readQFeaturesFromDIANN: Read DIA-NN output as a QFeatures objects
In lgatto/Features: Quantitative features for mass spectrometry data

readQFeaturesFromDIANN

R Documentation

Read DIA-NN output as a QFeatures objects

Description

This function takes the Report.tsv output files from DIA-NN and converts them into a multi-set QFeatures object. It is a wrapper around readQFeatures() with default parameters set to match DIA-NN label-free and plexDIA report files: default runCol is "File.Name" and default quantColsis"Ms1.Area"'.

Usage

readQFeaturesFromDIANN(
  assayData,
  colData = NULL,
  quantCols = "Ms1.Area",
  runCol = "File.Name",
  multiplexing = c("none", "mTRAQ"),
  extractedData = NULL,
  ecol = NULL,
  verbose = TRUE,
  ...
)

Arguments

`assayData`	A `data.frame`, or any object that can be coerced into a `data.frame`, holding the quantitative assay. For `readSummarizedExperiment()`, this can also be a `character(1)` pointing to a filename. This `data.frame` is typically generated by an identification and quantification software, such as Sage, Proteome Discoverer, MaxQuant, ...
`colData`	A `data.frame` (or any object that can be coerced to a `data.frame`) containing sample/column annotations, including `quantCols` and `runCol` (see details).
`quantCols`	A `numeric()`, `logical()` or `character()` defining the columns of the `assayData` that contain the quantitative data. This information can also be defined in `colData` (see details).
`runCol`	For the multi-set case, a `numeric(1)` or `character(1)` pointing to the column of `assayData` (and `colData`, is set) that contains the runs/batches. Make sure that the column name in both tables are identical and syntactically valid (if you supply a `character`) or have the same index (if you supply a `numeric`). Note that characters are converted to syntactically valid names using `make.names`
`multiplexing`	A `character(1)` indicating the type of multiplexing used in the experiment. One of `"none"` (default, for label-free experiments) or `"mTRAQ"` (for plexDIA experiments).
`extractedData`	A `data.frame` or any object that can be coerced to a `data.frame` that contains the data from the `⁠*_ms1_extracted.tsv⁠` file generated by DIA-NN. This argument is optional and is currently only applicable for mTRAQ multiplexed experiments where DIA-NN was run using the `plexdia` module (see references).
`ecol`	Same as `quantCols`. Available for backwards compatibility. Default is `NULL`. If both `ecol` and `colData` are set, an error is thrown.
`verbose`	A `logical(1)` indicating whether the progress of the data reading and formatting should be printed to the console. Default is `TRUE`.
`...`	Further arguments passed to `readQFeatures()`.

Value

An instance of class QFeatures. The quantiative data of each acquisition run is stored in a separate set as a SummarizedExperiment object.

Author(s)

Laurent Gatto, Christophe Vanderaa

References

Derks, Jason, Andrew Leduc, Georg Wallmann, R. Gray Huffman, Matthew Willetts, Saad Khan, Harrison Specht, Markus Ralser, Vadim Demichev, and Nikolai Slavov. 2022. "Increasing the Throughput of Sensitive Proteomics by plexDIA." Nature Biotechnology, July. Link to article

Examples


x <- read.delim(MsDataHub::benchmarkingDIA.tsv())
x[["File.Name"]] <- x[["Run"]]

#################################
## Label-free multi-set case

## using default arguments
readQFeaturesFromDIANN(x)

## with a colData (and default arguments)
cd <- data.frame(sampleInfo = LETTERS[1:24],
                 quantCols = "Ms1.Area",
                 runCol = unique(x[["File.Name"]]))
readQFeaturesFromDIANN(x, colData = cd)

#################################
## mTRAQ multi-set case

x2 <- read.delim(MsDataHub::Report.Derks2022.plexDIA.tsv())
x2[["File.Name"]] <- x2[["Run"]]
readQFeaturesFromDIANN(x2, multiplexing = "mTRAQ")

lgatto/Features documentation built on Sept. 22, 2024, 7:13 p.m.