# Proteomics Data Import

Constantin Ahlmann-Eltze

#### 26 April 2023

# Contents

# 1 Introduction

After running your samples on a mass spectrometer, you want to find out if there are interesting patterns in the data. But the first challenge is how do you get the data from the files that your mass spectrometer produced into R?

In the following, I will describe several ways of importing data from MaxQuant. The general approaches will also be applicable to data from other tools, you will just have to adapt the column names.

## 1.1 MaxQuant File Overview

MaxQuant is a popular tool for identifying, integrating, and combining MS peaks to derive peptide and protein intensities. MaxQuant produces several output files including proteinGroups.txt. It is usually a tab separated table with a lot of different columns, which can make it difficult to not get overwhelmed with information.

The most important columns that every proteinGroups.txt file contains are

- Protein IDs: a semicolon delimited text listing all protein identifiers that match an identified set of peptides. Most of the time this is just a single protein, but sometimes proteins are so similar to each other because of gene duplication that it was not possible to distinguish them.

- Majority protein IDs: a semicolon delimited text that lists all proteins from the Protein IDs column which had more than half of their peptides identified.

- Identification type [SAMPLENAME]: For each sample there is one column that explains how the peptide peaks where identified. Either they were directly sequenced by the MS2 (“By MS/MS”) or by matching the m/z peak and elution timing across samples (“By matching”).

- Intensity [SAMPLENAME]: The combined intensity of the peptides of the protein. Missing or non-identified proteins are simply stored as `0`. In a label-free experiment, this is also often called LFQ Intensity [SAMPLENAME].

- iBAQ [SAMPLENAME]: iBAQ is short for intensity-based absolute quantification. It is an attempt to make intensity values comparable across proteins. Usually the intensity values are only relative, which means that they are only comparable within one protein. This is because differences in ionization and detection efficiency. It is usually better to just compare the Intensity columns to identify differentially abundant proteins.

- Only identified by site: Contains a “+” if the protein was only identified by a modification site.

- Reverse: Contains a “+” if the protein matches the reversed part of the decoy database.

- Contaminant: Contains a “+” if the protein is a commonly occurring contaminant.

The last three columns are commonly used to filter out false positive hits.

The full information what each column means is provided in the tables.pdf file in the MaxQuant output folder.

# 2 Workflow

Our goal is to turn this complicated table into a useable matrix or a `SummarizedExperiment` object. There are several ways to achieve this:

1. Use the base R functions (`read.delim()` and `[<-`) to read in the data
2. Use the `tidyverse` packages to load the file and turn it into a useable object
3. Use the `DEP` package and the `import_MaxQuant()` function

I will demonstrate each approach using an example file that comes with this package.

The example file contains the LFQ data from a BioID experiment in Drosophila melanogaster. 11 different Palmitoyltransferases (short DHHC) were tagged with a promiscuous biotin ligase and all biotinylated proteins were enriched and identified using label-free mass spectrometry. The conditions are named after the tagged DHHC and the negative control condition is called S2R for the cell line. Each condition was measured in triplicates, which means that there are a total of 36 samples To make the file smaller, I provide a reduced data set which only contains the first 122 rows of the data.

# 3 Base R

The example file is located in

    system.file("extdata/proteinGroups.txt",
                package = "proDA", mustWork = TRUE)
    #> [1] "F:/biocbuild/bbs-3.17-bioc/tmpdir/RtmpaMMY1s/Rinst32cc306d717e/proDA/extdata/proteinGroups.txt"

In this specific file, all spaces have been replaced with dots. This is an example how each output file from MaxQuant slightly differs. This can make it difficult to write a generic import function. Instead I will first demonstrate the most general approach which is to simply use the base R tools for loading the data and turning it into useful objects.

The first step is to load the full table.

    full_data <- read.delim(
        system.file("extdata/proteinGroups.txt",
                    package = "proDA", mustWork = TRUE),
        stringsAsFactors = FALSE
    )

    head(colnames(full_data))
    #> [1] "Protein.IDs"                   "Majority.protein.IDs"          "Peptide.counts..all."
    #> [4] "Peptide.counts..razor.unique." "Peptide.counts..unique."       "Protein.names"

Next, I create a matrix of the intensity data, where each sample is a column and each protein group is a row.

    # I use a regular expression (regex) to select the intensity columns
    intensity_colnames <- grep("^LFQ\\.intensity\\.", colnames(full_data), value=TRUE)

    # Create matrix which only contains the intensity columns
    data <- as.matrix(full_data[, intensity_colnames])
    colnames(data) <- sub("^LFQ\\.intensity\\.", "", intensity_colnames)
    # Code missing values explicitly as NA
    data[data == 0] <- NA
    # log transformation to account for mean-variance relation
    data <- log2(data)
    # Overview of data
    data[1:7, 1:6]
    #>      CG1407.01 CG1407.02 CG1407.03 CG4676.01 CG4676.02 CG4676.03
    #> [1,]        NA        NA        NA        NA        NA        NA
    #> [2,]        NA        NA        NA        NA        NA        NA
    #> [3,]        NA        NA        NA        NA  20.20120        NA
    #> [4,]        NA  18.87622  18.90683        NA  18.77520        NA
    #> [5,]  20.98961  20.40302  19.78941        NA  20.22682        NA
    #> [6,]        NA        NA        NA        NA  19.25836        NA
    #> [7,]        NA        NA        NA        NA        NA        NA
    # Set rownames after showing data, because they are so long
    rownames(data) <- full_data$Protein.IDs

In the next step I will create an annotation `data.frame` that contains information on the sample name, the condition and the replicate.

    annotation_df <- data.frame(
        Condition = sub("\\.\\d+", "", sub("^LFQ\\.intensity\\.",
                                           "", intensity_colnames)),
        Replicate = as.numeric(sub("^LFQ\\.intensity\\.[[:alnum:]]+\\.",
                                   "", intensity_colnames)),
        stringsAsFactors = FALSE, row.names = colnames(data)
    )
    head(annotation_df)
    #>           Condition Replicate
    #> CG1407.01    CG1407         1
    #> CG1407.02    CG1407         2
    #> CG1407.03    CG1407         3
    #> CG4676.01    CG4676         1
    #> CG4676.02    CG4676         2
    #> CG4676.03    CG4676         3

We can use this data to fit the probabilistic dropout model and test for differentially abundant proteins.

    # Not Run
    library(proDA)
    fit <- proDA(data, design= annotation_df$Condition, col_data = annotation_df)
    test_diff(fit, contrast = CG1407 - S2R)
    # End Not Run

Optionally, we can turn the data also into a `SummarizedExperiment` or `MSnSet` object

    library(SummarizedExperiment)
    se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df)
    rowData(se) <- full_data[, c("Only.identified.by.site",
                                 "Reverse", "Potential.contaminant")]
    se
    #> class: SummarizedExperiment
    #> dim: 122 36
    #> metadata(0):
    #> assays(1): LFQ
    #> rownames(122): Q8IP47;Q9VJP8;Q9V435;A0A023GPQ3;Q2PDT6;Q7K540 A0A023GPV6;A8JV04;Q7YU03 ...
    #>   A0A0B4KGU4;Q9VHP0;M9PBB5;P09052 A0A0B4KGW0;Q8IMX7-2;A0A0B4J3Z9;Q8IMX7
    #> rowData names(3): Only.identified.by.site Reverse Potential.contaminant
    #> colnames(36): CG1407.01 CG1407.02 ... S2R.02 S2R.03
    #> colData names(2): Condition Replicate

    library(MSnbase)

    fData <- AnnotatedDataFrame(full_data[, c("Only.identified.by.site",
                                     "Reverse", "Potential.contaminant")])
    rownames(fData) <- rownames(data)
    ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData)
    ms
    #> MSnSet (storageMode: lockedEnvironment)
    #> assayData: 122 features, 36 samples
    #>   element names: exprs
    #> protocolData: none
    #> phenoData
    #>   sampleNames: CG1407.01 CG1407.02 ... S2R.03 (36 total)
    #>   varLabels: Condition Replicate
    #>   varMetadata: labelDescription
    #> featureData
    #>   featureNames: Q8IP47;Q9VJP8;Q9V435;A0A023GPQ3;Q2PDT6;Q7K540 A0A023GPV6;A8JV04;Q7YU03
    #>     ... A0A0B4KGW0;Q8IMX7-2;A0A0B4J3Z9;Q8IMX7 (122 total)
    #>   fvarLabels: Only.identified.by.site Reverse Potential.contaminant
    #>   fvarMetadata: labelDescription
    #> experimentData: use 'experimentData(object)'
    #> Annotation:
    #> - - - Processing information - - -
    #>  MSnbase version: 2.26.0

Both input types are also accepted by `proDA`.

    # Not Run
    library(proDA)
    fit <- proDA(se, design = ~ Condition - 1)
    test_diff(fit, contrast = ConditionCG1407 - ConditionS2R)
    # End Not Run

# 4 Tidyverse

The tidyverse is a set of coherent R packages that provide many useful functions for common data analysis tasks. It replicates many of the functionalities already available in base R packages, but learns from its mistakes and avoids some of the surprising behaviors. For example strings are never automatically converted to factors. Another popular feature in the tidyverse is the pipe operator (`%>%`) that makes it easy to chain complex transformations.

    library(dplyr)
    library(stringr)
    library(readr)
    library(tidyr)
    library(tibble)
    # Or short
    # library(tidyverse)

I first load the full data file

    # The read_tsv function works faster and more reliable than read.delim
    # But it sometimes needs help to identify the right type for each column,
    # because it looks only at the first 1,000 elements.
    # Here, I explicitly define the `Reverse` column as a character column
    full_data <- read_tsv(
        system.file("extdata/proteinGroups.txt",
                    package = "proDA", mustWork = TRUE),
        col_types = cols(Reverse = col_character())
    )

    full_data
    #> # A tibble: 122 × 359
    #>    Protein.IDs                      Majority.protein.IDs Peptide.counts..all. Peptide.counts..razo…¹
    #>    <chr>                            <chr>                <chr>                <chr>
    #>  1 Q8IP47;Q9VJP8;Q9V435;A0A023GPQ3… Q8IP47;Q9VJP8;Q9V43… 7;7;7;7;3;1          7;7;7;7;3;1
    #>  2 A0A023GPV6;A8JV04;Q7YU03         A0A023GPV6;A8JV04;Q… 2;2;2                2;2;2
    #>  3 A0A023GQA5;P24156                A0A023GQA5;P24156    13;13                13;13
    #>  4 Q1RKY1;A0A0B4LG19;A0A0B4J401;B7… Q1RKY1;A0A0B4LG19;A… 3;3;3;3;3;3;3;3;3;3… 3;3;3;3;3;3;3;3;3;3;3…
    #>  5 A0A0B4JD00;A8DY69;I0E2I4;A0A0B4… A0A0B4JD00;A8DY69;I… 6;6;6;6;6;5;5;5;5    6;6;6;6;6;5;5;5;5
    #>  6 A0A0B4JCT8;Q9V780                A0A0B4JCT8;Q9V780    2;2                  2;2
    #>  7 A0A0B4LHQ4;A0A0B4JD62;A0A0B4JDB… A0A0B4LHQ4;A0A0B4JD… 3;3;3;3;3;3;3;3;3    3;3;3;3;3;3;3;3;3
    #>  8 A0A0B4JCW4;Q9VHJ8;Q95U38         A0A0B4JCW4;Q9VHJ8;Q… 10;10;10             10;10;10
    #>  9 Q9VDV4;A0A0B4JCY1;Q8IN71;A0A0B4… Q9VDV4;A0A0B4JCY1;Q… 4;4;3;3              4;4;3;3
    #> 10 A0A0B4JCY6;Q7KSF4;A0A0B4KHN1;A0… A0A0B4JCY6;Q7KSF4;A… 6;6;6;6;6;3;3;3;3    6;6;6;6;6;3;3;3;3
    #> # ℹ 112 more rows
    #> # ℹ abbreviated name: ¹​Peptide.counts..razor.unique.
    #> # ℹ 355 more variables: Peptide.counts..unique. <chr>, Protein.names <chr>, Gene.names <chr>,
    #> #   Fasta.headers <chr>, Number.of.proteins <dbl>, Peptides <dbl>, Razor...unique.peptides <dbl>,
    #> #   Unique.peptides <dbl>, Peptides.CG1407.01 <dbl>, Peptides.CG1407.02 <dbl>,
    #> #   Peptides.CG1407.03 <dbl>, Peptides.CG4676.01 <dbl>, Peptides.CG4676.02 <dbl>,
    #> #   Peptides.CG4676.03 <dbl>, Peptides.CG51963.01 <dbl>, Peptides.CG51963.02 <dbl>, …

Next, I create a tidy version of the data set. I pipe (`%>%`) the results from each transformation to the next transformation, to first `select` the columns of interest, reshape (`gather`) the dataset from wide to long format, and lastly create new columns with `mutate`.

    # I explicitly call `dplyr::select()` because there is a naming conflict
    # between the tidyverse and BioConductor packages for `select()` function
    tidy_data <- full_data %>%
        dplyr::select(ProteinID=Protein.IDs, starts_with("LFQ.intensity.")) %>%
        gather(Sample, Intensity, starts_with("LFQ.intensity.")) %>%
        mutate(Condition = str_match(Sample,
                     "LFQ\\.intensity\\.([[:alnum:]]+)\\.\\d+")[,2]) %>%
        mutate(Replicate = as.numeric(str_match(Sample,
                     "LFQ\\.intensity\\.[[:alnum:]]+\\.(\\d+)")[,2])) %>%
        mutate(SampleName = paste0(Condition, ".", Replicate))

    tidy_data
    #> # A tibble: 4,392 × 6
    #>    ProteinID                                         Sample Intensity Condition Replicate SampleName
    #>    <chr>                                             <chr>      <dbl> <chr>         <dbl> <chr>
    #>  1 Q8IP47;Q9VJP8;Q9V435;A0A023GPQ3;Q2PDT6;Q7K540     LFQ.i…         0 CG1407            1 CG1407.1
    #>  2 A0A023GPV6;A8JV04;Q7YU03                          LFQ.i…         0 CG1407            1 CG1407.1
    #>  3 A0A023GQA5;P24156                                 LFQ.i…         0 CG1407            1 CG1407.1
    #>  4 Q1RKY1;A0A0B4LG19;A0A0B4J401;B7YZL2;A1ZBH5;B7YZL… LFQ.i…         0 CG1407            1 CG1407.1
    #>  5 A0A0B4JD00;A8DY69;I0E2I4;A0A0B4JCQ5;Q8SXP0;E5DK1… LFQ.i…   2082100 CG1407            1 CG1407.1
    #>  6 A0A0B4JCT8;Q9V780                                 LFQ.i…         0 CG1407            1 CG1407.1
    #>  7 A0A0B4LHQ4;A0A0B4JD62;A0A0B4JDB5;A0A0B4LGQ5;A0A0… LFQ.i…         0 CG1407            1 CG1407.1
    #>  8 A0A0B4JCW4;Q9VHJ8;Q95U38                          LFQ.i…   2858600 CG1407            1 CG1407.1
    #>  9 Q9VDV4;A0A0B4JCY1;Q8IN71;A0A0B4KGH4               LFQ.i…   1291400 CG1407            1 CG1407.1
    #> 10 A0A0B4JCY6;Q7KSF4;A0A0B4KHN1;A0A0B4KGT8;Q9VEN1;A… LFQ.i…         0 CG1407            1 CG1407.1
    #> # ℹ 4,382 more rows

Using the tidy data, I create the annotation data frame and the data matrix.

    data <- tidy_data %>%
        mutate(Intensity = ifelse(Intensity == 0, NA, log2(Intensity))) %>%
        dplyr::select(ProteinID, SampleName, Intensity) %>%
        spread(SampleName, Intensity) %>%
        column_to_rownames("ProteinID") %>%
        as.matrix()

    data[1:4, 1:7]
    #>                          CG1407.1 CG1407.2 CG1407.3 CG4676.1 CG4676.2 CG4676.3 CG51963.1
    #> A0A023GPV6;A8JV04;Q7YU03       NA       NA       NA       NA       NA       NA        NA
    #> A0A023GQA5;P24156              NA       NA       NA       NA 20.20120       NA  18.09151
    #> A0A0B4JCT8;Q9V780              NA       NA       NA       NA 19.25836       NA        NA
    #> A0A0B4JCW4;Q9VHJ8;Q95U38 21.44688 22.29852 21.19821       NA 21.91189       NA  22.14476

    annotation_df <- tidy_data %>%
        dplyr::select(SampleName, Condition, Replicate) %>%
        distinct() %>%
        arrange(Condition, Replicate) %>%
        as.data.frame() %>%
        column_to_rownames("SampleName")

    annotation_df
    #>           Condition Replicate
    #> CG1407.1     CG1407         1
    #> CG1407.2     CG1407         2
    #> CG1407.3     CG1407         3
    #> CG4676.1     CG4676         1
    #> CG4676.2     CG4676         2
    #> CG4676.3     CG4676         3
    #> CG51963.1   CG51963         1
    #> CG51963.2   CG51963         2
    #> CG51963.3   CG51963         3
    #> CG5620A.1   CG5620A         1
    #> CG5620A.2   CG5620A         2
    #> CG5620A.3   CG5620A         3
    #> CG5620B.1   CG5620B         1
    #> CG5620B.2   CG5620B         2
    #> CG5620B.3   CG5620B         3
    #> CG5880.1     CG5880         1
    #> CG5880.2     CG5880         2
    #> CG5880.3     CG5880         3
    #> CG6017.1     CG6017         1
    #> CG6017.2     CG6017         2
    #> CG6017.3     CG6017         3
    #> CG6618.1     CG6618         1
    #> CG6618.2     CG6618         2
    #> CG6618.3     CG6618         3
    #> CG6627.1     CG6627         1
    #> CG6627.2     CG6627         2
    #> CG6627.3     CG6627         3
    #> CG8314.1     CG8314         1
    #> CG8314.2     CG8314         2
    #> CG8314.3     CG8314         3
    #> GsbPI.1       GsbPI         1
    #> GsbPI.2       GsbPI         2
    #> GsbPI.3       GsbPI         3
    #> S2R.1           S2R         1
    #> S2R.2           S2R         2
    #> S2R.3           S2R         3

Optionally, we can again turn this into a `SummarizedExperiment` or `MSnSet` object

    library(SummarizedExperiment)
    se <- SummarizedExperiment(SimpleList(LFQ=data), colData=annotation_df)
    rowData(se) <- full_data[, c("Only.identified.by.site",
                                 "Reverse", "Potential.contaminant")]
    se
    #> class: SummarizedExperiment
    #> dim: 122 36
    #> metadata(0):
    #> assays(1): LFQ
    #> rownames(122): A0A023GPV6;A8JV04;Q7YU03 A0A023GQA5;P24156 ... Q9VHX9;A0A0B4KFA6
    #>   Q9VNF8;A0A0B4K6T4;A0A0B4K5Z8
    #> rowData names(3): Only.identified.by.site Reverse Potential.contaminant
    #> colnames(36): CG1407.1 CG1407.2 ... S2R.2 S2R.3
    #> colData names(2): Condition Replicate

    library(MSnbase)

    fData <- AnnotatedDataFrame(full_data[, c("Only.identified.by.site",
                                     "Reverse", "Potential.contaminant")])
    rownames(fData) <- rownames(data)
    #> Warning: Setting row names on a tibble is deprecated.
    ms <- MSnSet(data, pData=AnnotatedDataFrame(annotation_df), fData=fData)
    ms
    #> MSnSet (storageMode: lockedEnvironment)
    #> assayData: 122 features, 36 samples
    #>   element names: exprs
    #> protocolData: none
    #> phenoData
    #>   sampleNames: CG1407.1 CG1407.2 ... S2R.3 (36 total)
    #>   varLabels: Condition Replicate
    #>   varMetadata: labelDescription
    #> featureData
    #>   featureNames: 1 2 ... 3 (122 total)
    #>   fvarLabels: Only.identified.by.site Reverse Potential.contaminant
    #>   fvarMetadata: labelDescription
    #> experimentData: use 'experimentData(object)'
    #> Annotation:
    #> - - - Processing information - - -
    #>  MSnbase version: 2.26.0

Both input types are also accepted by `proDA`.

    # Not Run
    library(proDA)
    fit <- proDA(se, design = ~ Condition - 1)
    test_diff(fit, contrast = ConditionCG1407 - ConditionS2R)
    # End Not Run

# 5 DEP

DEP is a BioConductor package that is designed for the analysis of mass spectrometry data. It provides helper functions to impute missing values and makes it easy to run limma on the completed dataset.

To load the data, we need to provide all the column names of the intensity values. I then call the `import_MaxQuant()` function that directly creates a `SummarizedExperiment` object.

    library(DEP)
    #>
    #> Attaching package: 'DEP'
    #> The following object is masked from 'package:MSnbase':
    #>
    #>     impute
    #> The following object is masked from 'package:ProtGenerics':
    #>
    #>     impute
    #> The following object is masked from 'package:proDA':
    #>
    #>     test_diff

    full_data <- read.delim(
        system.file("extdata/proteinGroups.txt",
                    package = "proDA", mustWork = TRUE),
        stringsAsFactors = FALSE
    )

    exp_design <- data.frame(
       label =c("LFQ.intensity.CG1407.01", "LFQ.intensity.CG1407.02",  "LFQ.intensity.CG1407.03",  "LFQ.intensity.CG4676.01",  "LFQ.intensity.CG4676.02", "LFQ.intensity.CG4676.03", "LFQ.intensity.CG51963.01", "LFQ.intensity.CG51963.02", "LFQ.intensity.CG51963.03","LFQ.intensity.CG5620A.01", "LFQ.intensity.CG5620A.02", "LFQ.intensity.CG5620A.03", "LFQ.intensity.CG5620B.01","LFQ.intensity.CG5620B.02", "LFQ.intensity.CG5620B.03", "LFQ.intensity.CG5880.01", "LFQ.intensity.CG5880.02",  "LFQ.intensity.CG5880.03",  "LFQ.intensity.CG6017.01",  "LFQ.intensity.CG6017.02", "LFQ.intensity.CG6017.03", "LFQ.intensity.CG6618.01",  "LFQ.intensity.CG6618.02",  "LFQ.intensity.CG6618.03",  "LFQ.intensity.CG6627.01", "LFQ.intensity.CG6627.02", "LFQ.intensity.CG6627.03",  "LFQ.intensity.CG8314.01", "LFQ.intensity.CG8314.02",  "LFQ.intensity.CG8314.03", "LFQ.intensity.GsbPI.001", "LFQ.intensity.GsbPI.002",  "LFQ.intensity.GsbPI.003",  "LFQ.intensity.S2R.01", "LFQ.intensity.S2R.02", "LFQ.intensity.S2R.03"),
       condition = c("CG1407", "CG1407", "CG1407", "CG4676", "CG4676", "CG4676", "CG51963", "CG51963", "CG51963", "CG5620A", "CG5620A", "CG5620A", "CG5620B", "CG5620B", "CG5620B", "CG5880", "CG5880", "CG5880", "CG6017", "CG6017", "CG6017", "CG6618", "CG6618", "CG6618", "CG6627", "CG6627", "CG6627", "CG8314", "CG8314", "CG8314", "GsbPI", "GsbPI", "GsbPI", "S2R", "S2R", "S2R" ),
       replicate = rep(1:3, times=12),
       stringsAsFactors = FALSE
    )

    se <- import_MaxQuant(full_data, exp_design)
    #> Filtering based on 'Reverse', 'Potential.contaminant' column(s)
    #> Making unique names
    #> Obtaining SummarizedExperiment object
    se
    #> class: SummarizedExperiment
    #> dim: 122 36
    #> metadata(0):
    #> assays(1): ''
    #> rownames(122): yuri A0A023GPV6 ... bel Miro
    #> rowData names(325): Protein.IDs Majority.protein.IDs ... name ID
    #> colnames(36): CG1407_1 CG1407_2 ... S2R_2 S2R_3
    #> colData names(4): label ID condition replicate
    assay(se)[1:5, 1:5]
    #>            CG1407_1 CG1407_2 CG1407_3 CG4676_1 CG4676_2
    #> yuri             NA       NA       NA       NA       NA
    #> A0A023GPV6       NA       NA       NA       NA       NA
    #> l(2)37Cc         NA       NA       NA       NA 20.20120
    #> CG10737-RC       NA 18.87622 18.90683       NA 18.77520
    #> Lpin       20.98961 20.40302 19.78941       NA 20.22682

Again, we can run `proDA` on the result:

    # Not Run
    library(proDA)
    fit <- proDA(se, design = ~ condition - 1)
    # Here, we need to be specific, because DEP also has a test_diff method
    proDA::test_diff(fit, contrast = conditionCG1407 - conditionS2R)
    # End Not Run

# 6 Session Info

    sessionInfo()
    #> R version 4.3.0 RC (2023-04-13 r84269 ucrt)
    #> Platform: x86_64-w64-mingw32/x64 (64-bit)
    #> Running under: Windows Server 2022 x64 (build 20348)
    #>
    #> Matrix products: default
    #>
    #>
    #> locale:
    #> [1] LC_COLLATE=C                           LC_CTYPE=English_United States.utf8
    #> [3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.utf8
    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #>  [1] DEP_1.22.0                  tibble_3.2.1                tidyr_1.3.0
    #>  [4] readr_2.1.4                 stringr_1.5.0               dplyr_1.1.2
    #>  [7] MSnbase_2.26.0              ProtGenerics_1.32.0         mzR_2.34.0
    #> [10] Rcpp_1.0.10                 SummarizedExperiment_1.30.0 Biobase_2.60.0
    #> [13] GenomicRanges_1.52.0        GenomeInfoDb_1.36.0         IRanges_2.34.0
    #> [16] S4Vectors_0.38.0            BiocGenerics_0.46.0         MatrixGenerics_1.12.0
    #> [19] matrixStats_0.63.0          proDA_1.14.0                BiocStyle_2.28.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] bitops_1.0-7            sandwich_3.0-2          rlang_1.1.0
    #>   [4] magrittr_2.0.3          shinydashboard_0.7.2    GetoptLong_1.0.5
    #>   [7] clue_0.3-64             compiler_4.3.0          png_0.1-8
    #>  [10] vctrs_0.6.2             shape_1.4.6             pkgconfig_2.0.3
    #>  [13] crayon_1.5.2            fastmap_1.1.1           ellipsis_0.3.2
    #>  [16] magick_2.7.4            XVector_0.40.0          utf8_1.2.3
    #>  [19] promises_1.2.0.1        rmarkdown_2.21          tzdb_0.3.0
    #>  [22] preprocessCore_1.62.0   purrr_1.0.1             bit_4.0.5
    #>  [25] xfun_0.39               zlibbioc_1.46.0         cachem_1.0.7
    #>  [28] jsonlite_1.8.4          gmm_1.7                 later_1.3.0
    #>  [31] highr_0.10              DelayedArray_0.26.0     BiocParallel_1.34.0
    #>  [34] parallel_4.3.0          cluster_2.1.4           R6_2.5.1
    #>  [37] bslib_0.4.2             stringi_1.7.12          RColorBrewer_1.1-3
    #>  [40] limma_3.56.0            jquerylib_0.1.4         assertthat_0.2.1
    #>  [43] bookdown_0.33           iterators_1.0.14        knitr_1.42
    #>  [46] zoo_1.8-12              httpuv_1.6.9            Matrix_1.5-4
    #>  [49] tidyselect_1.2.0        yaml_2.3.7              doParallel_1.0.17
    #>  [52] codetools_0.2-19        affy_1.78.0             lattice_0.21-8
    #>  [55] plyr_1.8.8              shiny_1.7.4             withr_2.5.0
    #>  [58] tmvtnorm_1.5            evaluate_0.20           archive_1.1.5
    #>  [61] norm_1.0-10.0           circlize_0.4.15         pillar_1.9.0
    #>  [64] affyio_1.70.0           BiocManager_1.30.20     DT_0.27
    #>  [67] foreach_1.5.2           MALDIquant_1.22.1       ncdf4_1.21
    #>  [70] generics_0.1.3          vroom_1.6.1             RCurl_1.98-1.12
    #>  [73] hms_1.1.3               ggplot2_3.4.2           munsell_0.5.0
    #>  [76] scales_1.2.1            extraDistr_1.9.1        xtable_1.8-4
    #>  [79] glue_1.6.2              pheatmap_1.0.12         tools_4.3.0
    #>  [82] mzID_1.38.0             vsn_3.68.0              imputeLCMD_2.1
    #>  [85] mvtnorm_1.1-3           XML_3.99-0.14           grid_4.3.0
    #>  [88] impute_1.74.0           MsCoreUtils_1.12.0      colorspace_2.1-0
    #>  [91] GenomeInfoDbData_1.2.10 cli_3.6.1               fansi_1.0.4
    #>  [94] ComplexHeatmap_2.16.0   pcaMethods_1.92.0       gtable_0.3.3
    #>  [97] sass_0.4.5              digest_0.6.31           htmlwidgets_1.6.2
    #> [100] rjson_0.2.21            htmltools_0.5.5         lifecycle_1.0.3
    #> [103] mime_0.12               GlobalOptions_0.1.2     bit64_4.0.5
    #> [106] MASS_7.3-59