# Supported input formats for readQFeatures()

Karolína Kryštofová and Laurent Gatto

#### 28 April 2026

#### Package

QFeatures 1.22.0

## 1 Methods

### 1.1 Datasets

This vignette demonstrates the use of the QFeatures package’s `readQFeatures()` function to import data produced by popular third-party software. For this purpose, subsets of the following previously publicly available datasets have been used:

Citation | PXD ID | Mode | Label | Code
--- | --- | --- | --- | ---
Van Puyvelde et al., 2022 | PXD028735 | DIA | LFQ | A_DIA_LFQ
DDA | LFQ | A_DDA_LFQ
Derks et al., 2022 | PXD029531 | DIA | plexDIA | B_DIA_plex
Christoforou et al., 2016 | PXD001279 | DDA | TMT | C_DDA_TMT

Specifically, these subsets consist of these files:

Code | Original raw file name
A_DIA_LFQ | LFQ_timsTOFPro_diaPASEF_Condition_A_Sample_Alpha_01.d
LFQ_timsTOFPro_diaPASEF_Condition_A_Sample_Alpha_02.d
LFQ_timsTOFPro_diaPASEF_Condition_A_Sample_Alpha_03.d
LFQ_timsTOFPro_diaPASEF_Condition_B_Sample_Alpha_01.d
LFQ_timsTOFPro_diaPASEF_Condition_B_Sample_Alpha_02.d
LFQ_timsTOFPro_diaPASEF_Condition_B_Sample_Alpha_03.d
A_DDA_LFQ | LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_01.raw
LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_02.raw
LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_03.raw
LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_01.raw
LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_02.raw
LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_03.raw
B_DIA_plex | wJD803.raw
wJD804.raw
wJD815.raw
C_DDA_TMT | Replicate1_fraction1.raw
Replicate1_fraction2.raw

Identifications and quantifications performed on these datasets were used in combination with the following software:

The data files are available in the `MsDataHub` package (>= 1.11.5).

    library("MsDataHub")
    MsDataHub() |>
        dplyr::filter(grepl("19137577", SourceUrl)) |>
        dplyr::pull(Title)

    ##  [1] "Christoforou_2016_TMT_DDA_FragPipe_Fraction1_psm.tsv"
    ##  [2] "Christoforou_2016_TMT_DDA_FragPipe_Fraction2_psm.tsv"
    ##  [3] "Christoforou_2016_TMT_DDA_MaxQuant_evidence.txt"
    ##  [4] "Christoforou_2016_TMT_DDA_sage_results.sage.tsv"
    ##  [5] "Christoforou_2016_TMT_DDA_sage_tmt.tsv"
    ##  [6] "Derks_2022_plex_DIA_DIANN_report_subset.tsv"
    ##  [7] "vanPuyvelde_2022_LFQ_DDA_FragPipe_A_1_psm.tsv"
    ##  [8] "vanPuyvelde_2022_LFQ_DDA_FragPipe_A_2_psm.tsv"
    ##  [9] "vanPuyvelde_2022_LFQ_DDA_FragPipe_A_3_psm.tsv"
    ## [10] "vanPuyvelde_2022_LFQ_DDA_FragPipe_B_1_psm.tsv"
    ## [11] "vanPuyvelde_2022_LFQ_DDA_FragPipe_B_2_psm.tsv"
    ## [12] "vanPuyvelde_2022_LFQ_DDA_FragPipe_B_3_psm.tsv"
    ## [13] "vanPuyvelde_2022_LFQ_DDA_MaxQuant_evidence.txt"
    ## [14] "vanPuyvelde_2022_LFQ_DDA_MaxQuant_peptides.txt"
    ## [15] "vanPuyvelde_2022_LFQ_DDA_MaxQuant_proteinGroups.txt"
    ## [16] "vanPuyvelde_2022_LFQ_DDA_PEAKS_LFQ_report.csv"
    ## [17] "vanPuyvelde_2022_LFQ_DDA_sage_lfq.tsv"
    ## [18] "vanPuyvelde_2022_LFQ_DDA_sage_results.sage.tsv"
    ## [19] "vanPuyvelde_2022_LFQ_DIA_DIANN_report.parquet"
    ## [20] "vanPuyvelde_2022_LFQ_DIA_DIANN_report.tsv"

Each file can be accessed with the function that has its name:

    vanPuyvelde_2022_LFQ_DDA_FragPipe_A_2_psm.tsv()

    ## see ?MsDataHub and browseVignettes('MsDataHub') for documentation

    ## loading from cache

    ##                                                                               EH10423
    ## "/Users/biocbuild/Library/Caches/org.R-project.R/R/ExperimentHub/12303394dca9e_10490"

    Derks_2022_plex_DIA_DIANN_report_subset.tsv()

    ## see ?MsDataHub and browseVignettes('MsDataHub') for documentation
    ## loading from cache

    ##                                                                               EH10421
    ## "/Users/biocbuild/Library/Caches/org.R-project.R/R/ExperimentHub/1230322316876_10488"

and imported as a standard `data.frame` using the usual `read.*()` functions (see below).

### 1.2 Existing search outputs

Example outputs for LFQ quantification using DIA-NN, Spectronaut and PEAKS were sourced from the ProteoBench website. IDs of these outputs on the ProteoBench website are as follows:

- DIA-NN:
  - DIA-NN_20250714_074145 (.parquet output)
  - DIA-NN_20250606_103313 (.tsv output)
- Spectronaut:
  - Spectronaut_20250609_135453
- PEAKS:
  - PEAKS_20250714_150458

As an example output for multiplex quantification using DIA-NN, a search result of the plexDIA dataset was sourced from the MassIVE repository (MSV000088302).

### 1.3 New search outputs

Following software versions were used to produce search results used in this vignette:

- MaxQuant 2.7.0
- sage 0.14.7
- FragPipe 23.1

## 2 Introduction

Below, we describe individual outputs and their processing using the `readQFeatures()` functions. When applicable, we demonstrate how to read data on PSM, precursor, as well as protein group level.

For general explanation of the `QFeatures` class and detailed description of individual arguments taken by the `readQFeatures()` group of functions, consult the `readQFeatures()` manual page of this vignette.

To initiate the session, we will load the `QFeatures` package.

    library(QFeatures)

## 3 MaxQuant

MaxQuant produces several output `.txt` files. In order to obtain information from several levels of the search, we can look at the `evidence.txt`, `peptides.txt` and `proteinGroups.txt` files.

### 3.1 Label-free

Here we will process the results of a multi-set label-free experiment. First we will read the `evidence.txt` file storing information about PSM-level data:

    dataMaxquantLFQevidence <-
        vanPuyvelde_2022_LFQ_DDA_MaxQuant_evidence.txt() |>
        read.delim()

    nrow(dataMaxquantLFQevidence)

    ## [1] 1219

We can now import the data.frame as a `QFeatures` object using `"Intensity"` as quantitative column. This column the quantitation values of all samples, acquired in different runs, as defined in the `"Experiment"` column. We also rename the set names, prefixing them with `"psm_"`.

    qfMaxquant <- readQFeatures(dataMaxquantLFQevidence,
                                quantCols = "Intensity",
                                runCol = "Experiment")

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    names(qfMaxquant) <- paste('psm', names(qfMaxquant), sep = '_')

    qfMaxquant

    ## An instance of class QFeatures (type: bulk) with 6 sets:
    ##
    ##  [1] psm_A_Sample_Alpha_01: SummarizedExperiment with 224 rows and 1 columns
    ##  [2] psm_A_Sample_Alpha_02: SummarizedExperiment with 227 rows and 1 columns
    ##  [3] psm_A_Sample_Alpha_03: SummarizedExperiment with 222 rows and 1 columns
    ##  [4] psm_B_Sample_Alpha_01: SummarizedExperiment with 185 rows and 1 columns
    ##  [5] psm_B_Sample_Alpha_02: SummarizedExperiment with 172 rows and 1 columns
    ##  [6] psm_B_Sample_Alpha_03: SummarizedExperiment with 189 rows and 1 columns

Next we will read the peptide-level results from a `peptides.txt` file and append this to the `QFeatures` object as a new assay:

    dataMaxquantLFQpeptide <-
        vanPuyvelde_2022_LFQ_DDA_MaxQuant_peptides.txt() |>
        read.delim()
    nrow(dataMaxquantLFQpeptide)

    ## [1] 260

This table is in a large format, meaning that the peptide quantitation values of different samples are stored in different columns. We thus get the indices of respective intensity columns, starting with `"Intensity."`.

    (i <- grep('Intensity.', colnames(dataMaxquantLFQpeptide), fixed = TRUE))

    ## [1] 53 54 55 56 57 58

    colnames(dataMaxquantLFQpeptide)[i]

    ## [1] "Intensity.A_Sample_Alpha_01" "Intensity.A_Sample_Alpha_02"
    ## [3] "Intensity.A_Sample_Alpha_03" "Intensity.B_Sample_Alpha_01"
    ## [5] "Intensity.B_Sample_Alpha_02" "Intensity.B_Sample_Alpha_03"

We can read this peptide-level table as a new `QFeatures` object using the same `readQFeatures()` as above. This time, it will contain a single set with as many columns as there are samples/acquisitions in the data.

    readQFeatures(dataMaxquantLFQpeptide, quantCols = i, fnames = 'Sequence')

    ## An instance of class QFeatures (type: bulk) with 1 set:
    ##
    ##  [1] quants: SummarizedExperiment with 260 rows and 6 columns

If we want to add the peptide-level data to our previously created `QFeatures` object, we can read it as an invidual set (a `SummarizedExperiment` instance) and add it with `addAssay()`

    pepSE <- readSummarizedExperiment(dataMaxquantLFQpeptide,
                                      quantCols = i,
                                      fnames = 'Sequence')
    pepSE

    ## class: SummarizedExperiment
    ## dim: 260 6
    ## metadata(0):
    ## assays(1): ''
    ## rownames(260): GAGSSEPVTGLDAK VEATFGVDESNAK ... VMALELGPHK
    ##   VNAVNPTVVMTSMGQATWSDPHK
    ## rowData names(65): Sequence N.term.cleavage.window ... Mass.deficit
    ##   MS.MS.Count
    ## colnames(6): Intensity.A_Sample_Alpha_01 Intensity.A_Sample_Alpha_02
    ##   ... Intensity.B_Sample_Alpha_02 Intensity.B_Sample_Alpha_03
    ## colData names(0):

    qfMaxquant <- addAssay(qfMaxquant,
                          pepSE,
                          name = 'peptides')
    qfMaxquant

    ## An instance of class QFeatures (type: bulk) with 7 sets:
    ##
    ##  [1] psm_A_Sample_Alpha_01: SummarizedExperiment with 224 rows and 1 columns
    ##  [2] psm_A_Sample_Alpha_02: SummarizedExperiment with 227 rows and 1 columns
    ##  [3] psm_A_Sample_Alpha_03: SummarizedExperiment with 222 rows and 1 columns
    ##  [4] psm_B_Sample_Alpha_01: SummarizedExperiment with 185 rows and 1 columns
    ##  [5] psm_B_Sample_Alpha_02: SummarizedExperiment with 172 rows and 1 columns
    ##  [6] psm_B_Sample_Alpha_03: SummarizedExperiment with 189 rows and 1 columns
    ##  [7] peptides: SummarizedExperiment with 260 rows and 6 columns

We see that a new assay has been appended to `QFeatures` object.

Finally, we will append the protein group-level in the same manner. Here we will use the `"LFQ.intensity"` columns:

    dataMaxquantLFQprotein <-
        vanPuyvelde_2022_LFQ_DDA_MaxQuant_proteinGroups.txt() |>
        read.delim()
    nrow(dataMaxquantLFQprotein)

    ## [1] 40

    ## get indices of LFQ intensity columns
    (i <- grep('LFQ.intensity.', colnames(dataMaxquantLFQprotein),
               fixed = TRUE))

    ## [1] 52 53 54 55 56 57

    colnames(dataMaxquantLFQprotein)[i]

    ## [1] "LFQ.intensity.A_Sample_Alpha_01" "LFQ.intensity.A_Sample_Alpha_02"
    ## [3] "LFQ.intensity.A_Sample_Alpha_03" "LFQ.intensity.B_Sample_Alpha_01"
    ## [5] "LFQ.intensity.B_Sample_Alpha_02" "LFQ.intensity.B_Sample_Alpha_03"

    ## load the data
    protSE <- readSummarizedExperiment(dataMaxquantLFQprotein,
                                       quantCols = i,
                                       fnames = 'Protein.IDs')
    protSE

    ## class: SummarizedExperiment
    ## dim: 40 6
    ## metadata(0):
    ## assays(1): ''
    ## rownames(40): iRT-b-cRAP iRT-c-cRAP ... P07149 Q7Z4W1
    ## rowData names(73): Protein.IDs Majority.protein.IDs ... Taxonomy.IDs
    ##   Taxonomy.names
    ## colnames(6): LFQ.intensity.A_Sample_Alpha_01
    ##   LFQ.intensity.A_Sample_Alpha_02 ... LFQ.intensity.B_Sample_Alpha_02
    ##   LFQ.intensity.B_Sample_Alpha_03
    ## colData names(0):

    qfMaxquant <- addAssay(qfMaxquant,
                           protSE,
                           name = 'proteinGroups')
    qfMaxquant

    ## An instance of class QFeatures (type: bulk) with 8 sets:
    ##
    ##  [1] psm_A_Sample_Alpha_01: SummarizedExperiment with 224 rows and 1 columns
    ##  [2] psm_A_Sample_Alpha_02: SummarizedExperiment with 227 rows and 1 columns
    ##  [3] psm_A_Sample_Alpha_03: SummarizedExperiment with 222 rows and 1 columns
    ##  ...
    ##  [6] psm_B_Sample_Alpha_03: SummarizedExperiment with 189 rows and 1 columns
    ##  [7] peptides: SummarizedExperiment with 260 rows and 6 columns
    ##  [8] proteinGroups: SummarizedExperiment with 40 rows and 6 columns

It is important to highlight however that, while it is possible to add PSM-, peptide- and protein-level sets one-by-one, as illustrated above, we strongly recommend to compute the peptide-level data from the PSM-level data, and the protein-level data from the peptide-level data using the `QFeatures::aggregateFeatures()` function. The function will record the link between features, PSM to peptide and peptides to protein, and consistently apply filtering across these levels. Alternatively, these links between sets can be re-computer with the `addAssayLink()` function.

### 3.2 TMT

Below, we will demonstrate how to read data from a TMT-labeled experiment consisting of two runs:

    dataMaxquantTMTevidence <-
        Christoforou_2016_TMT_DDA_MaxQuant_evidence.txt() |>
        read.delim()

    (i <- grep('Reporter.intensity.\\d+', colnames(dataMaxquantTMTevidence)))

    ##  [1] 73 74 75 76 77 78 79 80 81 82

    colnames(dataMaxquantTMTevidence)[i]

    ##  [1] "Reporter.intensity.1"  "Reporter.intensity.2"  "Reporter.intensity.3"
    ##  [4] "Reporter.intensity.4"  "Reporter.intensity.5"  "Reporter.intensity.6"
    ##  [7] "Reporter.intensity.7"  "Reporter.intensity.8"  "Reporter.intensity.9"
    ## [10] "Reporter.intensity.10"

    qfMaxquantTMT <- readQFeatures(dataMaxquantTMTevidence,
                                   quantCols = i,
                                   runCol = 'Raw.file',
                                   fnames = 'Sequence')

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    ## Warning in FUN(X[[i]], ...): Duplicated entries found in 'Sequence' in rowData
    ## of assay Replicate1_fraction1; they are made unique.

    ## Warning in FUN(X[[i]], ...): Duplicated entries found in 'Sequence' in rowData
    ## of assay Replicate1_fraction2; they are made unique.

    qfMaxquantTMT

    ## An instance of class QFeatures (type: bulk) with 2 sets:
    ##
    ##  [1] Replicate1_fraction1: SummarizedExperiment with 47 rows and 10 columns
    ##  [2] Replicate1_fraction2: SummarizedExperiment with 77 rows and 10 columns

We see that a separate experiment has been created for each run with 10 columns corresponding to the 10 TMT channels.

## 4 DIA-NN

### 4.1 Label-free

DIA-NN versions 1.9.0 and below produce a main .tsv search result file, which has been replaced by a .parquet file from version 2.0.0 up solely .

DIA-NN .tsv reports can be read using the `readQFeaturesFromDIANN()` function:

    qfDiannLFQ <-
        vanPuyvelde_2022_LFQ_DIA_DIANN_report.tsv() |>
        read.delim() |>
        readQFeaturesFromDIANN(runCol = 'Run')

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    qfDiannLFQ

    ## An instance of class QFeatures (type: bulk) with 6 sets:
    ##
    ##  [1] ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494: SummarizedExperiment with 393 rows and 1 columns
    ##  [2] ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500: SummarizedExperiment with 394 rows and 1 columns
    ##  [3] ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506: SummarizedExperiment with 391 rows and 1 columns
    ##  [4] ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496: SummarizedExperiment with 382 rows and 1 columns
    ##  [5] ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502: SummarizedExperiment with 378 rows and 1 columns
    ##  [6] ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508: SummarizedExperiment with 381 rows and 1 columns

In order to read a .parquet file in R, we need to use the `arrow` package, that provides an interface to the Arrow C++ library. After reading this file however, we can work with the resulting data.frame in the same manner as we are used to in case of the .tsv report.

    qfDiannParquet <-
        vanPuyvelde_2022_LFQ_DIA_DIANN_report.parquet() |>
        arrow::read_parquet() |>
        readQFeaturesFromDIANN(runCol = 'Run')

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    qfDiannParquet

    ## An instance of class QFeatures (type: bulk) with 6 sets:
    ##
    ##  [1] ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494: SummarizedExperiment with 422 rows and 1 columns
    ##  [2] ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500: SummarizedExperiment with 426 rows and 1 columns
    ##  [3] ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506: SummarizedExperiment with 422 rows and 1 columns
    ##  [4] ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496: SummarizedExperiment with 408 rows and 1 columns
    ##  [5] ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502: SummarizedExperiment with 409 rows and 1 columns
    ##  [6] ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508: SummarizedExperiment with 416 rows and 1 columns

As you can see, both experiments consist of the same run names as both searches have been performed on the same set of raw data. The numbers of rows in each `SummarizedExperiment` however differ between those two reports, as both searches have been performed using different software versions, as well as different search parameters.

### 4.2 plexDIA

To correctly parse a plexDIA experiment, it is necessary to set the `multiplexing` parameter to `"mTRAQ"`:

    qfDiannPlex <-
        Derks_2022_plex_DIA_DIANN_report_subset.tsv() |>
        read.delim() |>
        readQFeaturesFromDIANN(runCol = 'Run',
                               multiplexing = 'mTRAQ')

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    qfDiannPlex

    ## An instance of class QFeatures (type: bulk) with 3 sets:
    ##
    ##  [1] eJD905: SummarizedExperiment with 65 rows and 3 columns
    ##  [2] eJD906: SummarizedExperiment with 66 rows and 3 columns
    ##  [3] eJD907: SummarizedExperiment with 75 rows and 3 columns

The run names in this output file are not the most informative with regards to the samples. We will now edit the sample metadata to contain more meaningful sample annotation. All runs were performed on the same sample, in 3 technical replicates:

    qfDiannPlex$sample <- 'mixed standard'
    qfDiannPlex$rep <- rep(1:3, each = 3)
    qfDiannPlex$label <- paste0('mTraq d', rep(c(0, 4, 8), times = 3))
    colData(qfDiannPlex)

    ## DataFrame with 9 rows and 3 columns
    ##                 sample       rep       label
    ##            <character> <integer> <character>
    ## eJD905_0 mixed stan...         1    mTraq d0
    ## eJD905_4 mixed stan...         1    mTraq d4
    ## eJD905_8 mixed stan...         1    mTraq d8
    ## eJD906_0 mixed stan...         2    mTraq d0
    ## eJD906_4 mixed stan...         2    mTraq d4
    ## eJD906_8 mixed stan...         2    mTraq d8
    ## eJD907_0 mixed stan...         3    mTraq d0
    ## eJD907_4 mixed stan...         3    mTraq d4
    ## eJD907_8 mixed stan...         3    mTraq d8

## 5 sage

The sage search engine stores quantification data either in the lfq.tsv or tmt.tsv file based on the quantification used.

As above for label-free quantification, the lfq.tsv file contains estimated quantities of identified peptidoforms and is grouped on modified sequence level:

    dataSageLFQ <-
        vanPuyvelde_2022_LFQ_DDA_sage_lfq.tsv() |>
        read.delim()

    (i <- grep('.mzML', colnames(dataSageLFQ), fixed = TRUE))

    ## [1]  7  8  9 10 11 12

    colnames(dataSageLFQ)[i]

    ## [1] "LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_01.mzML"
    ## [2] "LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_02.mzML"
    ## [3] "LFQ_Orbitrap_DDA_Condition_A_Sample_Alpha_03.mzML"
    ## [4] "LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_01.mzML"
    ## [5] "LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_02.mzML"
    ## [6] "LFQ_Orbitrap_DDA_Condition_B_Sample_Alpha_03.mzML"

    qfSageLFQ <- readQFeatures(dataSageLFQ,
                               quantCols = i,
                               name = 'peptides')

    qfSageLFQ

    ## An instance of class QFeatures (type: bulk) with 1 set:
    ##
    ##  [1] peptides: SummarizedExperiment with 336 rows and 6 columns

As for TMT-based quantification, the PSM-level quantification is included in the tmt.tsv file.

    dataSageTMT <-
        Christoforou_2016_TMT_DDA_sage_tmt.tsv() |>
        read.delim()

Upon inspection, we can see that peptide identification information is missing in this file:

    colnames(dataSageTMT)

    ##  [1] "filename"           "scannr"             "ion_injection_time"
    ##  [4] "tmt_1"              "tmt_2"              "tmt_3"
    ##  [7] "tmt_4"              "tmt_5"              "tmt_6"
    ## [10] "tmt_7"              "tmt_8"              "tmt_9"
    ## [13] "tmt_10"

We can source this from the main results.sage.tsv file and append this information to the quantification data frame. We also extract the indices of the quantification columns before loading the data using the `readQFeatures()` function.

    dataSageTMTident <-
        Christoforou_2016_TMT_DDA_sage_results.sage.tsv() |>
        read.delim()

    dataSageTMTfinal <- merge(dataSageTMT, dataSageTMTident, by = c('filename', 'scannr'))

    (i <- grep('tmt_', colnames(dataSageTMTfinal), fixed = TRUE))

    ##  [1]  4  5  6  7  8  9 10 11 12 13

    colnames(dataSageTMTfinal)[i]

    ##  [1] "tmt_1"  "tmt_2"  "tmt_3"  "tmt_4"  "tmt_5"  "tmt_6"  "tmt_7"  "tmt_8"
    ##  [9] "tmt_9"  "tmt_10"

    qfSageTMT <- readQFeatures(dataSageTMTfinal, quantCols = i)
    qfSageTMT

    ## An instance of class QFeatures (type: bulk) with 1 set:
    ##
    ##  [1] quants: SummarizedExperiment with 251 rows and 10 columns

A more straightforward way is to use the `sager::sageQFeatures()` function from the BiocStyle::Githubpkg(“UCLouvain-CBIO/sager”) package to quickly load TMT quantification data from both .tsv output files into a `QFeatures` object.

    sager::sageQFeatures(
               Christoforou_2016_TMT_DDA_sage_tmt.tsv(),
               Christoforou_2016_TMT_DDA_sage_results.sage.tsv())

## 6 FragPipe

FragPipe produces several outputs. The following code block shows the processing of a label-free multi-set experiment.

First we can load the psm.tsv files that are produced separately for each sample-biological replicate combination as specified during FragPipe configuration. In our case, there has been a separate file created for each run.

We start by extracting the relevant filenames from `MsDataHub`.

    fls <- MsDataHub() |>
        dplyr::filter(grepl("2022_LFQ_DDA_FragPipe", Title)) |>
        dplyr::pull(1)
    fls

    ## [1] "vanPuyvelde_2022_LFQ_DDA_FragPipe_A_1_psm.tsv"
    ## [2] "vanPuyvelde_2022_LFQ_DDA_FragPipe_A_2_psm.tsv"
    ## [3] "vanPuyvelde_2022_LFQ_DDA_FragPipe_A_3_psm.tsv"
    ## [4] "vanPuyvelde_2022_LFQ_DDA_FragPipe_B_1_psm.tsv"
    ## [5] "vanPuyvelde_2022_LFQ_DDA_FragPipe_B_2_psm.tsv"
    ## [6] "vanPuyvelde_2022_LFQ_DDA_FragPipe_B_3_psm.tsv"

Below, we iterate over each filename, convert it to a function call that we then evaluate, and then load as a `SummarizedExperiment`. The code below produces a list of `SummarizedExperiment` instances, that we then name using the initial filenames.

    lst <- lapply(fls, function(fl) {
        call(fl) |>
            eval() |>
            read.delim() |>
            readSummarizedExperiment(quantCols = "Intensity")
    })

    names(lst) <- fls

We can now pass this list to the `QFeatures` constructor to create a `QFeatures` object.

    qfFpipeLFQ <- QFeatures(lst)
    qfFpipeLFQ

    ## An instance of class QFeatures (type: bulk) with 6 sets:
    ##
    ##  [1] vanPuyvelde_2022_LFQ_DDA_FragPipe_A_1_psm.tsv: SummarizedExperiment with 385 rows and 1 columns
    ##  [2] vanPuyvelde_2022_LFQ_DDA_FragPipe_A_2_psm.tsv: SummarizedExperiment with 392 rows and 1 columns
    ##  [3] vanPuyvelde_2022_LFQ_DDA_FragPipe_A_3_psm.tsv: SummarizedExperiment with 452 rows and 1 columns
    ##  [4] vanPuyvelde_2022_LFQ_DDA_FragPipe_B_1_psm.tsv: SummarizedExperiment with 360 rows and 1 columns
    ##  [5] vanPuyvelde_2022_LFQ_DDA_FragPipe_B_2_psm.tsv: SummarizedExperiment with 356 rows and 1 columns
    ##  [6] vanPuyvelde_2022_LFQ_DDA_FragPipe_B_3_psm.tsv: SummarizedExperiment with 379 rows and 1 columns

The names of the assays are based on the (rather long) filenames, they were derived from. We can shorten these:

    names(qfFpipeLFQ) <- sub('vanPuyvelde_2022_LFQ_DDA_FragPipe_(\\w_\\d_psm)\\.tsv', '\\1', names(qfFpipeLFQ))
    qfFpipeLFQ

    ## An instance of class QFeatures (type: bulk) with 6 sets:
    ##
    ##  [1] A_1_psm: SummarizedExperiment with 385 rows and 1 columns
    ##  [2] A_2_psm: SummarizedExperiment with 392 rows and 1 columns
    ##  [3] A_3_psm: SummarizedExperiment with 452 rows and 1 columns
    ##  [4] B_1_psm: SummarizedExperiment with 360 rows and 1 columns
    ##  [5] B_2_psm: SummarizedExperiment with 356 rows and 1 columns
    ##  [6] B_3_psm: SummarizedExperiment with 379 rows and 1 columns

The processing of peptide and protein-level outputs is similar to MaxQuant processing above.

### 6.1 TMT

In the following section, we demonstrate the processing of TMT-labelled multi-set experiment. It consists of two runs named Fraction1 and Fraction2. Just like in the case of a label-free experiment, there is a separate psm.tsv file produced for each run:

    fls <- MsDataHub() |>
        dplyr::filter(grepl("Christoforou_2016_TMT_DDA_FragPipe_Fraction", Title)) |>
        dplyr::pull(1)

    lst <- lapply(fls,
           function(fl) {
               x <- eval(call(fl)) |>
                   read.delim()
               i <- grep('Intensity\\.', colnames(x))
               readSummarizedExperiment(x, quantCols = i)
           })

    names(lst) <- fls
    QFeatures(lst)

    ## An instance of class QFeatures (type: bulk) with 2 sets:
    ##
    ##  [1] Christoforou_2016_TMT_DDA_FragPipe_Fraction1_psm.tsv: SummarizedExperiment with 134 rows and 10 columns
    ##  [2] Christoforou_2016_TMT_DDA_FragPipe_Fraction2_psm.tsv: SummarizedExperiment with 166 rows and 10 columns