max_depth	Max tree depth to use in isolation forest when calculateAnomalyScores=TRUE. Default is "auto" which calculates depth as log2(N) where N is the number of runs. Otherwise must be an integer.
numberOfCores	Number of cores for parallel processing anomaly detection model. When > 1, a logfile named 'MSstats_anomaly_model_progress.log' is created to track progress. Only works for Linux & Mac OS. Default is 1.
use_log_file	logical. If TRUE, information about data processing will be saved to a file.
append	logical. If TRUE, information about data processing will be added to an existing log file.
verbose	logical. If TRUE, information about data processing will be printed to the console.
log_file_path	character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If append = TRUE, has to be a valid path to a file.
...	additional parameters to data.table::fread.

Value

data.frame in the MSstats required format.

Author(s)

Meena Choi, Olga Vitek

Examples

spectronaut_raw = system.file("tinytest/raw_data/Spectronaut/spectronaut_input.csv",
                              package = "MSstatsConvert")
spectronaut_raw = data.table::fread(spectronaut_raw)
spectronaut_imported = SpectronauttoMSstatsFormat(spectronaut_raw, use_log_file = FALSE)
head(spectronaut_imported)

---