MSstatsPTM LabelFree 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 labelfree dataset. It also provides examples and an analysis of how adjusting for global protein levels allows for better interpretations of PTM modeling results.

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 ProgenesistoMSstatsPTMFormat, or converters from base MSstats such as SkylinetoMSstatsFormat, MaxQtoMSstatsFormat, ProgenesistoMSstatsFormat, ect. If using converters from MSstats 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.

If there is not a dedicated MSstatsPTM converter for a 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

After loading in the input data, the next step is to use the dataSummarizationPTM 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.


MSstatsPTM.summary <- dataSummarizationPTM(raw.input, verbose = FALSE)
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head(MSstatsPTM.summary$PTM$ProteinLevelData)
#>   RUN     Protein LogIntensities originalRUN GROUP SUBJECT
#> 1   3 Q9UHD8_K028       20.40683   CCCP-B2T1  CCCP    BCH2
#> 2   4 Q9UHD8_K028       20.42412   CCCP-B2T2  CCCP    BCH2
#> 3   7 Q9UHD8_K028       20.62455  Combo-B2T1 Combo    BCH2
#> 4   8 Q9UHD8_K028       20.72569  Combo-B2T2 Combo    BCH2
#> 5  11 Q9UHD8_K028       20.40666   Ctrl-B2T1  Ctrl    BCH2
#> 6  12 Q9UHD8_K028       20.65381   Ctrl-B2T2  Ctrl    BCH2
#>   TotalGroupMeasurements NumMeasuredFeature MissingPercentage more50missing
#> 1                      4                  1                 0         FALSE
#> 2                      4                  1                 0         FALSE
#> 3                      4                  1                 0         FALSE
#> 4                      4                  1                 0         FALSE
#> 5                      4                  1                 0         FALSE
#> 6                      4                  1                 0         FALSE
#>   NumImputedFeature
#> 1                 0
#> 2                 0
#> 3                 0
#> 4                 0
#> 5                 0
#> 6                 0
head(MSstatsPTM.summary$PROTEIN$ProteinLevelData)
#>   RUN Protein LogIntensities   originalRUN    GROUP SUBJECT
#> 1   3  Q9UHD8       19.36883     CCCP-B2T1     CCCP    BCH2
#> 2   4  Q9UHD8       19.56289     CCCP-B2T2     CCCP    BCH2
#> 3   7  Q9UHD8       18.69612    Combo-B2T1    Combo    BCH2
#> 4  11  Q9UHD8       19.77119     Ctrl-B2T1     Ctrl    BCH2
#> 5  12  Q9UHD8       19.62490     Ctrl-B2T2     Ctrl    BCH2
#> 6  15  Q9UHD8       19.16970 USP30_OE-B2T1 USP30_OE    BCH2
#>   TotalGroupMeasurements NumMeasuredFeature MissingPercentage more50missing
#> 1                      4                  1                 0         FALSE
#> 2                      4                  1                 0         FALSE
#> 3                      4                  1                 0         FALSE
#> 4                      4                  1                 0         FALSE
#> 5                      4                  1                 0         FALSE
#> 6                      4                  1                 0         FALSE
#>   NumImputedFeature
#> 1                 0
#> 2                 0
#> 3                 0
#> 4                 0
#> 5                 0
#> 6                 0

The summarize function returns a list with PTM and Protein summarization information. Each PTM and Protein include a list of data.tables: FeatureLevelData is a data.table of reformatted input of dataSummarizationPTM, ProteinLevelData is the run level summarization data.

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(-1,0,1,0),nrow=1)
row.names(comparison) <- "CCCP-Ctrl"
colnames(comparison) <- c("CCCP", "Combo", "Ctrl", "USP30_OE")

MSstatsPTM.model <- groupComparisonPTM(MSstatsPTM.summary, 
                                       data.type = "LabelFree",
                                       contrast.matrix = comparison,
                                       verbose = FALSE)
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head(MSstatsPTM.model$PTM.Model)
#>        Protein     Label    log2FC         SE    Tvalue DF    pvalue adj.pvalue
#> 1: Q9UHD8_K028 CCCP-Ctrl 0.1147642 0.09463998 1.2126393  4 0.2919872  0.4201767
#> 2: Q9UHD8_K069 CCCP-Ctrl 0.2688399 0.41750153 0.6439256  8 0.5376428  0.6473658
#> 3: Q9UHD8_K141 CCCP-Ctrl 0.7141059 1.15951976 0.6158635  3 0.5815577  0.6642347
#> 4: Q9UHD8_K262 CCCP-Ctrl 0.3076673 0.41648528 0.7387232  8 0.4811835  0.5976805
#> 5: Q9UHQ9_K046 CCCP-Ctrl 1.0516086 0.63193681 1.6641040  4 0.1714238  0.2889715
#> 6: Q9UHQ9_K062 CCCP-Ctrl 7.4586281 3.91369471 1.9057767  4 0.1293742  0.2336522
#>    issue MissingPercentage ImputationPercentage
#> 1:  <NA>               0.5                    0
#> 2:  <NA>               0.0                    0
#> 3:  <NA>               0.5                    0
#> 4:  <NA>               0.0                    0
#> 5:  <NA>               0.5                    0
#> 6:  <NA>               0.5                    0
head(MSstatsPTM.model$PROTEIN.Model)
#>    Protein     Label     log2FC        SE     Tvalue DF      pvalue adj.pvalue
#> 1:  Q9UHD8 CCCP-Ctrl  0.2321867 0.3054474  0.7601529  3 0.502444586 0.67065761
#> 2:  Q9UHQ9 CCCP-Ctrl -0.1543455 0.1532654 -1.0070472  4 0.370886065 0.64286918
#> 3:  Q9UIA9 CCCP-Ctrl  0.1738736 0.1096855  1.5852005  9 0.147381886 0.33080672
#> 4:  Q9UIF8 CCCP-Ctrl  1.1429060 0.2462052  4.6420872  4 0.009718807 0.06317225
#> 5:  Q9UL25 CCCP-Ctrl -2.0671120 0.2668733 -7.7456678  3 0.004475377 0.03878660
#> 6:  Q9UM54 CCCP-Ctrl -0.3602191 0.4761387 -0.7565424  8 0.471013931 0.67065761
#>    issue MissingPercentage ImputationPercentage
#> 1:    NA         0.5000000            0.0000000
#> 2:    NA         0.5000000            0.0000000
#> 3:    NA         0.2500000            0.2500000
#> 4:    NA         0.5000000            0.0000000
#> 5:    NA         0.5000000            0.0000000
#> 6:    NA         0.3333333            0.3333333
head(MSstatsPTM.model$ADJUSTED.Model)
#>        Protein     Label      log2FC        SE      Tvalue        DF    pvalue
#> 1: Q9UHD8_K028 CCCP-Ctrl -0.11742259 0.3197731 -0.36720591  3.578917 0.7341316
#> 2: Q9UHD8_K069 CCCP-Ctrl  0.03665317 0.5173062  0.07085392 10.689428 0.9448222
#> 3: Q9UHD8_K141 CCCP-Ctrl  0.48191914 1.1990764  0.40190862  3.414364 0.7116107
#> 4: Q9UHD8_K262 CCCP-Ctrl  0.07548059 0.5164863  0.14614248 10.680564 0.8865306
#> 5: Q9UHQ9_K046 CCCP-Ctrl  1.20595408 0.6502572  1.85458014  4.468955 0.1297166
#> 6: Q9UHQ9_K062 CCCP-Ctrl  7.61297362 3.9166946  1.94372408  4.012269 0.1236293
#>    adj.pvalue GlobalProtein
#> 1:  0.8250241        Q9UHD8
#> 2:  0.9694697        Q9UHD8
#> 3:  0.8074044        Q9UHD8
#> 4:  0.9176370        Q9UHD8
#> 5:  0.2319176        Q9UHQ9
#> 6:  0.2244347        Q9UHQ9

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.