Package 'QFeaturesGUI'

A shiny app to import QFeatures objects.

Description

importQFeatures is a simple graphical interface to import bulk and single-cell proteomics data. The app uses the readQFeatures function from the QFeatures package to convert simple tables (single or multiple, CSV or TSV) to a QFeatures object. The app allows users to convert tables to a QFeatures object.

Usage

importQFeatures(colData = NULL, assayData = NULL, maxSize = 1000)

Arguments

colData	A data frame that contains the sample table.
assayData	A data frame that contains the input table.
maxSize	An integer that changes the shiny.maxRequestSize value, in MB.

Value

The "importQFeatures" Shiny app object.

Examples

library(QFeaturesGUI)

data("sampleTable")
data("inputTable")
app <- importQFeatures(colData = sampleTable, assayData = inputTable, maxSize = 100)

if (interactive()) {
    shiny::runApp(app)
}

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Example MaxQuant/SCoPE2 output

Description

A data.frame with 1088 observations and 139 variables, as produced by reading a MaxQuant output file with read.delim().

• Sequence: a character vector

• Length: a numeric vector

• Modifications: a character vector

• Modified.sequence: a character vector

• Deamidation..N..Probabilities: a character vector

• Oxidation..M..Probabilities: a character vector

• Deamidation..N..Score.Diffs: a character vector

• Oxidation..M..Score.Diffs: a character vector

• Acetyl..Protein.N.term.: a numeric vector

• Deamidation..N.: a numeric vector

• Oxidation..M.: a numeric vector

• Missed.cleavages: a numeric vector

• Proteins: a character vector

• Leading.proteins: a character vector

• protein: a character vector

• Gene.names: a character vector

• Protein.names: a character vector

• Type: a character vector

• Set: a character vector

• MS.MS.m.z: a numeric vector

• Charge: a numeric vector

• m.z: a numeric vector

• Mass: a numeric vector

• Resolution: a numeric vector

• Uncalibrated...Calibrated.m.z..ppm.: a numeric vector

• Uncalibrated...Calibrated.m.z..Da.: a numeric vector

• Mass.error..ppm.: a numeric vector

• Mass.error..Da.: a numeric vector

• Uncalibrated.mass.error..ppm.: a numeric vector

• Uncalibrated.mass.error..Da.: a numeric vector

• Max.intensity.m.z.0: a numeric vector

• Retention.time: a numeric vector

• Retention.length: a numeric vector

• Calibrated.retention.time: a numeric vector

• Calibrated.retention.time.start: a numeric vector

• Calibrated.retention.time.finish: a numeric vector

• Retention.time.calibration: a numeric vector

• Match.time.difference: a logical vector

• Match.m.z.difference: a logical vector

• Match.q.value: a logical vector

• Match.score: a logical vector

• Number.of.data.points: a numeric vector

• Number.of.scans: a numeric vector

• Number.of.isotopic.peaks: a numeric vector

• PIF: a numeric vector

• Fraction.of.total.spectrum: a numeric vector

• Base.peak.fraction: a numeric vector

• PEP: a numeric vector

• MS.MS.count: a numeric vector

• MS.MS.scan.number: a numeric vector

• Score: a numeric vector

• Delta.score: a numeric vector

• Combinatorics: a numeric vector

• Intensity: a numeric vector

• Reporter.intensity.corrected.0: a numeric vector

• Reporter.intensity.corrected.1: a numeric vector

• Reporter.intensity.corrected.2: a numeric vector

• Reporter.intensity.corrected.3: a numeric vector

• Reporter.intensity.corrected.4: a numeric vector

• Reporter.intensity.corrected.5: a numeric vector

• Reporter.intensity.corrected.6: a numeric vector

• Reporter.intensity.corrected.7: a numeric vector

• Reporter.intensity.corrected.8: a numeric vector

• Reporter.intensity.corrected.9: a numeric vector

• Reporter.intensity.corrected.10: a numeric vector

• RI1: a numeric vector

• RI2: a numeric vector

• RI3: a numeric vector

• RI4: a numeric vector

• RI5: a numeric vector

• RI6: a numeric vector

• RI7: a numeric vector

• RI8: a numeric vector

• RI9: a numeric vector

• RI10: a numeric vector

• RI11: a numeric vector

• Reporter.intensity.count.0: a numeric vector

• Reporter.intensity.count.1: a numeric vector

• Reporter.intensity.count.2: a numeric vector

• Reporter.intensity.count.3: a numeric vector

• Reporter.intensity.count.4: a numeric vector

• Reporter.intensity.count.5: a numeric vector

• Reporter.intensity.count.6: a numeric vector

• Reporter.intensity.count.7: a numeric vector

• Reporter.intensity.count.8: a numeric vector

• Reporter.intensity.count.9: a numeric vector

• Reporter.intensity.count.10: a numeric vector

• Reporter.PIF: a logical vector

• Reporter.fraction: a logical vector

• Reverse: a character vector

• Potential.contaminant: a logical vector

• id: a numeric vector

• Protein.group.IDs: a character vector

• Peptide.ID: a numeric vector

• Mod..peptide.ID: a numeric vector

• MS.MS.IDs: a character vector

• Best.MS.MS: a numeric vector

• AIF.MS.MS.IDs: a logical vector

• Deamidation..N..site.IDs: a numeric vector

• Oxidation..M..site.IDs: a logical vector

• remove: a logical vector

• dart_PEP: a numeric vector

• dart_qval: a numeric vector

• razor_protein_fdr: a numeric vector

• Deamidation..NQ..Probabilities: a logical vector

• Deamidation..NQ..Score.Diffs: a logical vector

• Deamidation..NQ.: a logical vector

• Reporter.intensity.corrected.11: a logical vector

• Reporter.intensity.corrected.12: a logical vector

• Reporter.intensity.corrected.13: a logical vector

• Reporter.intensity.corrected.14: a logical vector

• Reporter.intensity.corrected.15: a logical vector

• Reporter.intensity.corrected.16: a logical vector

• RI12: a logical vector

• RI13: a logical vector

• RI14: a logical vector

• RI15: a logical vector

• RI16: a logical vector

• Reporter.intensity.count.11: a logical vector

• Reporter.intensity.count.12: a logical vector

• Reporter.intensity.count.13: a logical vector

• Reporter.intensity.count.14: a logical vector

• Reporter.intensity.count.15: a logical vector

• Reporter.intensity.count.16: a logical vector

• Deamidation..NQ..site.IDs: a logical vector

• input_id: a logical vector

• rt_minus: a logical vector

• rt_plus: a logical vector

• mu: a logical vector

• muij: a logical vector

• sigmaij: a logical vector

• pep_new: a logical vector

• exp_id: a logical vector

• peptide_id: a logical vector

• stan_peptide_id: a logical vector

• exclude: a logical vector

• residual: a logical vector

• participated: a logical vector

• peptide: a character vector

Usage

data("inputTable")

Format

An object of class data.frame with 1361 rows and 149 columns.

Details

The dataset is a subset of the SCoPE2 dataset (version 2, Specht et al. 2019, BioRXiv). The input file evidence_unfiltered.csv was downloaded from a Google Drive repository. The MaxQuant evidence file was loaded and the data was cleaned (renaming columns, removing duplicate fields,...). MS runs that were selected in the scp1 dataset (see ?scp1) were kept along with a blank run. The data is stored as a data.frame.

See Also

readSCP() from the scp package for an example on how mqScpData is parsed into a QFeatures object.

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Launch a Shiny application to process QFeatures objects

Description

processQFeatures() launches an interactive Shiny application that allows users to visually configure and apply pre-processing workflows to a QFeatures object.

The input qfeatures can be provided as an in-memory QFeatures object, as a path to an .rds file containing one, or omitted. If omitted, the application prompts the user to upload a QFeatures object from an .rds file.

Usage

processQFeatures(
  qfeatures = NULL,
  initialSets = NULL,
  prefilledSteps = c("sampleFiltering", "featureFiltering", "missingValuesFeatures",
    "missingValuesSamples", "normalisation", "aggregation", "join", "aggregation"),
  maxSize = 100
)

Arguments

qfeatures	Optional QFeatures object to be processed, or a character string specifying the path to a .rds file containing a QFeatures object. If omitted, the app starts without processing steps and displays a startup modal.
initialSets	An integer, logical, or character vector specifying which assays (feature sets) should be used as the starting point for processing. If NULL and qfeatures is provided, all assays in qfeatures are used. If qfeatures is omitted, the user chooses the initial sets after uploading the .rds file.
prefilledSteps	A character vector specifying the initial workflow steps to display when the application launches. Steps must be provided using their internal identifiers (e.g. "sampleFiltering", "featureFiltering", "normalisation").
maxSize	An integer that changes the shiny.maxRequestSize value, in MB. This controls the maximum upload size for the startup .rds file upload modal.

Details

The application provides a drag-and-drop workflow builder that allows users to select, order, and configure processing steps such as filtering, normalization, and transformation. The configured workflow can then be applied to the selected assays.

Value

The processQFeatures Shiny application.

Examples

# the example should be launched with `ask = FALSE`

library(QFeatures)
library(QFeaturesGUI)

data("sampleTable")
data("inputTable")

qfeatures <- readQFeatures(
    inputTable,
    colData = sampleTable,
    runCol = "Raw.file"
)

app <- processQFeatures(
    qfeatures,
    initialSets = seq_along(qfeatures),
    maxSize = 100
)

if (interactive()) {
    shiny::runApp(app)
}

---

Single-cell sample annotation

Description

A data frame with 48 observations on the following 6 variables.

• Set: a character vector

• Channel: a character vector

• SampleType: a character vector

• lcbatch: a character vector

• sortday: a character vector

• digest: a character vector

Usage

data("sampleTable")

Format

An object of class data.frame with 64 rows and 6 columns.

Details

The dataset is a subset of the SCoPE2 dataset (version 2, Specht et al. 2019, BioRXiv). The input files batch.csv and annotation.csv were downloaded from a Google Drive repository. The two files were loaded and the column names were adapted for consistency with mqScpData table (see ?mqScpData). The two tables were filtered to contain only sets present in mqScpData. The tables were then merged based on the run ID, hence merging the sample annotation and the batch annotation. Finally, annotation for the blank run was added manually. The data is stored as a data.frame.

See Also

readSCP() from the scp package to see how this file is used.

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