MSstatsPTM TMT Workflow

Devon Kohler (kohler.d@northeastern.edu)

2024-09-25

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.

We go in depth on all converters included in this package in the MSstatsPTM_LabelFree_Workflow. For more information about data conversion please review the relevant sections there.

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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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_1220_Y321  15.69534 Condition_4_1
#> 6       1              1 1_1    126C Protein_1235_S416  16.00939 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_4

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.


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,
                                       use_log_file = FALSE, append = FALSE)
#> INFO  [2024-09-25 19:16:40] Design: 2 mixtures.
#> INFO  [2024-09-25 19:16:40] Design: 1 MS run per mixture.
#> INFO  [2024-09-25 19:16:40] Design: group comparison design (Different conditions contains different biological subjects).
#> INFO  [2024-09-25 19:16:40] Model fitting for 90 proteins.
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#> INFO  [2024-09-25 19:16:42] Testing for 90 proteins:
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#> INFO  [2024-09-25 19:16:42] Design: 2 mixtures.
#> INFO  [2024-09-25 19:16:42] Design: 1 MS run per mixture.
#> INFO  [2024-09-25 19:16:42] Design: group comparison design (Different conditions contains different biological subjects).
#> INFO  [2024-09-25 19:16:42] Model fitting for 85 proteins.
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#> INFO  [2024-09-25 19:16:46] Testing for 85 proteins:
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head(MSstatsPTM.model$PTM.Model)
#>             Protein  Label      log2FC         SE       DF       pvalue
#>              <fctr> <char>       <num>      <num>    <num>        <num>
#> 1: Protein_1076_Y67    1-4  0.11835636 0.05264970 15.00000 4.004178e-02
#> 2: Protein_1076_Y67    2-5  0.24162457 0.05264970 15.00000 3.544345e-04
#> 3: Protein_1076_Y67    3-6 -0.27968805 0.06173696 15.00028 3.984772e-04
#> 4: Protein_1076_Y67    1-3  0.42262536 0.05712141 15.00008 2.223242e-06
#> 5: Protein_1076_Y67    2-3  0.37549462 0.05712141 15.00008 8.829044e-06
#> 6: Protein_1076_Y67    4-6  0.02458095 0.05712141 15.00008 6.730757e-01
#>      adj.pvalue  issue
#>           <num> <lgcl>
#> 1: 0.0493665762     NA
#> 2: 0.0020224378     NA
#> 3: 0.0071725899     NA
#> 4: 0.0001000459     NA
#> 5: 0.0003973070     NA
#> 6: 0.7530134974     NA
head(MSstatsPTM.model$PROTEIN.Model)
#>         Protein  Label    log2FC        SE    DF      pvalue adj.pvalue  issue
#>          <fctr> <char>     <num>     <num> <num>       <num>      <num> <lgcl>
#> 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
#>               <fctr> <char>       <num>      <num>      <num>     <num>
#> 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 19.996632
#> 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 Adjusted
#>           <num>       <num>        <char>   <lgcl>
#> 1: 0.8390276607 0.868296067  Protein_1076     TRUE
#> 2: 0.0001318386 0.000879508  Protein_1145     TRUE
#> 3: 0.8527193854 0.872322130  Protein_1146     TRUE
#> 4: 0.1080693549 0.152669406  Protein_1160     TRUE
#> 5: 0.0326935599 0.054900506    Protein_12     TRUE
#> 6: 0.0008302273 0.003694511  Protein_1220     TRUE

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.

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)