MSstatsTMT
Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling

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MSstatsTMT : A package for protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling

MSstatsTMT : A package for protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling
In MSstatsTMT: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling 

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(MSstatsTMT)

This vignette summarizes the introduction and various options of all functionalities in MSstatsTMT.

MSstatsTMT includes the following three steps for statistical testing:

  1. Converters for different peptide quantification tools to get the input with required format: PDtoMSstatsTMTFormat, MaxQtoMSstatsTMTFormat, SpectroMinetoMSstatsTMTFormat and OpenMStoMSstatsTMTFormat.
  2. Protein summarization based on peptide quantification data: proteinSummarization
  3. Group comparison on protein quantification data: groupComparisonTMT

1. Converters for different peptide quantification tools

PDtoMSstatsTMTFormat()

Preprocess PSM data from Proteome Discoverer and convert into the required input format for MSstatsTMT.

Arguments

Example

# read in PD PSM sheet
# raw.pd <- read.delim("161117_SILAC_HeLa_UPS1_TMT10_5Mixtures_3TechRep_UPSdB_Multiconsensus_PD22_Intensity_PSMs.txt")
head(raw.pd)

# Read in annotation including condition and biological replicates per run and channel.
# Users should make this annotation file. It is not the output from Proteome Discoverer.
# annotation.pd <- read.csv(file="PD_Annotation.csv", header=TRUE)
head(annotation.pd)

# do not remove PSM with missing values within one run
input.pd <- PDtoMSstatsTMTFormat(raw.pd, annotation.pd)
head(input.pd)

# remove PSM with missing values within one run
input.pd.no.miss <- PDtoMSstatsTMTFormat(raw.pd, annotation.pd,
                                 rmPSM_withMissing_withinRun = TRUE)
head(input.pd.no.miss)

MaxQtoMSstatsTMTFormat()

Preprocess PSM-level data from MaxQuant and convert into the required input format for MSstatsTMT.

Arguments

Example

# Read in MaxQuant files
# proteinGroups <- read.table("proteinGroups.txt", sep="\t", header=TRUE)

# evidence <- read.table("evidence.txt", sep="\t", header=TRUE)

# Users should make this annotation file. It is not the output from MaxQuant.
# annotation.mq <- read.csv(file="MQ_Annotation.csv", header=TRUE)

input.mq <- MaxQtoMSstatsTMTFormat(evidence, proteinGroups, annotation.mq)
head(input.mq)

SpectroMinetoMSstatsTMTFormat()

Preprocess PSM data from SpectroMine and convert into the required input format for MSstatsTMT.

Arguments

Example

# Read in SpectroMine PSM report
# raw.mine <- read.csv('20180831_095547_CID-OT-MS3-Short_PSM Report_20180831_103118.xls', sep="\t")

# Users should make this annotation file. It is not the output from SpectroMine
# annotation.mine <- read.csv(file="Mine_Annotation.csv", header=TRUE)

input.mine <- SpectroMinetoMSstatsTMTFormat(raw.mine, annotation.mine)
head(input.mine)

OpenMStoMSstatsTMTFormat()

Preprocess MSstatsTMT report from OpenMS and convert into the required input format for MSstatsTMT.

Arguments

Example

# read in MSstatsTMT report from OpenMS
# raw.om <- read.csv("OpenMS_20200222/20200225_MSstatsTMT_OpenMS_Export.csv")
head(raw.om)

# the function only requries one input file
input.om <- OpenMStoMSstatsTMTFormat(raw.om)
head(input.om)

2. Protein summarization, normalization and visualization

2.1. proteinSummarization()

Global median normalization is first applied to peptide level quantification data (equalizing the medians across all the channels and MS runs). Protein-level summarization from peptide level quantification should be performed before testing differentially abundant proteins. Then, normalization between MS runs using normalization channels will be implemented. In particular, protein summarization method MSstats assumes missing values are censored and then imputes the missing values before summarizing peptide level data into protein level data. Other methods, including MedianPolish, Median and LogSum, do not impute missing values.

Arguments

Example

# use MSstats for protein summarization
quant.msstats <- proteinSummarization(input.pd,
                                      method="msstats",
                                      global_norm=TRUE,
                                      reference_norm=TRUE,
                                      remove_norm_channel = TRUE,
                                      remove_empty_channel = TRUE)
head(quant.msstats)

# use Median for protein summarization
# since median method doesn't impute missing values, 
# we need to use the input data without missing values
quant.median <- proteinSummarization(input.pd.no.miss,
                                     method="Median",
                                     global_norm=TRUE,
                                     reference_norm=TRUE,
                                     remove_norm_channel = TRUE,
                                     remove_empty_channel = TRUE)

head(quant.median)

2.2 dataProcessPlotsTMT()

Visualization for explanatory data analysis. To illustrate the quantitative data after data-preprocessing and quality control of TMT runs, dataProcessPlotsTMT takes the quantitative data from converter functions (PDtoMSstatsTMTFormat, MQtoMSstatsTMTFormat and SpectroMinetoMSstatsTMTFormat) and summarized data from function proteinSummarization as input. It generates two 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.

Arguments

Example

## Profile plot without norm channnels and empty channels
dataProcessPlotsTMT(data.peptide = input.pd,
                     data.summarization = quant.msstats,
                     type = 'ProfilePlot',
                     width = 21, # adjust the figure width since there are 15 TMT runs.
                     height = 7)

# ## Profile plot with all the channels
# quant.msstats.all <- proteinSummarization(input.pd,
#                                       method="msstats",
#                                       normalization=TRUE,
#                                       remove_norm_channel=FALSE,
#                                       remove_empty_channel=FALSE)
# 
# dataProcessPlotsTMT(data.peptide = input.pd,
#                      data.summarization = quant.msstats.all,
#                      type = 'ProfilePlot',
#                      width = 21, # adjust the figure width since there are 15 TMT runs.
#                      height = 7)

## Quality control plot 
# dataProcessPlotsTMT(data.peptide=input.pd,
                     # data.summarization=quant.msstats, 
                     # type='QCPlot',
                     # width = 21, # adjust the figure width since there are 15 TMT runs. 
                     # height = 7)

3. groupComparisonTMT()

Tests for significant changes in protein abundance across conditions based on a family of linear mixed-effects models in TMT experiment. Experimental design of case-control study (patients are not repeatedly measured) is automatically determined based on proper statistical model.

Arguments

Example

# test for all the possible pairs of conditions
test.pairwise <- groupComparisonTMT(quant.msstats)
head(test.pairwise)

# Check the conditions in the protein data
levels(quant.msstats$Condition)
# Only compare condition 0.125 and 1
comparison<-matrix(c(-1,0,0,1),nrow=1)
# Set the names of each row
row.names(comparison)<-"1-0.125"
# Set the column names
colnames(comparison)<- c("0.125", "0.5", "0.667", "1")
comparison

test.contrast <- groupComparisonTMT(data = quant.msstats, contrast.matrix = comparison)
head(test.contrast)


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MSstatsTMT documentation built on Dec. 20, 2020, 2:01 a.m.

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