Package 'MSstats'

Description

Check if data represents repeated measurements design

Usage

checkRepeatedDesign(summarization_output)

Arguments

Details

This extracts information required by the group comparison workflow

Value

logical, TRUE if data represent repeated measurements design

Examples

QuantData1 <- dataProcess(SRMRawData, use_log_file = FALSE)
checkRepeatedDesign(QuantData1)

Description

Process MS data: clean, normalize and summarize before differential analysis

Usage

dataProcess(
  raw,
  logTrans = 2,
  normalization = "equalizeMedians",
  nameStandards = NULL,
  featureSubset = "all",
  remove_uninformative_feature_outlier = FALSE,
  min_feature_count = 2,
  n_top_feature = 3,
  summaryMethod = "TMP",
  equalFeatureVar = TRUE,
  censoredInt = "NA",
  MBimpute = TRUE,
  remove50missing = FALSE,
  fix_missing = NULL,
  maxQuantileforCensored = 0.999,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  numberOfCores = 1
)

Arguments

Examples

# Consider a raw data (i.e. SRMRawData) for a label-based SRM experiment from a yeast study
# with ten time points (T1-T10) of interests and three biological replicates.
# It is a time course experiment. The goal is to detect protein abundance changes
# across time points.
head(SRMRawData)
# Log2 transformation and normalization are applied (default)
QuantData<-dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
# Log10 transformation and normalization are applied
QuantData1<-dataProcess(SRMRawData, logTrans=10, use_log_file = FALSE)
head(QuantData1$FeatureLevelData)
# Log2 transformation and no normalization are applied
QuantData2<-dataProcess(SRMRawData,normalization=FALSE, use_log_file = FALSE)
head(QuantData2$FeatureLevelData)

Description

To illustrate the quantitative data after data-preprocessing and quality control of MS runs, dataProcessPlots takes the quantitative data from function (dataProcess) as input and automatically generate three types of figures in pdf files as output : (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; (3) mean plot for conditions (specify "ConditionPlot" in option type), to illustrate mean and variability of each condition per protein.

Usage

dataProcessPlots(
  data,
  type,
  featureName = "Transition",
  ylimUp = FALSE,
  ylimDown = FALSE,
  scale = FALSE,
  interval = "CI",
  x.axis.size = 10,
  y.axis.size = 10,
  text.size = 4,
  text.angle = 0,
  legend.size = 7,
  dot.size.profile = 2,
  dot.size.condition = 3,
  width = 800,
  height = 600,
  which.Protein = "all",
  originalPlot = TRUE,
  summaryPlot = TRUE,
  save_condition_plot_result = FALSE,
  remove_uninformative_feature_outlier = FALSE,
  address = "",
  isPlotly = FALSE
)

Arguments

Details

• Profile Plot : identify the potential sources of variation of each protein. QuantData$FeatureLevelData is used for plots. X-axis is run. Y-axis is log-intensities of transitions. Reference/endogenous signals are in the left/right panel. Line colors indicate peptides and line types indicate transitions. In summarization plots, gray dots and lines are the same as original profile plots with QuantData$FeatureLevelData. Dark dots and lines are for summarized intensities from QuantData$ProteinLevelData.

• QC Plot : illustrate the systematic bias between MS runs. After normalization, the reference signals for all proteins should be stable across MS runs. QuantData$FeatureLevelData is used for plots. X-axis is run. Y-axis is log-intensities of transition. Reference/endogenous signals are in the left/right panel. The pdf file contains (1) QC plot for all proteins and (2) QC plots for each protein separately.

• Condition Plot : illustrate the systematic difference between conditions. Summarized intensnties from QuantData$ProteinLevelData are used for plots. X-axis is condition. Y-axis is summarized log transformed intensity. If scale is TRUE, the levels of conditions is scaled according to its actual values at x-axis. Red points indicate the mean for each condition. If interval is "CI", blue error bars indicate the confidence interval with 0.95 significant level for each condition. If interval is "SD", blue error bars indicate the standard deviation for each condition.The interval is not related with model-based analysis.

The input of this function is the quantitative data from function dataProcess.

Examples

# Consider quantitative data (i.e. QuantData) from a yeast study with ten time points of interests, 
# three biological replicates, and no technical replicates which is a time-course experiment. 
# The goal is to provide pre-analysis visualization by automatically generate two types of figures 
# in two separate pdf files. 
# Protein IDHC (gene name IDP2) is differentially expressed in time point 1 and time point 7, 
# whereas, Protein PMG2 (gene name GPM2) is not.

QuantData<-dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
# Profile plot
dataProcessPlots(data=QuantData,type="ProfilePlot")
# Quality control plot 
dataProcessPlots(data=QuantData,type="QCPlot")	
# Quantification plot for conditions
dataProcessPlots(data=QuantData,type="ConditionPlot")

Description

This is a data set obtained from a published study (Mueller, et. al, 2007). A controlled spike-in experiment, where 6 proteins, (horse myoglobin, bovine carbonic anhydrase, horse Cytochrome C, chicken lysozyme, yeast alcohol dehydrogenase, rabbit aldolase A) were spiked into a complex background in known concentrations in a latin square design. The experiment contained 6 mixtures, and each mixture was analyzed in label-free LC-MS mode with 3 technical replicates (resulting in the total of 18 runs). Each protein was represented by 7-21 peptides, and each peptide was represented by 1-5 transition.

Usage

DDARawData

Format

data.frame

Details

The raw data (input data for MSstats) is required to contain variable of ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity. The variable names should be fixed.

If the information of one or more columns is not available for the original raw data, please retain the column variables and type in fixed value. For example, the original raw data does not contain the information of PrecursorCharge and ProductCharge, we retain the column PrecursorCharge and ProductCharge and then type in NA for all transitions in RawData.

Variable Intensity is required to be original signal without any log transformation and can be specified as the peak of height or the peak of area under curve.

Value

data.frame with the required format of MSstats.

Author(s)

Meena Choi, Olga Vitek.

Maintainer: Meena Choi ([email protected])

References

Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" Bioinformatics, 30(17):1514-1526, 2014.

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" BMC Bioinformatics, 13:S16, 2012.

Mueller, L. N., Rinner, O., Schmidt, A., Letarte, S., Bodenmiller, B., Brusniak, M., Vitek, O., Aebersold, R., and Muller, M. (2007). SuperHirn - a novel tool for high resolution LC-MS based peptide/protein profiling. Proteomics, 7, 3470-3480. 3, 34

Examples

head(DDARawData)

Description

This is a data set obtained from a published study (Mueller, et. al, 2007). A controlled spike-in experiment, where 6 proteins, (horse myoglobin, bovine carbonic anhydrase, horse Cytochrome C, chicken lysozyme, yeast alcohol dehydrogenase, rabbit aldolase A) were spiked into a complex background in known concentrations in a latin square design. The experiment contained 6 mixtures, and each mixture was analyzed in label-free LC-MS mode with 3 technical replicates (resulting in the total of 18 runs). Each protein was represented by 7-21 peptides, and each peptide was represented by 1-5 transition. Skyline is used for processing.

Usage

DDARawData.Skyline

Format

data.frame

Details

The raw data (input data for MSstats) is required to contain variable of ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity. The variable names should be fixed.

This is 'MSstats input' format from Skyline used by 'MSstats_report.skyr'. The column names, 'FileName' and 'Area', should be changed to 'Run' and 'Intensity'. There are two extra columns called 'StandardType' and 'Truncated'.'StandardType' column can be used for normalization='globalStandard' in dataProcess. 'Truncated' columns can be used to remove the truncated peaks with skylineReport=TRUE in dataProcess.

If the information of one or more columns is not available for the original raw data, please retain the column variables and type in fixed value. For example, the original raw data does not contain the information of PrecursorCharge and ProductCharge, we retain the column PrecursorCharge and ProductCharge and then type in NA for all transitions in RawData.

Variable Intensity is required to be original signal without any log transformation and can be specified as the peak of height or the peak of area under curve.

Value

data.frame with the required format of MSstats.

Author(s)

Meena Choi, Olga Vitek.

Maintainer: Meena Choi ([email protected])

References

Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" Bioinformatics, 30(17):1514-1526, 2014.

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" BMC Bioinformatics, 13:S16, 2012.

Examples

head(DDARawData.Skyline)

Description

Calculate sample size for future experiments of a Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment based on intensity-based linear model. Two options of the calculation: (1) number of biological replicates per condition, (2) power.

Usage

designSampleSize(
  data,
  desiredFC,
  FDR = 0.05,
  numSample = TRUE,
  power = 0.9,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

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.

Author(s)

Meena Choi, Ching-Yun Chang, Olga Vitek.

Examples

# Consider quantitative data (i.e. QuantData) from yeast study.
# A time course study with ten time points of interests and three biological replicates.
QuantData <- dataProcess(SRMRawData)
head(QuantData$FeatureLevelData)
## based on multiple comparisons  (T1 vs T3; T1 vs T7; T1 vs T9)
comparison1<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
comparison2<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
comparison3<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
comparison<-rbind(comparison1,comparison2, comparison3)
row.names(comparison)<-c("T3-T1","T7-T1","T9-T1")
colnames(comparison)<-unique(QuantData$ProteinLevelData$GROUP)

testResultMultiComparisons<-groupComparison(contrast.matrix=comparison,data=QuantData)

## Calculate sample size for future experiments:
#(1) Minimal number of biological replicates per condition
designSampleSize(data=testResultMultiComparisons$FittedModel, numSample=TRUE,
                 desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
#(2) Power calculation
designSampleSize(data=testResultMultiComparisons$FittedModel, numSample=2,
                 desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)

Description

To illustrate the relationship of desired fold change and the calculated minimal number sample size which are (1) number of biological replicates per condition, (2) number of peptides per protein, (3) number of transitions per peptide, and (4) power. The input is the result from function (designSampleSize.

Usage

designSampleSizePlots(data, isPlotly = FALSE)

Arguments

Details

Data in the example is based on the results of sample size calculation from function designSampleSize

Value

Plot for estimated sample size with assigned variable.

Author(s)

Meena Choi, Ching-Yun Chang, Olga Vitek.

Examples

# Based on the results of sample size calculation from function designSampleSize,
# we generate a series of sample size plots for number of biological replicates, or peptides, 
# or transitions or power plot.
QuantData<-dataProcess(SRMRawData)
head(QuantData$ProcessedData)
## based on multiple comparisons  (T1 vs T3; T1 vs T7; T1 vs T9)
comparison1<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
comparison2<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
comparison3<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
comparison<-rbind(comparison1,comparison2, comparison3)
row.names(comparison)<-c("T3-T1","T7-T1","T9-T1")
colnames(comparison)<-unique(QuantData$ProteinLevelData$GROUP)

testResultMultiComparisons<-groupComparison(contrast.matrix=comparison, data=QuantData)

# plot the calculated sample sizes for future experiments:
# (1) Minimal number of biological replicates per condition
result.sample<-designSampleSize(data=testResultMultiComparisons$FittedModel, numSample=TRUE,
                                desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
designSampleSizePlots(data=result.sample)
# (2) Power
result.power<-designSampleSize(data=testResultMultiComparisons$FittedModel, numSample=2,
                               desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)
designSampleSizePlots(data=result.power)

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,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  MBR = TRUE,
  ...
)

Arguments

Value

data.frame in the MSstats required format.

Author(s)

Elijah Willie

Examples

## Not run: 
input = fread('diann_pooled_report.tsv')
annot = fread('Annotation.csv')
colnames(annot) = c('Condition', 'Run', 'BioReplicate')
input = DIANNtoMSstatsFormat(input, annotation = annot, MBR = F)
head(input)

## End(Not run)

Description

This example dataset was obtained from a group comparison study of S. Pyogenes. Two conditions, S. Pyogenes with 0% and 10% of human plasma added (denoted Strep 0% and Strep 10%), were profiled in two replicates, in the label-free mode, with a SWATH-MS-enabled AB SCIEX TripleTOF 5600 System. The identification and quantification of spectral peaks was assisted by a spectral library, and was performed using OpenSWATH software (http: //proteomics.ethz.ch/openswath.html). For reasons of space, the example dataset only contains two proteins from this study. Protein FabG shows strong evidence of differential abundance, while protein Probable RNA helicase exp9 only shows moderate evidence of dif- ferential abundance between conditions.

Usage

DIARawData

Format

data.frame

Details

The raw data (input data for MSstats) is required to contain variable of ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity. The variable names should be fixed.

If the information of one or more columns is not available for the original raw data, please retain the column variables and type in fixed value. For example, the original raw data does not contain the information of PrecursorCharge and ProductCharge, we retain the column PrecursorCharge and ProductCharge and then type in NA for all transitions in RawData.

Variable Intensity is required to be original signal without any log transformation and can be specified as the peak of height or the peak of area under curve.

Value

data.frame with the required format of MSstats.

Author(s)

Meena Choi, Olga Vitek.

Maintainer: Meena Choi ([email protected])

Examples

head(DIARawData)

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

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/annotations/annot_diau.csv", 
                    package = "MSstats")
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)

Description

An example SDRF file which is used to store metadata for MS-based protemics experiments.

Usage

example_SDRF

Format

data.frame

Details

An example SDRF file which is used to store metadata for MS-based protemics experiments.

Value

data.frame example of an SDRF file.

Author(s)

Mateusz Staniak, Devon Kohler, Olga Vitek.

Examples

head(example_SDRF)

Description

Extract experimental design from MSstats format into SDRF format

Usage

extractSDRF(
  data,
  run_name = "comment[data file]",
  condition_name = "characteristics[disease]",
  biological_replicate = "characteristics[biological replicate]",
  fraction = NULL,
  meta_data = NULL
)

Arguments

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)

SDRF_file = extractSDRF(maxq_imported)

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

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)

Description

Get feature-level data to be used in the MSstatsSummarizationOutput function

Usage

getProcessed(input)

Arguments

Value

data.table processed by dataProcess subfunctions

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input_all = MSstatsSelectFeatures(input, "all") # all features
input_5 = MSstatsSelectFeatures(data.table::copy(input), 
"topN", top_n = 5) # top 5 features

proc1 = getProcessed(input_all)
proc2 = getProcessed(input_5)

proc1
proc2

Description

Get information about number of measurements for each group

Usage

getSamplesInfo(summarization_output)

Arguments

Details

This function extracts information required to compute percentages of missing and imputed values in group comparison.

Value

data.table

Examples

QuantData <- dataProcess(DDARawData, use_log_file = FALSE)
samples_info <- getSamplesInfo(QuantData)
samples_info

Description

Get proteins based on names or integer IDs

Usage

getSelectedProteins(chosen_proteins, all_proteins)

Arguments

Value

character

Description

Whole plot testing

Usage

groupComparison(
  contrast.matrix,
  data,
  save_fitted_models = TRUE,
  log_base = 2,
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL,
  numberOfCores = 1
)

Arguments

Details

contrast.matrix : comparison of interest. Based on the levels of conditions, specify 1 or -1 to the conditions of interests and 0 otherwise. The levels of conditions are sorted alphabetically. Command levels(QuantData$FeatureLevelData$GROUP_ORIGINAL) can illustrate the actual order of the levels of conditions. The underlying model fitting functions are lm and lmer for the fixed effects model and mixed effects model, respectively. The input of this function is the quantitative data from function (dataProcess).

Value

list that consists of three elements: "ComparisonResult" - data.frame with results of statistical testing, "ModelQC" - data.frame with data used to fit models for group comparison and "FittedModel" - list of fitted models.

Examples

# Consider quantitative data (i.e. QuantData) from yeast study with ten time points of interests, 
# three biological replicates, and no technical replicates. 
# It is a time-course experiment and we attempt to compare differential abundance
# between time 1 and 7 in a set of targeted proteins. 
# In this label-based SRM experiment, MSstats uses the fitted model with expanded scope of 
# Biological replication.  
QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
levels(QuantData$ProteinLevelData$GROUP)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
groups = levels(QuantData$ProteinLevelData$GROUP)
colnames(comparison) <- groups[order(as.numeric(groups))]
# Tests for differentially abundant proteins with models:
# label-based SRM experiment with expanded scope of biological replication.
testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData,
                                           use_log_file = FALSE)
# table for result
testResultOneComparison$ComparisonResult

Description

To summarize the results of log-fold changes and adjusted p-values for differentially abundant proteins, groupComparisonPlots takes testing results from function (groupComparison) as input and automatically generate three types of figures in pdf files as output : (1) volcano plot (specify "VolcanoPlot" in option type) for each comparison separately; (2) heatmap (specify "Heatmap" in option type) for multiple comparisons ; (3) comparison plot (specify "ComparisonPlot" in option type) for multiple comparisons per protein.

Usage

groupComparisonPlots(
  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 = 100,
  clustering = "both",
  width = 800,
  height = 600,
  which.Comparison = "all",
  which.Protein = "all",
  address = "",
  isPlotly = FALSE
)

Arguments

Details

• Volcano plot : illustrate actual log-fold changes and adjusted p-values for each comparison separately with all proteins. The x-axis is the log fold change. The base of logarithm transformation is the same as specified in "logTrans" from dataProcess. The y-axis is the negative log2 or log10 adjusted p-values. The horizontal dashed line represents the FDR cutoff. The points below the FDR cutoff line are non-significantly abundant proteins (colored in black). The points above the FDR cutoff line are significantly abundant proteins (colored in red/blue for up-/down-regulated). If fold change cutoff is specified (FCcutoff = specific value), the points above the FDR cutoff line but within the FC cutoff line are non-significantly abundant proteins (colored in black)/

• Heatmap : illustrate up-/down-regulated proteins for multiple comparisons with all proteins. Each column represents each comparison of interest. Each row represents each protein. Color red/blue represents proteins in that specific comparison are significantly up-regulated/down-regulated proteins with FDR cutoff and/or FC cutoff. The color scheme shows the evidences of significance. The darker color it is, the stronger evidence of significance it has. Color gold represents proteins are not significantly different in abundance.

• Comparison plot : illustrate log-fold change and its variation of multiple comparisons for single protein. X-axis is comparison of interest. Y-axis is the log fold change. The red points are the estimated log fold change from the model. The blue error bars are the confidence interval with 0.95 significant level for log fold change. This interval is only based on the standard error, which is estimated from the model.

Examples

QuantData<-dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
## based on multiple comparisons  (T1 vs T3; T1 vs T7; T1 vs T9)
comparison1<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
comparison2<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
comparison3<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
comparison<-rbind(comparison1,comparison2, comparison3)
row.names(comparison)<-c("T3-T1","T7-T1","T9-T1")
groups = levels(QuantData$ProteinLevelData$GROUP)
colnames(comparison) <- groups[order(as.numeric(groups))]
testResultMultiComparisons<-groupComparison(contrast.matrix=comparison,
data=QuantData, 
use_log_file = FALSE)
testResultMultiComparisons$ComparisonResult
# Volcano plot with FDR cutoff = 0.05 and no FC cutoff
groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="VolcanoPlot",
logBase.pvalue=2, address="Ex1_")
# Volcano plot with FDR cutoff = 0.05, FC cutoff = 70, upper y-axis limit = 100, 
# and no protein name displayed
# FCcutoff=70 is for demonstration purpose
groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="VolcanoPlot",
FCcutoff=70, logBase.pvalue=2, ylimUp=100, ProteinName=FALSE,address="Ex2_")
# Heatmap with FDR cutoff = 0.05
groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="Heatmap", 
logBase.pvalue=2, address="Ex1_")
# Heatmap with FDR cutoff = 0.05 and FC cutoff = 70
# FCcutoff=70 is for demonstration purpose
groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="Heatmap",
FCcutoff=70, logBase.pvalue=2, address="Ex2_")
# Comparison Plot
groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="ComparisonPlot",
address="Ex1_")
# Comparison Plot
groupComparisonPlots(data=testResultMultiComparisons$ComparisonResult, type="ComparisonPlot",
ylimUp=8, ylimDown=-1, address="Ex2_")

Description

To check the assumption of linear model for whole plot inference, groupComparisonQCPlots takes the results after fitting models from function (groupComparison) as input and automatically generate two types of figures in pdf files as output: (1) normal quantile-quantile plot (specify "QQPlot" in option type) for checking normally distributed errors.; (2) residual plot (specify "ResidualPlot" in option type).

Usage

groupComparisonQCPlots(
  data,
  type,
  axis.size = 10,
  dot.size = 3,
  width = 10,
  height = 10,
  which.Protein = "all",
  address = ""
)

Arguments

Details

Results based on statistical models for whole plot level inference are accurate as long as the assumptions of the model are met. The model assumes that the measurement errors are normally distributed with mean 0 and constant variance. The assumption of a constant variance can be checked by examining the residuals from the model.

• QQPlots : a normal quantile-quantile plot for each protein is generated in order to check whether the errors are well approximated by a normal distribution. If points fall approximately along a straight line, then the assumption is appropriate for that protein. Only large deviations from the line are problematic.

• ResidualPlots : The plots of residuals against predicted(fitted) values. If it shows a random scatter, then the assumption is appropriate.

Value

produce a pdf file

Examples

QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
levels(QuantData$FeatureLevelData$GROUP)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
colnames(comparison) <- unique(QuantData$ProteinLevelData$GROUP)
# Tests for differentially abundant proteins with models:
# label-based SRM experiment with expanded scope of biological replication.
testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData,
use_log_file = FALSE)
# normal quantile-quantile plots
groupComparisonQCPlots(data=testResultOneComparison, type="QQPlots", address="")
# residual plots
groupComparisonQCPlots(data=testResultOneComparison, type="ResidualPlots", address="")

Description

Prepare a peptides dictionary for global standards normalization

Usage

makePeptidesDictionary(input, normalization)

Arguments

Details

This function extracts information required to perform normalization with global standards. It is useful for running the summarization workflow outside of the dataProcess function.

Examples

input = data.table::as.data.table(DDARawData)
peptides_dict = makePeptidesDictionary(input, "GLOBALSTANDARDS")
head(peptides_dict) # ready to be passed to the MSstatsNormalize function

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

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)

Description

To check the assumption of linear model for whole plot inference, modelBasedQCPlots takes the results after fitting models from function (groupComparison) as input and automatically generate two types of figures in pdf files as output: (1) normal quantile-quantile plot (specify "QQPlot" in option type) for checking normally distributed errors.; (2) residual plot (specify "ResidualPlot" in option type).

Usage

modelBasedQCPlots(
  data,
  type,
  axis.size = 10,
  dot.size = 3,
  width = 10,
  height = 10,
  which.Protein = "all",
  address = "",
  displayDeprecationMessage = TRUE
)

Arguments

Details

Results based on statistical models for whole plot level inference are accurate as long as the assumptions of the model are met. The model assumes that the measurement errors are normally distributed with mean 0 and constant variance. The assumption of a constant variance can be checked by examining the residuals from the model.

• QQPlots : a normal quantile-quantile plot for each protein is generated in order to check whether the errors are well approximated by a normal distribution. If points fall approximately along a straight line, then the assumption is appropriate for that protein. Only large deviations from the line are problematic.

• ResidualPlots : The plots of residuals against predicted(fitted) values. If it shows a random scatter, then the assumption is appropriate.

Value

produce a pdf file

Examples

QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
levels(QuantData$FeatureLevelData$GROUP)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
colnames(comparison) <- unique(QuantData$ProteinLevelData$GROUP)
# Tests for differentially abundant proteins with models:
# label-based SRM experiment with expanded scope of biological replication.
testResultOneComparison <- groupComparison(contrast.matrix=comparison, data=QuantData,
use_log_file = FALSE)
# normal quantile-quantile plots
modelBasedQCPlots(data=testResultOneComparison, type="QQPlots", address="")
# residual plots
modelBasedQCPlots(data=testResultOneComparison, type="ResidualPlots", address="")

Description

Create a contrast matrix for groupComparison function

Usage

MSstatsContrastMatrix(contrasts, conditions, labels = NULL)

Arguments

Description

Group comparison

Usage

MSstatsGroupComparison(
  summarized_list,
  contrast_matrix,
  save_fitted_models,
  repeated,
  samples_info,
  numberOfCores = 1
)

Arguments

Examples

QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
group_comparison_input = MSstatsPrepareForGroupComparison(QuantData)
levels(QuantData$ProteinLevelData$GROUP)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
groups = levels(QuantData$ProteinLevelData$GROUP)
colnames(comparison) <- groups[order(as.numeric(groups))]
samples_info = getSamplesInfo(QuantData)
repeated = checkRepeatedDesign(QuantData)
group_comparison = MSstatsGroupComparison(group_comparison_input, comparison,
                                          FALSE, repeated, samples_info)
length(group_comparison) # list of length equal to number of proteins
group_comparison[[1]][[1]] # data used to fit linear model
group_comparison[[1]][[2]] # comparison result
group_comparison[[2]][[3]] # NULL, because we set save_fitted_models to FALSE

Description

Create output of group comparison based on results for individual proteins

Usage

MSstatsGroupComparisonOutput(input, summarization_output, log_base = 2)

Arguments

Value

list, same as the output of 'groupComparison'

Examples

QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
group_comparison_input = MSstatsPrepareForGroupComparison(QuantData)
levels(QuantData$ProteinLevelData$GROUP)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
groups = levels(QuantData$ProteinLevelData$GROUP)
colnames(comparison) <- groups[order(as.numeric(groups))]
samples_info = getSamplesInfo(QuantData)
repeated = checkRepeatedDesign(QuantData)
group_comparison = MSstatsGroupComparison(group_comparison_input, comparison,
                                          FALSE, repeated, samples_info)
group_comparison_final = MSstatsGroupComparisonOutput(group_comparison,
                                                      QuantData)
group_comparison_final[["ComparisonResult"]]

Description

Group comparison for a single protein

Usage

MSstatsGroupComparisonSingleProtein(
  single_protein,
  contrast_matrix,
  repeated,
  groups,
  samples_info,
  save_fitted_models,
  has_imputed
)

Arguments

Examples

QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
group_comparison_input <- MSstatsPrepareForGroupComparison(QuantData)
levels(QuantData$ProteinLevelData$GROUP)
comparison <- matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison) <- "T7-T1"
groups = levels(QuantData$ProteinLevelData$GROUP)
colnames(comparison) <- groups[order(as.numeric(groups))]
samples_info <- getSamplesInfo(QuantData)
repeated <- checkRepeatedDesign(QuantData)
single_output <- MSstatsGroupComparisonSingleProtein(
  group_comparison_input[[1]], comparison, repeated, groups, samples_info,
  FALSE, TRUE)
single_output # same as a single element of MSstatsGroupComparison output

Description

Handle censored missing values

Usage

MSstatsHandleMissing(
  input,
  summary_method,
  impute,
  missing_symbol,
  censored_cutoff
)

Arguments

Value

data.table

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
head(input)

Description

Re-format the data before feature selection

Usage

MSstatsMergeFractions(input)

Arguments

Value

data.table

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
head(input)

Description

Normalize MS data

Usage

MSstatsNormalize(
  input,
  normalization_method,
  peptides_dict = NULL,
  standards = NULL
)

Arguments

Value

data.table

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS") # median normalization
head(input)

Description

Prepare data for processing by 'dataProcess' function

Usage

MSstatsPrepareForDataProcess(input, log_base, fix_missing)

Arguments

Value

data.table

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
head(input)

Description

Prepare output for dataProcess for group comparison

Usage

MSstatsPrepareForGroupComparison(summarization_output)

Arguments

Value

list of run-level data for each protein in the input. This list has a "has_imputed" attribute that indicates if missing values were imputed in the input dataset.

Examples

QuantData <- dataProcess(SRMRawData, use_log_file = FALSE)
group_comparison_input = MSstatsPrepareForGroupComparison(QuantData)
length(group_comparison_input) # list of length equal to number of proteins
# in protein-level data of QuantData
head(group_comparison_input[[1]])

Description

Prepare feature-level data for protein-level summarization

Usage

MSstatsPrepareForSummarization(
  input,
  method,
  impute,
  censored_symbol,
  remove_uninformative_feature_outlier
)

Arguments

Value

data.table

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
head(input)

Description

Feature selection before feature-level data summarization

Usage

MSstatsSelectFeatures(input, method, top_n = 3, min_feature_count = 2)

Arguments

Value

data.table

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input_all = MSstatsSelectFeatures(input, "all") # all features
input_5 = MSstatsSelectFeatures(data.table::copy(input), "topN", top_n = 5) # top 5 features
input_informative = MSstatsSelectFeatures(input, "highQuality") # feature selection

head(input_all)
head(input_5)
head(input_informative)

Description

Post-processing output from MSstats summarization

Usage

MSstatsSummarizationOutput(
  input,
  summarized,
  processed,
  method,
  impute,
  censored_symbol
)

Arguments

Value

list that consists of the following elements:

• FeatureLevelData - feature-level data after processing

• ProteinLevelData - protein-level (summarized) data

• SummaryMethod (string) - name of summarization method that was used

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input = MSstatsSelectFeatures(input, "all")
processed = getProcessed(input)
input = MSstatsPrepareForSummarization(input, method, impute, cens, FALSE)
summarized = MSstatsSummarizeWithSingleCore(input, method, impute, cens, FALSE, TRUE)
output = output = MSstatsSummarizationOutput(input, summarized, processed,
method, impute, cens)

Description

Feature-level data summarization

Usage

MSstatsSummarize(
  proteins_list,
  method,
  impute,
  censored_symbol,
  remove50missing,
  equal_variance
)

Arguments

Value

list of length one with run-level data.

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input = MSstatsSelectFeatures(input, "all")
processed = getProcessed(input)
input = MSstatsPrepareForSummarization(input, method, impute, cens, FALSE)
input_split = split(input, input$PROTEIN)
summarized = MSstatsSummarize(input_split, method, impute, cens, FALSE, TRUE)
length(summarized) # list of summarization outputs for each protein
head(summarized[[1]][[1]]) # run-level summary

Description

Linear model-based summarization for a single protein

Usage

MSstatsSummarizeSingleLinear(single_protein, equal_variances = TRUE)

Arguments

Value

list with protein-level data

Examples

raw = DDARawData 
method = "linear"
cens = NULL
impute = FALSE 
# currently, MSstats only supports MBimpute = FALSE for linear summarization
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input = MSstatsSelectFeatures(input, "all")
input = MSstatsPrepareForSummarization(input, method, impute, cens, FALSE)
input_split = split(input, input$PROTEIN)
single_protein_summary = MSstatsSummarizeSingleLinear(input_split[[1]])
head(single_protein_summary[[1]])

Description

Tukey Median Polish summarization for a single protein

Usage

MSstatsSummarizeSingleTMP(
  single_protein,
  impute,
  censored_symbol,
  remove50missing
)

Arguments

Value

list of two data.tables: one with fitted survival model, the other with protein-level data

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE 
# currently, MSstats only supports MBimpute = FALSE for linear summarization
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input = MSstatsSelectFeatures(input, "all")
input = MSstatsPrepareForSummarization(input, method, impute, cens, FALSE)
input_split = split(input, input$PROTEIN)
single_protein_summary = MSstatsSummarizeSingleTMP(input_split[[1]],
                                                   impute, cens, FALSE)
head(single_protein_summary[[1]])

Description

Feature-level data summarization with multiple cores

Usage

MSstatsSummarizeWithMultipleCores(
  input,
  method,
  impute,
  censored_symbol,
  remove50missing,
  equal_variance,
  numberOfCores = 1
)

Arguments

Value

list of length one with run-level data.

Description

Feature-level data summarization with 1 core

Usage

MSstatsSummarizeWithSingleCore(
  input,
  method,
  impute,
  censored_symbol,
  remove50missing,
  equal_variance
)

Arguments

Value

list of length one with run-level data.

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input = MSstatsSelectFeatures(input, "all")
processed = getProcessed(input)
input = MSstatsPrepareForSummarization(input, method, impute, cens, FALSE)
summarized = MSstatsSummarizeWithSingleCore(input, method, impute, cens, FALSE, TRUE)
length(summarized) # list of summarization outputs for each protein
head(summarized[[1]][[1]]) # run-level summary

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

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)

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

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/annotations/annot_os.csv", 
                    package = "MSstats")
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)

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

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/annotations/annot_pd.csv", package = "MSstats")
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)

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

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)

Description

Model-based quantification for each condition or for each biological sample per protein in a targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment. Quantification takes the processed data set by dataProcess as input and automatically generate the quantification results (data.frame) in a long or matrix format.

Usage

quantification(
  data,
  type = "Sample",
  format = "matrix",
  use_log_file = TRUE,
  append = FALSE,
  verbose = TRUE,
  log_file_path = NULL
)

Arguments

Details

• Sample quantification : individual biological sample quantification for each protein. The label of each biological sample is a combination of the corresponding group and the sample ID. If there are no technical replicates or experimental replicates per sample, sample quantification is the same as run summarization from dataProcess. If there are technical replicates or experimental replicates, sample quantification is median among run quantification corresponding MS runs.

• Group quantification : quantification for individual group or individual condition per protein. It is median among sample quantification.

• The quantification for endogenous samples is based on run summarization from subplot model, with TMP robust estimation.

Value

data.frame as described in details.

Examples

# Consider quantitative data (i.e. QuantData) from a yeast study with ten time points of
# interests, three biological replicates, and no technical replicates which is
# a time-course experiment.
# Sample quantification shows model-based estimation of protein abundance in each biological
# replicate within each time point.
# Group quantification shows model-based estimation of protein abundance in each time point.
QuantData<-dataProcess(SRMRawData, use_log_file = FALSE)
head(QuantData$FeatureLevelData)
# Sample quantification
sampleQuant<-quantification(QuantData, use_log_file = FALSE)
head(sampleQuant)
# Group quantification
groupQuant<-quantification(QuantData, type="Group", use_log_file = FALSE)
head(groupQuant)

Description

Save a plot to pdf file

Usage

savePlot(name_base, file_name, width, height)

Arguments

Description

Takes an SDRF file and outputs an MSstats annotation file. Note the information in the SDRF file must be correctly annotated for MSstats so that MSstats can identify the experimental design. In particular the biological replicates must be correctly annotated, with group comparison experiments having a unique ID for each BioReplicate. For more information on this please see the Supplementary of the most recent MSstats paper

Usage

SDRFtoAnnotation(
  data,
  run_name = "comment[data file]",
  condition_name = "characteristics[disease]",
  biological_replicate = "characteristics[biological replicate]",
  fraction = NULL
)

Arguments

Examples

head(example_SDRF)

msstats_annotation = SDRFtoAnnotation(example_SDRF)

head(msstats_annotation)

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

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)

Description

Import Spectronaut files

Usage

SpectronauttoMSstatsFormat(
  input,
  annotation = NULL,
  intensity = "PeakArea",
  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

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)

Description

This is a partial data set obtained from a published study (Picotti, et. al, 2009). The experiment targeted 45 proteins in the glycolysis/gluconeogenesis/TCA cycle/glyoxylate cycle network, which spans the range of protein abundance from less than 128 to 10E6 copies per cell. Three biological replicates were analyzed at ten time points (T1-T10), while yeasts transited through exponential growth in a glucose-rich medium (T1-T4), diauxic shift (T5-T6), post-diauxic phase (T7-T9), and stationary phase (T10). Prior to trypsinization, the samples were mixed with an equal amount of proteins from the same N15-labeled yeast sample, which was used as a reference. Each sample was profiled in a single mass spectrometry run, where each protein was represented by up to two peptides and each peptide by up to three transitions. The goal of this study is to detect significantly change in protein abundance across time points. Transcriptional activity under the same experimental conditions has been previously investigated by (DeRisi et. al., 1997). Genes coding for 29 of the proteins are differentially expressed between conditions similar to those represented by T7 and T1 and could be treated as external sources to validate the proteomics analysis. In this exampled data set, two of the targeted proteins are selected and validated with gene expression study: Protein IDHC (gene name IDP2) is differentially expressed in time point 1 and time point 7, whereas, Protein PMG2 (gene name GPM2) is not. The protein names are based on Swiss Prot Name.

Usage

SRMRawData

Format

data.frame

Details

The raw data (input data for MSstats) is required to contain variable of ProteinName, PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge, IsotopeLabelType, Condition, BioReplicate, Run, Intensity. The variable names should be fixed.

If the information of one or more columns is not available for the original raw data, please retain the column variables and type in fixed value. For example, the original raw data does not contain the information of ProductCharge, we retain the column ProductCharge and type in NA for all transitions in RawData.

The column BioReplicate should label with unique patient ID (i.e., same patients should label with the same ID).

Variable Intensity is required to be original signal without any log transformation and can be specified as the peak of height or the peak of area under curve.

Value

data.frame with the required format of MSstats.

Author(s)

Meena Choi, Olga Vitek.

Maintainer: Meena Choi ([email protected])

References

Ching-Yun Chang, Paola Picotti, Ruth Huttenhain, Viola Heinzelmann-Schwarz, Marko Jovanovic, Ruedi Aebersold, Olga Vitek. Protein significance analysis in selected reaction monitoring (SRM) measurements. Molecular & Cellular Proteomics, 11:M111.014662, 2012.

Examples

head(SRMRawData)

Description

Theme for MSstats plots

Usage

theme_msstats(
  type,
  x.axis.size = 10,
  y.axis.size = 10,
  legend_size = 13,
  strip_background = element_rect(fill = "gray95"),
  strip_text_x = element_text(colour = c("black"), size = 14),
  legend_position = "top",
  legend_box = "vertical",
  text_angle = 0,
  text_hjust = NULL,
  text_vjust = NULL,
  ...
)

Arguments

Description

Check if annotation matches intended experimental design

Usage

validateAnnotation(msstats_table, design_type = "group comparison")

Arguments

Value

TRUE if annotation file is consistent with intended experimental design. Otherwise, an error is thrown