Package 'MSstatsPTM'

Description

annotSite annotates modified sites as their residues and locations.

Usage

annotSite(aaIndex, residue, lenIndex = NULL)

Arguments

Value

A string.

Examples

annotSite(10, "K")
annotSite(10, "K", 3L)

Description

To illustrate the quantitative data and quality control of MS runs, dataProcessPlotsPTM takes the quantitative data from dataSummarizationPTM or dataSummarizationPTM_TMT to plot the following : (1) profile plot (specify "ProfilePlot" in option type), to identify the potential sources of variation for each protein; (2) quality control plot (specify "QCPlot" in option type), to evaluate the systematic bias between MS runs.

Usage

dataProcessPlotsPTM(
  data,
  type = "PROFILEPLOT",
  ylimUp = FALSE,
  ylimDown = FALSE,
  x.axis.size = 10,
  y.axis.size = 10,
  text.size = 4,
  text.angle = 90,
  legend.size = 7,
  dot.size.profile = 2,
  ncol.guide = 5,
  width = 10,
  height = 12,
  ptm.title = "All PTMs",
  protein.title = "All Proteins",
  which.PTM = "all",
  which.Protein = NULL,
  originalPlot = TRUE,
  summaryPlot = TRUE,
  address = ""
)

Arguments

Value

plot or pdf

Examples

# QCPlot
dataProcessPlotsPTM(summary.data,
                    type = 'QCPLOT',
                    which.PTM = "allonly",
                    address = FALSE)
                    
#ProfilePlot
dataProcessPlotsPTM(summary.data,
                    type = 'PROFILEPLOT',
                    which.PTM = "Q9UQ80_K376",
                    address = FALSE)

Description

Utilizes functionality from MSstats to clean, summarize, and normalize PTM and protein level data. Imputes missing values, performs normalization, and summarizes data. PTM data is summarized up to the modification and protein data is summarized up to the protein level. Takes as input the output of the included converters (see included raw.input data object for required input format).

Usage

dataSummarizationPTM(
  data,
  logTrans = 2,
  normalization = "equalizeMedians",
  normalization.PTM = "equalizeMedians",
  nameStandards = NULL,
  nameStandards.PTM = NULL,
  featureSubset = "all",
  featureSubset.PTM = "all",
  remove_uninformative_feature_outlier = FALSE,
  remove_uninformative_feature_outlier.PTM = FALSE,
  min_feature_count = 2,
  min_feature_count.PTM = 1,
  n_top_feature = 3,
  n_top_feature.PTM = 3,
  summaryMethod = "TMP",
  equalFeatureVar = TRUE,
  censoredInt = "NA",
  MBimpute = TRUE,
  MBimpute.PTM = TRUE,
  remove50missing = FALSE,
  fix_missing = NULL,
  maxQuantileforCensored = 0.999,
  use_log_file = TRUE,
  append = TRUE,
  verbose = TRUE,
  log_file_path = NULL,
  base = "MSstatsPTM_log_"
)

Arguments

Value

list of summarized PTM and Protein results. These results contain the reformatted input to the summarization function, as well as run-level summarization results.

Examples

head(raw.input$PTM)
head(raw.input$PROTEIN)

quant.lf.msstatsptm = dataSummarizationPTM(raw.input, verbose = FALSE)
head(quant.lf.msstatsptm$PTM$ProteinLevelData)

Description

Utilizes functionality from MSstatsTMT to clean, summarize, and normalize PTM and protein level data. Imputes missing values, performs normalization, and summarizes data. PTM data is summarized up to the modification and protein data is summarized up to the protein level. Takes as input the output of the included converters (see included raw.input.tmt data object for required input format).

Usage

dataSummarizationPTM_TMT(
  data,
  method = "msstats",
  global_norm = TRUE,
  global_norm.PTM = TRUE,
  reference_norm = TRUE,
  reference_norm.PTM = TRUE,
  remove_norm_channel = TRUE,
  remove_empty_channel = TRUE,
  MBimpute = TRUE,
  MBimpute.PTM = TRUE,
  maxQuantileforCensored = NULL,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

list of two data.tables

Examples

head(raw.input.tmt$PTM)
head(raw.input.tmt$PROTEIN)

quant.tmt.msstatsptm = dataSummarizationPTM_TMT(raw.input.tmt,
                                                 method = "msstats", 
                                                 verbose = FALSE)
head(quant.tmt.msstatsptm$PTM$ProteinLevelData)

Description

Calculate sample size for future experiments of a PTM experiment based on intensity-based linear model. Calculation is only available for group comparison experimental designs (not including time series). Two options of the calculation: (1) number of biological replicates per condition, (2) power.

Usage

designSampleSizePTM(
  data,
  desiredFC,
  FDR = 0.05,
  numSample = TRUE,
  power = 0.8,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  base = "MSstatsPTM_log_"
)

Arguments

Details

The function fits the model and uses variance components to calculate sample size. The underlying model fitting with intensity-based linear model with technical MS run replication. Estimated sample size is rounded to 0 decimal. The function can only obtain either one of the categories of the sample size calculation (numSample, numPep, numTran, power) at the same time.

Value

data.frame - sample size calculation results including varibles: desiredFC, numSample, FDR, and power.

Examples

model.lf.msstatsptm = groupComparisonPTM(summary.data, 
                                     data.type = "LabelFree",
                                     verbose = FALSE)
                                     
#(1) Minimal number of biological replicates per condition
designSampleSizePTM(data=model.lf.msstatsptm, numSample=TRUE,
                 desiredFC=c(2.0,2.75), FDR=0.05, power=0.8)
#(2) Power calculation
designSampleSizePTM(data=model.lf.msstatsptm, numSample=5,
                 desiredFC=c(2.0,2.75), FDR=0.05, power=TRUE)

Description

Takes as input the report.tsv file from DIA-NN and converts it into MSstatsPTM format. Requires PSM and an annotation file. Optionally an additional report.tsv file for a corresponding global profiling run can be included.

Usage

DIANNtoMSstatsPTMFormat(
  input,
  annotation,
  input_protein = NULL,
  annotation_protein = NULL,
  fasta_path = NULL,
  use_unmod_peptides = FALSE,
  protein_id_col = "Protein.Group",
  fasta_protein_name = "uniprot_ac",
  global_qvalue_cutoff = 0.01,
  qvalue_cutoff = 0.01,
  pg_qvalue_cutoff = 0.01,
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeOxidationMpeptides = TRUE,
  removeProtein_with1Feature = FALSE,
  MBR = TRUE,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

list of one or two data.frame of class MSstatsTMT, named PTM and PROTEIN

Examples

# ptm = read.csv("Phospho/report.tsv", sep="\t")
# protein = read.csv("Protein/report.tsv", sep="\t")
# annotation = read.csv("Phospho/annotation.csv")
# annotation_protein = read.csv("Protein/annotation.csv")

#DIANNtoMSstatsPTMFormat(ptm, annotation, 
#                        protein, annotation_protein,
#                        fasta_path="fasta_file.fasta")

Description

Automatically created by FragPipe, manually checked by the user and input into the FragPipetoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.

Usage

fragpipe_annotation

Format

A data.table with 7 columns.

Details

• Run : Run name that matches exactly with FragPipe run. Used to join evidence and metadata in annotation file.

• Fraction : If multiple fractions were used (i.e. the same mixture split into multiple fractions) enter that here. TechRepMixture : Multiple runs using the same bioreplicate

• Channel : Mixture channel used

• Condition : Name of condition that was used for each run.

• Mixture : The unique mixture (plex) name

• BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.

Examples

head(fragpipe_annotation)

Description

Automatically created by FragPipe, manually checked by the user and input into the FragPipetoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.

Usage

fragpipe_annotation_protein

Format

A data.table with 7 columns.

Details

• Run : Run name that matches exactly with FragPipe run. Used to join evidence and metadata in annotation file.

• Fraction : If multiple fractions were used (i.e. the same mixture split into multiple fractions) enter that here. TechRepMixture : Multiple runs using the same bioreplicate

• Channel : Mixture channel used

• Condition : Name of condition that was used for each run.

• Mixture : The unique mixture (plex) name

• BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.

Examples

head(fragpipe_annotation_protein)

Description

This dataset was provided by the FragPipe team at the Nesvilab. It was processed using Philosopher and targeted Phosphorylation.

Usage

fragpipe_input

Format

A data.table with 29 columns and 246 rows.

Examples

head(fragpipe_input)

Description

This dataset was provided by the FragPipe team at the Nesvilab. It was processed using Philosopher and targeted Phosphorylation.

Usage

fragpipe_input_protein

Format

A data.table with 27 columns and 47 rows.

Examples

head(fragpipe_input_protein)

Description

Takes as input TMT experiments which are the output of Fragpipe and converts into MSstatsPTM format. Requires msstats.csv file and an annotation file. Optionally an additional msstats.csv file can be uploaded if a corresponding global profiling run was performed. Site localization is performed and only high probability localizations are kept.

Usage

FragPipetoMSstatsPTMFormat(
  input,
  annotation = NULL,
  input_protein = NULL,
  annotation_protein = NULL,
  use_unmod_peptides = FALSE,
  label_type = "TMT",
  protein_id_col = "Protein",
  peptide_id_col = "Peptide.Sequence",
  mod_id_col = "STY",
  localization_cutoff = 0.75,
  remove_unlocalized_peptides = TRUE,
  Purity_cutoff = 0.6,
  PeptideProphet_prob_cutoff = 0.7,
  useUniquePeptide = TRUE,
  rmPSM_withfewMea_withinRun = FALSE,
  rmPeptide_OxidationM = TRUE,
  rmProtein_with1Feature = FALSE,
  summaryforMultipleRows = sum,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

list of one or two data.frame of class MSstatsTMT, named PTM and PROTEIN

Examples

# TMT Example (with global profiling run)
head(fragpipe_input)
head(fragpipe_annotation)
head(fragpipe_input_protein)
head(fragpipe_annotation_protein)

msstats_data = FragPipetoMSstatsPTMFormat(fragpipe_input,
                                          fragpipe_annotation,
                                          fragpipe_input_protein, 
                                          fragpipe_annotation_protein,
                                          label_type="TMT",
                                          mod_id_col = "STY",
                                          localization_cutoff=.75,
                                          remove_unlocalized_peptides=TRUE)
head(msstats_data$PTM)
head(msstats_data$PROTEIN)

# LFQ Example (w/out global profiling run)
input = system.file("tinytest/raw_data/Fragpipe/MSstats.csv", 
                                        package = "MSstatsPTM")
input = data.table::fread(input)
annot = system.file("tinytest/raw_data/Fragpipe/experiment_annotation.tsv", 
                                        package = "MSstatsPTM")
annot = data.table::fread(annot)                                        

msstats_data = FragPipetoMSstatsPTMFormat(input,
                                          annot,
                                          label_type="LF",
                                          mod_id_col = "STY",
                                          localization_cutoff=.75,
                                          protein_id_col = "ProteinName",
                                          peptide_id_col = "PeptideSequence")
head(msstats_data$PTM)

# Note that this is NULL because we did not include a global profiling run.
# Ideally, you should include an independent global profiling run.
head(msstats_data$PROTEIN)

Description

To analyze the results of modeling changes in abundance of modified peptides and overall protein, groupComparisonPlotsPTM takes as input the results of the groupComparisonPTM function. It asses the results of three models: unadjusted PTM, adjusted PTM, and overall protein. To asses the results of the model, the following visualizations can be created: (1) VolcanoPlot (specify "VolcanoPlot" in option type), to plot peptides or proteins and their significance for each model. (2) Heatmap (specify "Heatmap" in option type), to evaluate the fold change between conditions and peptides/proteins

Usage

groupComparisonPlotsPTM(
  data = data,
  type,
  sig = 0.05,
  FCcutoff = FALSE,
  logBase.pvalue = 10,
  ylimUp = FALSE,
  ylimDown = FALSE,
  xlimUp = FALSE,
  x.axis.size = 10,
  y.axis.size = 10,
  dot.size = 3,
  text.size = 4,
  text.angle = 0,
  legend.size = 13,
  ProteinName = TRUE,
  colorkey = TRUE,
  numProtein = 50,
  width = 10,
  height = 10,
  which.Comparison = "all",
  which.PTM = "all",
  address = ""
)

Arguments

Value

plot or pdf

Examples

model.lf.msstatsptm = groupComparisonPTM(summary.data, 
                                     data.type = "LabelFree")
groupComparisonPlotsPTM(data = model.lf.msstatsptm,
                        type = "VolcanoPlot",
                        FCcutoff= 2,
                        logBase.pvalue = 2,
                        address=FALSE)

Description

Takes summarized PTM and protein data from dataSummarizationPTM or dataSummarizationPTM_TMT functions and performs differential analysis. Leverages unmodified protein data to perform adjustment and deconvolute the effect of the PTM and unmodified protein. If protein data is unavailable, PTM data can still be passed into the function, however adjustment can not be performed. All model results are returned for completeness.

Usage

groupComparisonPTM(
  data,
  data.type,
  contrast.matrix = "pairwise",
  moderated = FALSE,
  adj.method = "BH",
  log_base = 2,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  base = "MSstatsPTM_log_"
)

Arguments

Value

list of modeling results. Includes PTM, PROTEIN, and ADJUSTED data.tables with their corresponding model results.

Examples

model.lf.msstatsptm = groupComparisonPTM(summary.data, 
                                     data.type = "LabelFree",
                                     verbose = FALSE)

Description

locateMod locates modified sites with a peptide.

Usage

locateMod(peptide, aaStart, residueSymbol)

Arguments

Value

A string.

Examples

locateMod("P*EP*TIDE", 3, "\\*")

Description

PTMlocate annotates modified sites with associated peptides.

Usage

locatePTM(peptide, uniprot, fasta, modResidue, modSymbol, rmConfound = FALSE)

Arguments

Value

A data frame with three columns: uniprot_iso, peptide, site.

Examples

fasta = tidyFasta(system.file("extdata", "O13297.fasta", package="MSstatsPTM"))
locatePTM("DRVSYIHNDSC*TR", "O13297", fasta, "C", "\\*")

Description

Must be manually created by the user and input into the MaxQtoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.

Usage

maxq_lf_annotation

Format

A data.table with 5 columns.

Details

• Run : Run name that matches exactly with MaxQuant run. Used to join evidence and metadata in annotation file.

• Condition : Name of condition that was used for each run.

• BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.

• Raw.file : Run name that matches exactly with MaxQuant run. Used to join evidence and metadata in annotation file.

• IsotopeLabelType: Name of isotope label. May be all L or unique depending on experimental design.

Examples

head(maxq_lf_annotation)

Description

Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).

Usage

maxq_lf_evidence

Format

a data.table with 63 columns and 511 rows, the output of MaxQuant

Details

Bekker-Jensen, D.B., Bernhardt, O.M., Hogrebe, A. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 11, 787 (2020). https://doi.org/10.1038/s41467-020-14609-1

The experiment was processed using MaxQuant by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).

The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.

Examples

head(maxq_lf_evidence)

Description

Must be manually created by the user and input into the MaxQtoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.

Usage

maxq_tmt_annotation

Format

A data.table with 7 columns.

Details

• Run : Run name that matches exactly with MaxQuant run. Used to join evidence and metadata in annotation file.

• Fraction : If multiple fractions were used (i.e. the same mixture split into multiple fractions) enter that here. TechRepMixture : Multiple runs using the same bioreplicate

• Channel : Mixture channel used

• Condition : Name of condition that was used for each run.

• Mixture : The unique mixture (plex) name

• BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.

Examples

head(maxq_tmt_annotation)

Description

Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).

Usage

maxq_tmt_evidence

Format

a data.table with 96 columns and 199 rows, the output of MaxQuant

Details

Hogrebe, A., von Stechow, L., Bekker-Jensen, D.B. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat Commun 9, 1045 (2018). https://doi.org/10.1038/s41467-018-03309-6

The experiment was processed using MaxQuant by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).

The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.

Examples

head(maxq_tmt_evidence)

Description

Takes as input LF/TMT experiments from MaxQ and converts the data into the format needed for MSstatsPTM. Requires modified evidence.txt file from MaxQ and an annotation file for PTM data. To adjust modified peptides for changes in global protein level, unmodified TMT experimental data must also be returned. Optionally can use ⁠Phospho(STY)Sites.txt⁠ (or other PTM specific files) from MaxQuant, but this is not recommended. If PTM specific file provided, the raw intensities must be provided, not a ratio.

Usage

MaxQtoMSstatsPTMFormat(
  evidence = NULL,
  annotation = NULL,
  fasta_path,
  fasta_protein_name = "uniprot_ac",
  mod_id = "\\(Phospho \\(STY\\)\\)",
  sites_data = NULL,
  evidence_prot = NULL,
  proteinGroups = NULL,
  annotation_protein = NULL,
  use_unmod_peptides = FALSE,
  labeling_type = "LF",
  mod_num = "Single",
  TMT_keyword = "TMT",
  ptm_keyword = "phos",
  which_proteinid_ptm = "Proteins",
  which_proteinid_protein = "Proteins",
  remove_other_mods = TRUE,
  removeMpeptides = FALSE,
  removeOxidationMpeptides = FALSE,
  removeProtein_with1Peptide = FALSE,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.

Examples

# TMT experiment
head(maxq_tmt_evidence)
head(maxq_tmt_annotation)

msstats_format_tmt = MaxQtoMSstatsPTMFormat(evidence=maxq_tmt_evidence,
                        annotation=maxq_tmt_annotation,
                        fasta=system.file("extdata", "maxq_tmt_fasta.fasta", package="MSstatsPTM"),
                        fasta_protein_name="uniprot_ac",
                        mod_id="\\(Phospho \\(STY\\)\\)",
                        use_unmod_peptides=TRUE,
                        labeling_type = "TMT",
                        which_proteinid_ptm = "Proteins")

head(msstats_format_tmt$PTM)
head(msstats_format_tmt$PROTEIN)

# LF experiment
head(maxq_lf_evidence)
head(maxq_lf_annotation)

msstats_format_lf = MaxQtoMSstatsPTMFormat(evidence=maxq_lf_evidence,
                        annotation=maxq_lf_annotation,
                        fasta=system.file("extdata", "maxq_lf_fasta.fasta", package="MSstatsPTM"),
                        fasta_protein_name="uniprot_ac",
                        mod_id="\\(Phospho \\(STY\\)\\)",
                        use_unmod_peptides=TRUE,
                        labeling_type = "LF",
                        which_proteinid_ptm = "Proteins")
head(msstats_format_lf$PTM)
head(msstats_format_lf$PROTEIN)

Description

Import Metamorpheus files into PTM format

Usage

MetamorpheusToMSstatsPTMFormat(
  input,
  annotation,
  fasta_path,
  input_protein = NULL,
  annotation_protein = NULL,
  use_unmod_peptides = FALSE,
  mod_ids = c("\\[Common Biological:Phosphorylation on S\\]"),
  useUniquePeptide = TRUE,
  removeFewMeasurements = TRUE,
  removeProtein_with1Feature = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.

Author(s)

Anthony Wu

Examples

input = system.file("tinytest/raw_data/Metamorpheus/AllQuantifiedPeaks.tsv", 
                                package = "MSstatsPTM")
input = data.table::fread(input)
annot = system.file("tinytest/raw_data/Metamorpheus/ExperimentalDesign.tsv", 
                                package = "MSstatsPTM")
annot = data.table::fread(annot)
input_protein = system.file("tinytest/raw_data/Metamorpheus/AllQuantifiedPeaksGlobalProteome.tsv",
                                package = "MSstatsPTM")
input_protein = data.table::fread(input_protein)
annot_protein = system.file("tinytest/raw_data/Metamorpheus/ExperimentalDesignGlobalProteome.tsv", 
                                package = "MSstatsPTM")
annot_protein = data.table::fread(annot_protein)
fasta_path=system.file("extdata", "metamorpheus_fasta.fasta", 
                                package="MSstatsPTM")
metamorpheus_imported = MetamorpheusToMSstatsPTMFormat(
    input, 
    annot, 
    fasta_path=fasta_path,
    input_protein=input_protein,
    annotation_protein=annot_protein,
    use_unmod_peptides=FALSE,
    mod_ids = c("\\[Common Fixed:Carbamidomethyl on C\\]")
)
head(metamorpheus_imported$PTM)
head(metamorpheus_imported$PROTEIN)

Description

A set of tools for detecting differentially abundant PTMs and proteins in shotgun mass spectrometry-based proteomic experiments. The package can handle a variety of acquisition types, including label free and TMT experiments, acquired with DDA, DIA, SRM or PRM acquisition methods. The package includes tools to convert raw data from different spectral processing tools, summarize feature intensities, and fit a linear mixed effects model. A major advantage of the package is to leverage a separate global profiling run and adjust the PTM fold change for changes in the unmodified protein, showing the unconvoluted PTM fold change. Finally, the package includes functionality to plot a variety of data visualizations.

functions

• FragPipetoMSstatsPTMFormat : Generates MSstatsPTM required input format for TMT FragePipe outputs.

• MaxQtoMSstatsPTMFormat : Generates MSstatsPTM required input format for label-free and TMT MaxQuant outputs.

• ProgenesistoMSstatsPTMFormat : Generates MSstatsPTM required input format for label-free Progenesis outputs.

• SpectronauttoMSstatsPTMFormat : Generates MSstatsPTM required input format for label-free Spectronaut outputs.

• SkylinetoMSstatsPTMFormat : Generates MSstatsPTM required input format for Skyline outputs.

• PStoMSstatsPTMFormat : Generates MSstatsPTM required input format for PEAKS outputs.

• PDtoMSstatsPTMFormat : Generates MSstatsPTM required input format for Proteome Discoverer outputs.

• dataSummarizationPTM : Summarizes PSM level quantification to peptide (modification) and protein level quantification. For use in label-free analysis

• dataSummarizationPTM_TMT : Summarizes PSM level quantification to peptide (modification) and protein level quantification. For use in TMT analysis.

• dataProcessPlotsPTM : Visualization for explanatory data analysis. Specifically gives ability to plot Profile and Quality Control plots.

• groupComparisonPTM : Tests for significant changes in PTM and protein abundance across conditions. Adjusts PTM fold change for changes in protein abundance.

• groupComparisonPlotsPTM : Visualization for model-based analysis and summarization

Author(s)

Maintainer: Devon Kohler [email protected]

Authors:

• Tsung-Heng Tsai [email protected]

• Ting Huang [email protected]

• Mateusz Staniak [email protected]

• Meena Choi [email protected]

• Olga Vitek [email protected]

See Also

Useful links:

• Report bugs at https://github.com/Vitek-Lab/MSstatsPTM/issues

Description

Locate modification site number and amino acid

Usage

MSstatsPTMSiteLocator(
  data,
  protein_name_col = "ProteinName",
  unmod_pep_col = "PeptideSequence",
  mod_pep_col = "PeptideModifiedSequence",
  clean_mod = FALSE,
  fasta_file = NULL,
  fasta_protein_name = "header",
  mod_id = "\\*",
  localization_scores = FALSE,
  localization_cutoff = 0.75,
  remove_unlocalized_peptides = TRUE,
  terminus_included = FALSE,
  terminus_id = "\\.",
  mod_id_is_numeric = FALSE,
  remove_underscores = FALSE,
  remove_other_mods = FALSE,
  bracket = FALSE,
  replace_text = FALSE
)

Arguments

Value

data.table with site location added into Protein column.

Examples

##TODO

Description

Must be manually created by the user and input into the PDtoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.

Usage

pd_annotation

Format

A data.table with 3 columns.

Details

• Run : Run name that matches exactly with PD run. Used to join evidence and metadata in annotation file.

• Condition : Name of condition that was used for each run.

• BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.

Examples

head(pd_annotation)

Description

Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).

Usage

pd_psm_input

Format

a data.table with 60 columns and 1657 rows, the output of PD

Details

Bekker-Jensen, D.B., Bernhardt, O.M., Hogrebe, A. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 11, 787 (2020). https://doi.org/10.1038/s41467-020-14609-1

The experiment was processed using Proteome Discoverer by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).

The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.

Examples

head(pd_psm_input)

Description

output using example data provided in package

Usage

pd_testing_output

Format

a list with 2 data.frames

Details

The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.

Examples

head(pd_testing_output)

Description

Import Proteome Discoverer files, identify modification site location.

Usage

PDtoMSstatsPTMFormat(
  input,
  annotation,
  fasta_path,
  protein_input = NULL,
  annotation_protein = NULL,
  labeling_type = "LF",
  mod_id = "\\(Phospho\\)",
  use_localization_cutoff = FALSE,
  keep_all_mods = FALSE,
  use_unmod_peptides = FALSE,
  fasta_protein_name = "uniprot_iso",
  localization_cutoff = 75,
  remove_unlocalized_peptides = TRUE,
  useNumProteinsColumn = FALSE,
  useUniquePeptide = TRUE,
  summaryforMultipleRows = max,
  removeFewMeasurements = TRUE,
  removeOxidationMpeptides = FALSE,
  removeProtein_with1Peptide = FALSE,
  which_quantification = "Precursor.Area",
  which_proteinid = "Protein.Group.Accessions",
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

list of data.table

Examples

# Global profiling example
input = system.file("tinytest/raw_data/PD/pd-ptm-input.csv", 
package = "MSstatsPTM")
input = data.table::fread(input)
annot = system.file("tinytest/raw_data/PD/pd-ptm-annot.csv",
                    package = "MSstatsPTM")
annot = data.table::fread(annot)
input_protein = system.file("tinytest/raw_data/PD/protein-input.csv",
                            package = "MSstatsPTM")
input_protein = data.table::fread(input_protein)
annot_protein = system.file("tinytest/raw_data/PD/protein-annot.csv",
                            package = "MSstatsPTM")
annot_protein = data.table::fread(annot_protein)
fasta_path=system.file("extdata", "pd_with_proteome.fasta", 
                       package="MSstatsPTM")
pd_imported = PDtoMSstatsPTMFormat(
    input,
    annotation = annot,
    protein_input = input_protein,
    annotation_protein = annot_protein,
    fasta_path = fasta_path,
    mod_id = "\\(GG\\)",
    labeling_type = "TMT",
    use_localization_cutoff = FALSE,
    which_proteinid = "Master.Protein.Accessions")
   
head(pd_imported$PTM)
head(pd_imported$PROTEIN)

# No global profiling example
head(pd_psm_input)
head(pd_annotation)

msstats_format = PDtoMSstatsPTMFormat(pd_psm_input, 
                                      pd_annotation, 
                                      system.file("extdata", "pd_fasta.fasta", package="MSstatsPTM"),
                                      use_unmod_peptides=TRUE, 
                                      which_proteinid = "Master.Protein.Accessions")

head(msstats_format$PTM)
head(msstats_format$PROTEIN)

Description

Converts non-TMT Progenesis output into the format needed for MSstatsPTM

Usage

ProgenesistoMSstatsPTMFormat(
  ptm_input,
  annotation,
  global_protein_input = FALSE,
  fasta_path = FALSE,
  useUniquePeptide = TRUE,
  summaryforMultipleRows = max,
  removeFewMeasurements = TRUE,
  removeOxidationMpeptides = FALSE,
  removeProtein_with1Peptide = FALSE,
  mod.num = "Single"
)

Arguments

Value

a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.

Examples

input = system.file("tinytest/raw_data/Progenesis/progenesis_peptide.csv", 
                                package = "MSstatsPTM")
input = data.table::fread(input)
colnames(input) = unlist(input[1,])
input = input[-1,]
annot = system.file("tinytest/raw_data/Progenesis/phospho_annotation.csv", 
                                package = "MSstatsPTM")
annot = data.table::fread(annot)
prog_imported = ProgenesistoMSstatsPTMFormat(
    input, 
    annot
)
head(prog_imported$PTM)

Description

Currently only supports label-free quantification.

Usage

PStoMSstatsPTMFormat(
  input,
  annotation,
  input_protein = NULL,
  annotation_protein = NULL,
  use_unmod_peptides = FALSE,
  target_modification = NULL,
  remove_oxidation_peptides = FALSE,
  remove_multi_mod_types = FALSE,
  summaryforMultipleRows = max,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

list of data.table

Examples

# The output should be in the following format.
head(raw.input$PTM)
head(raw.input$PROTEIN)

Description

It can be the output of MSstatsPTM converter ProgenesistoMSstatsPTMFormat or other MSstats converter functions (Please see MSstatsPTM_LabelFree_Workflow vignette). The dataset is formatted as a list with two data.tables named PTM and PROTEIN. In each data.table the variables are as follows:

Usage

raw.input

Format

A list of two data.tables named PTM and PROTEIN with 1745 and 478 rows respectively.

Details

#'

• ProteinName : Name of protein with modification site mapped in with an underscore. ie "Protein_4_Y474"

• PeptideSequence

• Condition : Condition (ex. Healthy, Cancer, Time0)

• BioReplicate : Unique ID for biological subject.

• Run : MS run ID.

• Intensity

• PrecursorCharge

• FragmentIon

• ProductCharge

• IsotopeLabelType

Examples

head(raw.input$PTM)
head(raw.input$PROTEIN)

Description

It can be the output of MSstatsPTM converter MaxQtoMSstatsPTMFormat or other MSstatsTMT converter functions (Please see MSstatsPTM_TMT_Workflow vignette). The dataset is formatted as a list with two data.tables named PTM and PROTEIN. In each data.table the variables are as follows:

Usage

raw.input.tmt

Format

A list of two data.tables named PTM and PROTEIN with 1716 and 29221 rows respectively.

Details

• ProteinName : Name of protein with modification site mapped in with an underscore. ie "Protein_4_Y474"

• PeptideSequence

• Charge

• PSM

• Mixture : Mixture of samples labeled with different TMT reagents, which can be analyzed in a single mass spectrometry experiment. If the channal doesn't have sample, please add ⁠Empty' under Condition. \item TechRepMixture : Technical replicate of one mixture. One mixture may have multiple technical replicates. For example, if ⁠TechRepMixture' = 1, 2 are the two technical replicates of one mixture, then they should match with same ⁠Mixture' value. \item Run : MS run ID. \item Channel : Labeling information (126, ... 131). \item Condition : Condition (ex. Healthy, Cancer, Time0) \item BioReplicate : Unique ID for biological subject. If the channal doesn't have sample, please add ⁠Empty' under BioReplicate.

• Intensity

Examples

head(raw.input.tmt$PTM)
head(raw.input.tmt$PROTEIN)

Description

Currently only supports label-free quantification.

Usage

SkylinetoMSstatsPTMFormat(
  input,
  fasta_path,
  fasta_protein_name = "uniprot_iso",
  annotation = NULL,
  input_protein = NULL,
  annotation_protein = NULL,
  use_unmod_peptides = FALSE,
  removeiRT = TRUE,
  filter_with_Qvalue = TRUE,
  qvalue_cutoff = 0.01,
  use_unique_peptide = TRUE,
  remove_few_measurements = FALSE,
  remove_oxidation_peptides = FALSE,
  removeProtein_with1Feature = FALSE,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Value

list of data.table

Examples

# The output should be in the following format.
head(raw.input$PTM)
head(raw.input$PROTEIN)

Description

Must be manually created by the user and input into the SpectronauttoMSstatsPTMFormat converter. Requires the correct columns and maps the experimental desing into the MSstats format. Specify unique bioreplicates for group comparison designs, and the same bioreplicate for repeated measure designs. The columns and descriptions are below.

Usage

spectronaut_annotation

Format

A data.table with 5 columns.

Details

• Run : Run name that matches exactly with Spectronaut run. Used to join evidence and metadata in annotation file.

• Condition : Name of condition that was used for each run.

• BioReplicate : Name of biological replicate. Repeating the same name here will tell MSstatsPTM that the experiment is a repeated measure design.

• Raw.file : Run name that matches exactly with Spectronaut run. Used to join evidence and metadata in annotation file.

Examples

head(spectronaut_annotation)

Description

Experiment was performed by the Olsen lab and published on Nat. Commun. (citation below).

Usage

spectronaut_input

Format

a data.table with 23 columns and 2683 rows, the output of Spectronaut

Details

Bekker-Jensen, D.B., Bernhardt, O.M., Hogrebe, A. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 11, 787 (2020). https://doi.org/10.1038/s41467-020-14609-1

The experiment was processed using Spectronaut by the computational proteomics team at Pfizer (Liang Xue and Pierre Jean).

The experiment did not contain a global profiling run, but we show an example of extracting the unmodified peptides and using them in place of the profiling run.

Examples

head(spectronaut_input)

Description

Converters label-free Spectronaut data into MSstatsPTM format. Requires PSM output from Spectronaut and a custom made annotation file, mapping the run name to the condition and bioreplicate. Can optionally take a seperate PSM file for a global profiling run. If no global profiling run provided, the function can extract the unmodified peptides from the PTM PSM file and use them as a global profiling run (not recommended).

Usage

SpectronauttoMSstatsPTMFormat(
  input,
  annotation = NULL,
  fasta_path = NULL,
  protein_input = NULL,
  annotation_protein = NULL,
  use_unmod_peptides = FALSE,
  intensity = "PeakArea",
  mod_id = "\\[Phospho \\(STY\\)\\]",
  fasta_protein_name = "uniprot_iso",
  remove_other_mods = TRUE,
  filter_with_Qvalue = TRUE,
  qvalue_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

Value

a list of two data.tables named 'PTM' and 'PROTEIN' in the format required by MSstatsPTM.

Examples

head(spectronaut_input)
head(spectronaut_annotation)

msstats_input = SpectronauttoMSstatsPTMFormat(spectronaut_input, 
                  annotation=spectronaut_annotation, 
                  fasta_path=system.file("extdata", "spectronaut_fasta.fasta", package="MSstatsPTM"),
                  use_unmod_peptides=TRUE,
                  mod_id = "\\[Phospho \\(STY\\)\\]",
                  fasta_protein_name = "uniprot_iso"
                  )

head(msstats_input$PTM)
head(msstats_input$PROTEIN)

Description

It is made from raw.input. It is the output of dataSummarizationPTM function from MSstatsPTM. It should include a list with two names PTM and PROTEIN. Each of these list values is also a list with two names ProteinLevelData and FeatureLevelData, which correspond to two data.tables.The columns in these two data.tables are listed below. The variables are as follows:

• FeatureLevelData :

  • PROTEIN : Protein ID with modification site mapped in. Ex. Protein_1002_S836

  • PEPTIDE : Full peptide with charge

  • TRANSITION: Charge

  • FEATURE : Combination of Protien, Peptide, and Transition Columns

  • LABEL :

  • GROUP : Condition (ex. Healthy, Cancer, Time0)

  • RUN : Unique ID for technical replicate of one TMT mixture.

  • SUBJECT : Unique ID for biological subject.

  • FRACTION : Unique Fraction ID

  • originalRUN : Run name

  • censored :

  • INTENSITY : Unique ID for TMT mixture.

  • ABUNDANCE : Unique ID for TMT mixture.

  • newABUNDANCE : Unique ID for TMT mixture.

  • predicted : Unique ID for TMT mixture.

• ProteinLevelData :

  • RUN : MS run ID

  • Protein : Protein ID with modification site mapped in. Ex. Protein_1002_S836

  • LogIntensities: Protein-level summarized abundance

  • originalRUN : Labeling information (126, ... 131)

  • GROUP : Condition (ex. Healthy, Cancer, Time0)

  • SUBJECT : Unique ID for biological subject.

  • TotalGroupMeasurements : Unique ID for technical replicate of one TMT mixture.

  • NumMeasuredFeature : Unique ID for TMT mixture.

  • MissingPercentage : Unique ID for TMT mixture.

  • more50missing : Unique ID for TMT mixture.

  • NumImputedFeature : Unique ID for TMT mixture.

Usage

summary.data

Format

A list of two lists with four data.tables.

Examples

head(summary.data)

Description

It is made from raw.input.tmt. It is the output of dataSummarizationPTM_TMT function from MSstatsPTM. It should include a list with two names PTM and PROTEIN. Each of these list values is also a list with two names ProteinLevelData and FeatureLevelData, which correspond to two data.tables.The columns in these two data.tables are listed below. The variables are as follows:

• FeatureLevelData :

  • ProteinName : MS run ID

  • PSM : Protein ID with modification site mapped in. Ex. Protein_1002_S836

  • censored: Protein-level summarized abundance

  • predicted : Labeling information (126, ... 131)

  • log2Intensity : Condition (ex. Healthy, Cancer, Time0)

  • Run : Unique ID for biological subject.

  • Channel : Unique ID for technical replicate of one TMT mixture.

  • BioReplicate : Unique ID for TMT mixture.

  • Condition : Unique ID for TMT mixture.

  • Mixture : Unique ID for TMT mixture.

  • TechRepMixture : Unique ID for TMT mixture.

  • PeptideSequence : Unique ID for TMT mixture.

  • Charge : Unique ID for TMT mixture.

• ProteinLevelData :

  • Mixture : MS run ID

  • TechRepMixture : Protein ID with modification site mapped in. Ex. Protein_1002_S836

  • Run: Protein-level summarized abundance

  • Channel : Labeling information (126, ... 131)

  • Protein : Condition (ex. Healthy, Cancer, Time0)

  • Abundance : Unique ID for biological subject.

  • BioReplicate : Unique ID for technical replicate of one TMT mixture.

  • Condition : Unique ID for TMT mixture.

Usage

summary.data.tmt

Format

A list of two lists with four data.tables.

Examples

head(summary.data.tmt)

Description

tidyFasta reads and tidys FASTA file. Use this function as the first step in identifying modification sites.

Usage

tidyFasta(path)

Arguments

Value

A data.table with columns named header, sequence, uniprot_ac, uniprot_iso, entry_name.

Examples

tidyFasta(system.file("extdata", "O13297.fasta", package="MSstatsPTM"))