MSstatsPTM TMT Workflow
Devon Kohler (kohler.d@northeastern.edu)
2021-05-21
Source:vignettes/MSstatsPTM_TMT_Workflow.Rmd
MSstatsPTM_TMT_Workflow.Rmd
library(MSstatsPTM)This Vignette provides an example workflow for how to use the package MSstatsPTM for a TMT dataset.
Installation
To install this package, start R (version “4.0”) and enter:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MSstatsPTM")1. Workflow
1.1 Raw Data Format
Note: We are actively developing dedicated converters for MSstatsPTM. If you have data from a processing tool that does not have a dedicated converter in MSstatsPTM please add a github issue https://github.com/Vitek-Lab/MSstatsPTM/issues and we will add the converter.
The first step is to load in the raw dataset for both the PTM and Protein datasets. Each dataset can formatted using dedicated converters in MSstatsPTM, such as MaxQtoMSstatsPTMFormat, or converters from base MSstatsTMT such as PDtoMSstatsTMTFormat, MaxQtoMSstatsTMTFormat, SpectroMinetoMSstatsTMTFormat, ect. If using converters from MSstatsTMT note they will need to be run both on the global protein and PTM datasets.
Please note for the PTM dataset, both the protein and modification site (or peptide), must be added into the ProteinName column. This allows for the package to summarize to the peptide level, and avoid the off chance there are matching peptides between proteins. For an example of how this can be done please see the code below.
1.1.1 Raw Data Converter
# Run MSstatsPTM converter with modified and unmodified datasets.
raw.input <- MaxQtoMSstatsPTMFormat(raw_ptm_df, annotation, evidence_file,
proteinGroupsfile)The output of the converter is a list with two formatted data.tables. One each for the PTM and Protein datasets.
Given there is not a dedicated MSstatsPTM converter for the processing tool, base MSstats converters can be used as follows. Please note ProteinName column must be a combination of the Protein Name and sitename.
# Add site into ProteinName column
raw_ptm_df$ProteinName <- paste(raw_ptm_df$ProteinName,
raw_ptm_df$Site, sep = "_")
# Run MSstats Converters
PTM.data <- MaxQtoMSstatsTMTFormat(raw_ptm_df)
PROTEIN.data <- MaxQtoMSstatsTMTFormat(raw_protein_df)
# Combine into one list
raw.input <- list(PTM = PTM.data,
PROTEIN = PROTEIN.data)Both of these conversion methods will output the same results.
head(raw.input.tmt$PTM)
#> ProteinName PeptideSequence Charge PSM Mixture TechRepMixture
#> 1 Protein_12_S703 Peptide491 3 Peptide_491_3 1 1
#> 2 Protein_12_S703 Peptide491 3 Peptide_491_3 1 1
#> 3 Protein_12_S703 Peptide491 3 Peptide_491_3 1 1
#> 4 Protein_12_S703 Peptide491 3 Peptide_491_3 1 1
#> 5 Protein_12_S703 Peptide491 3 Peptide_491_3 1 1
#> 6 Protein_12_S703 Peptide491 3 Peptide_491_3 1 1
#> Run Channel Condition BioReplicate Intensity
#> 1 1_1 128N Condition_2 Condition_2_1 48030.0
#> 2 1_1 129C Condition_4 Condition_4_2 100224.4
#> 3 1_1 131C Condition_3 Condition_3_2 66804.6
#> 4 1_1 130N Condition_1 Condition_1_2 46779.8
#> 5 1_1 128C Condition_6 Condition_6_1 77497.9
#> 6 1_1 126C Condition_4 Condition_4_1 81559.7
head(raw.input.tmt$PROTEIN)
#> ProteinName PeptideSequence Charge PSM Mixture TechRepMixture Run
#> 1 Protein_12 Peptide9121 3 Peptide_9121_3 1 1 1_1
#> 2 Protein_12 Peptide27963 5 Peptide_27963_5 1 1 1_1
#> 3 Protein_12 Peptide28482 4 Peptide_28482_4 1 1 1_1
#> 4 Protein_12 Peptide10940 2 Peptide_10940_2 2 1 2_1
#> 5 Protein_12 Peptide4900 2 Peptide_4900_2 2 1 2_1
#> 6 Protein_12 Peptide4900 3 Peptide_4900_3 2 1 2_1
#> Channel Condition BioReplicate Intensity
#> 1 126C Condition_4 Condition_4_1 10996116.9
#> 2 127C Condition_5 Condition_5_1 56965.1
#> 3 131N Condition_2 Condition_2_2 286121.7
#> 4 131N Condition_2 Condition_2_4 534806.0
#> 5 126C Condition_4 Condition_4_3 1134908.7
#> 6 126C Condition_4 Condition_4_3 1605773.21.2 Summarization - dataSummarizationPTM_TMT
After loading in the input data, the next step is to use the dataSummarizationPTM_TMT function This provides the summarized dataset needed to model the protein/PTM abundance. The function will summarize the Protein dataset up to the protein level and will summarize the PTM dataset up to the peptide level. There are multiple options for normalization and missing value imputation. These options should be reviewed in the package documentation.
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head(MSstatsPTM.summary$PTM$ProteinLevelData)
#> Mixture TechRepMixture Run Channel Protein Abundance BioReplicate
#> 1 1 1 1_1 126C Protein_1076_Y67 13.53222 Condition_4_1
#> 2 1 1 1_1 126C Protein_1145_T915 12.05299 Condition_4_1
#> 3 1 1 1_1 126C Protein_1146_S328 14.43190 Condition_4_1
#> 4 1 1 1_1 126C Protein_1160_S188 16.17976 Condition_4_1
#> 5 1 1 1_1 126C Protein_12_S703 16.35842 Condition_4_1
#> 6 1 1 1_1 126C Protein_1220_Y321 15.69534 Condition_4_1
#> Condition
#> 1 Condition_4
#> 2 Condition_4
#> 3 Condition_4
#> 4 Condition_4
#> 5 Condition_4
#> 6 Condition_4
head(MSstatsPTM.summary$PROTEIN$ProteinLevelData)
#> Mixture TechRepMixture Run Channel Protein Abundance BioReplicate
#> 1 1 1 1_1 126C Protein_1076 18.59474 Condition_4_1
#> 2 1 1 1_1 126C Protein_1145 14.38146 Condition_4_1
#> 3 1 1 1_1 126C Protein_1146 18.74102 Condition_4_1
#> 4 1 1 1_1 126C Protein_1160 17.92639 Condition_4_1
#> 5 1 1 1_1 126C Protein_12 18.08824 Condition_4_1
#> 6 1 1 1_1 126C Protein_1220 17.51226 Condition_4_1
#> Condition
#> 1 Condition_4
#> 2 Condition_4
#> 3 Condition_4
#> 4 Condition_4
#> 5 Condition_4
#> 6 Condition_4The summarize function returns a list with PTM and Protein summarization information.
1.2.1 QCPlot
Once summarized, MSstatsPTM provides multiple plots to analyze the experiment. Here we show the quality control boxplot. The first plot shows the modified data and the second plot shows the global protein dataset.
dataProcessPlotsPTM(MSstatsPTM.summary,
type = 'QCPLOT',
which.PTM = "allonly",
address = FALSE)
1.2.2 Profile Plot
Here we show a profile plot. Again the top plot shows the modified peptide, and the bottom shows the overall protein.
dataProcessPlotsPTM(MSstatsPTM.summary,
type = 'PROFILEPLOT',
which.Protein = c("Protein_12"),
address = FALSE)
1.3 Modeling - groupComparisonPTM
After summarization, the summarized datasets can be modeled using the groupComparisonPTM function. This function will model the PTM and Protein summarized datasets, and then adjust the PTM model for changes in overall protein abundance. The output of the function is a list containing these three models named: PTM.Model, PROTEIN.Model, ADJUSTED.Model.
# Specify contrast matrix
comparison <- matrix(c(1,0,0,-1,0,0,
0,1,0,0,-1,0,
0,0,1,0,0,-1,
1,0,-1,0,0,0,
0,1,-1,0,0,0,
0,0,0,1,0,-1,
0,0,0,0,1,-1),nrow=7, ncol=6, byrow=TRUE)
# Set the names of each row
row.names(comparison)<-c('1-4', '2-5', '3-6', '1-3',
'2-3', '4-6', '5-6')
colnames(comparison) <- c('Condition_1','Condition_2','Condition_3',
'Condition_4','Condition_5','Condition_6')
MSstatsPTM.model <- groupComparisonPTM(MSstatsPTM.summary,
data.type = "TMT",
contrast.matrix = comparison)
#> INFO [2021-05-21 14:19:52] Model fitting for 90 proteins.
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#> INFO [2021-05-21 14:19:53] Testing for 90 proteins:
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#> INFO [2021-05-21 14:19:54] Model fitting for 85 proteins.
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#> INFO [2021-05-21 14:19:57] Testing for 85 proteins:
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head(MSstatsPTM.model$PTM.Model)
#> Protein Label log2FC SE DF pvalue
#> 1: Protein_1076_Y67 1-4 0.11835636 0.05264970 15.00000 4.004178e-02
#> 2: Protein_1076_Y67 2-5 0.24162457 0.05264970 15.00000 3.544345e-04
#> 3: Protein_1076_Y67 3-6 -0.27968805 0.06173696 15.00028 3.984772e-04
#> 4: Protein_1076_Y67 1-3 0.42262536 0.05712141 15.00008 2.223242e-06
#> 5: Protein_1076_Y67 2-3 0.37549462 0.05712141 15.00008 8.829044e-06
#> 6: Protein_1076_Y67 4-6 0.02458095 0.05712141 15.00008 6.730757e-01
#> adj.pvalue issue
#> 1: 0.0493665763 NA
#> 2: 0.0020224378 NA
#> 3: 0.0071725900 NA
#> 4: 0.0001000459 NA
#> 5: 0.0003973070 NA
#> 6: 0.7530134974 NA
head(MSstatsPTM.model$PROTEIN.Model)
#> Protein Label log2FC SE DF pvalue adj.pvalue issue
#> 1: Protein_1076 1-4 0.1511817 0.1459011 5 0.347602256 0.41614355 NA
#> 2: Protein_1076 2-5 0.3559027 0.1459011 5 0.058701203 0.09494067 NA
#> 3: Protein_1076 3-6 0.3195065 0.1786916 5 0.133803423 0.24198491 NA
#> 4: Protein_1076 1-3 0.4304476 0.1459011 5 0.031875419 0.07322731 NA
#> 5: Protein_1076 2-3 0.6261724 0.1459011 5 0.007776053 0.03478761 NA
#> 6: Protein_1076 4-6 0.5987725 0.1786916 5 0.020309479 0.05754353 NA
head(MSstatsPTM.model$ADJUSTED.Model)
#> Protein Label log2FC SE Tvalue DF
#> 1: Protein_1076_Y67 1-4 -0.03282532 0.15511001 -0.2116261 6.351078
#> 2: Protein_1145_T915 1-4 -1.26808260 0.24799278 -5.1133852 14.829339
#> 3: Protein_1146_S328 1-4 0.03359191 0.17861660 0.1880671 19.996633
#> 4: Protein_1160_S188 1-4 -0.24643910 0.14565603 -1.6919252 17.822868
#> 5: Protein_12_S703 1-4 -0.24745688 0.10078246 -2.4553567 10.599996
#> 6: Protein_1220_Y321 1-4 -0.33005855 0.08668981 -3.8073512 24.562266
#> pvalue adj.pvalue GlobalProtein
#> 1: 0.8390276607 0.868296067 Protein_1076
#> 2: 0.0001318386 0.000879508 Protein_1145
#> 3: 0.8527193852 0.872322130 Protein_1146
#> 4: 0.1080693549 0.152669406 Protein_1160
#> 5: 0.0326935599 0.054900506 Protein_12
#> 6: 0.0008302273 0.003694511 Protein_12201.3.1 Volcano Plot
The models from the groupComparisonPTM function can be used in the model visualization function, groupComparisonPlotsPTM. Here we show Volcano Plots for the models.
groupComparisonPlotsPTM(data = MSstatsPTM.model,
type = "VolcanoPlot",
which.Comparison = c('1-4'),
which.PTM = 1:50,
address=FALSE)
1.3.2 Heatmap Plot
Here we show a Heatmap for the models.
groupComparisonPlotsPTM(data = MSstatsPTM.model,
type = "Heatmap",
which.PTM = 1:49,
address=FALSE)
