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  1605773.2

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_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_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)
#> 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_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.

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)