MSstatsPTM TMT Workflow

Devon Kohler (kohler.d@northeastern.edu)

2021-11-08

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

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.

Both of these conversion methods will output the same results.

1.2 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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The 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.

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.

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  [2021-11-08 17:56:38] Design: 2 mixtures.
#> INFO  [2021-11-08 17:56:38] Design: 1 MS run per mixture.
#> INFO  [2021-11-08 17:56:38] Design: group comparison design (Different conditions contains different biological subjects).
#> INFO  [2021-11-08 17:56:38] Model fitting for 90 proteins.
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#> INFO  [2021-11-08 17:56:40] Testing for 90 proteins:
#> 
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#> INFO  [2021-11-08 17:56:40] Design: 2 mixtures.
#> INFO  [2021-11-08 17:56:40] Design: 1 MS run per mixture.
#> INFO  [2021-11-08 17:56:40] Design: group comparison design (Different conditions contains different biological subjects).
#> INFO  [2021-11-08 17:56:40] Model fitting for 85 proteins.
#> 
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#> INFO  [2021-11-08 17:56:45] Testing for 85 proteins:
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head(MSstatsPTM.model$PTM.Model)
#>             Protein Label      log2FC         SE       DF       pvalue
#> 1: Protein_1076_Y67   1-4  0.11835636 0.05264970 15.00000 4.004178e-02
#> 2: Protein_1076_Y67   2-5  0.24162457 0.05264970 15.00000 3.544345e-04
#> 3: Protein_1076_Y67   3-6 -0.27968805 0.06173696 15.00028 3.984772e-04
#> 4: Protein_1076_Y67   1-3  0.42262536 0.05712141 15.00008 2.223242e-06
#> 5: Protein_1076_Y67   2-3  0.37549462 0.05712141 15.00008 8.829044e-06
#> 6: Protein_1076_Y67   4-6  0.02458095 0.05712141 15.00008 6.730757e-01
#>      adj.pvalue issue
#> 1: 0.0493665762    NA
#> 2: 0.0020224378    NA
#> 3: 0.0071725899    NA
#> 4: 0.0001000459    NA
#> 5: 0.0003973070    NA
#> 6: 0.7530134974    NA
head(MSstatsPTM.model$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_1220

1.3.1 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.

1.3.2 Heatmap Plot

Here we show a Heatmap for the models.