readQFeatures: QFeatures from tabular data

In rformassspectrometry/QFeatures: Quantitative features for mass spectrometry data

QFeatures from tabular data

Description

These functions convert tabular data into dedicated data objets. The readSummarizedExperiment() function takes a file name or data.frame and converts it into a SummarizedExperiment() object. The readQFeatures() function takes a data.frame and converts it into a QFeatures object (see QFeatures() for details). For the latter, two use-cases exist:

• The single-set case will generate a QFeatures object with a single SummarizedExperiment containing all features of the input table.

• The multi-set case will generate a QFeatures object containing multiple SummarizedExperiments, resulting from splitting the input table. This multi-set case is generally used when the input table contains data from multiple runs/batches.

Usage

readSummarizedExperiment(
  assayData,
  quantCols = NULL,
  fnames = NULL,
  ecol = NULL,
  ...
)

readQFeatures(
  assayData,
  colData = NULL,
  quantCols = NULL,
  runCol = NULL,
  name = "quants",
  removeEmptyCols = FALSE,
  verbose = TRUE,
  ecol = NULL,
  ...
)

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, ...
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).
fnames	For the single- and multi-set cases, an optional character(1) or numeric(1) indicating the column to be used as feature names. Note that rownames must be unique within QFeatures sets.
ecol	Same as quantCols. Available for backwards compatibility. Default is NULL. If both ecol and colData are set, an error is thrown.
...	Further arguments that can be passed on to read.csv() except stringsAsFactors, which is always FALSE. Only applicable to readSummarizedExperiment().
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).
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
name	For the single-set case, an optional character(1) to name the set in the QFeatures object. Default is quants.
removeEmptyCols	A logical(1). If TRUE, quantitative columns that contain only missing values are removed.
verbose	A logical(1) indicating whether the progress of the data reading and formatting should be printed to the console. Default is TRUE.

Details

The single- and multi-set cases are defined by the quantCols and runCol parameters, whether passed by the quantCols and runCol vectors and/or the colData data.frame (see below).

Single-set case

The quantitative data variables are defined by the quantCols. The single-set case can be represented schematically as shown below.

|------+----------------+-----------|
| cols | quantCols 1..N | more cols |
| .    | ...            | ...       |
| .    | ...            | ...       |
| .    | ...            | ...       |
|------+----------------+-----------|

Note that every quantCols column contains data for a single sample. The single-set case is defined by the absence of any runCol input (see next section). We here provide a (non-exhaustive) list of typical data sets that fall under the single-set case:

• Peptide- or protein-level label-free data (bulk or single-cell).

• Peptide- or protein-level multiplexed (e.g. TMT) data (bulk or single-cell).

• PSM-level multiplexed data acquired in a single MS run (bulk or single-cell).

• PSM-level data from fractionation experiments, where each fraction of the same sample was acquired with the same multiplexing label.

Multi-set case

A run/batch variable, runCol, is required to import multi-set data. The multi-set case can be represented schematically as shown below.

|--------+------+----------------+-----------|
| runCol | cols | quantCols 1..N | more cols |
|   1    | .    | ...            | ...       |
|   1    | .    | ...            | ...       |
|--------+------+----------------+-----------|
|   2    | .    | ...            | ...       |
|--------+------+----------------+-----------|
|   .    | .    | ...            | ...       |
|--------+------+----------------+-----------|

Every quantCols column contains data for multiple samples acquired in different runs. The multi-set case applies when runCol is provided, which will determine how the table is split into multiple sets.

We here provide a (non-exhaustive) list of typical data sets that fall under the multi-set case:

• PSM- or precursor-level multiplexed data acquired in multiple runs (bulk or single-cell)

• PSM- or precursor-level label-free data acquired in multiple runs (bulk or single-cell)

• DIA-NN data (see also readQFeaturesFromDIANN()).

Adding sample annotations with colData

We recommend providing sample annotations when creating a QFeatures object. The colData is a table in which each row corresponds to a sample and each column provides information about the samples. There is no restriction on the number of columns and on the type of data they should contain. However, we impose one or two columns (depending on the use case) that allow to link the annotations of each sample to its quantitative data:

• Single-set case: the colData must contain a column named quantCols that provides the names of the columns in assayData containing quantitative values for each sample (see single-set cases in the examples).

• Multi-set case: the colData must contain a column named quantCols that provides the names of the columns in assayData with the quantitative values for each sample, and a column named runCol that provides the MS runs/batches in which each sample has been acquired. The entries in colData[["runCol"]] are matched against the entries provided by assayData[[runCol]].

When the quantCols argument is not provided to readQFeatures(), the function will automatically determine the quantCols from colData[["quantCols"]]. Therefore, quantCols and colData cannot be both missing.

Samples that are present in assayData but absent colData will lead to a warning, and the missing entries will be automatically added to the colData and filled with NAs.

When using the quantCols and runCol arguments only (without colData), the colData contains zero columns/variables.

Value

An instance of class QFeatures or SummarizedExperiment::SummarizedExperiment(). For the former, the quantitative sets of each run are stored in SummarizedExperiment::SummarizedExperiment() object.

Author(s)

Laurent Gatto, Christophe Vanderaa

See Also

• The QFeatures (see QFeatures()) class to read about how to manipulate the resulting QFeatures object.

• The readQFeaturesFromDIANN() function to import DIA-NN quantitative data.

Examples

######################################
## Single-set case.

## Load a data.frame with PSM-level data
data(hlpsms)
hlpsms[1:10, c(1, 2, 10:11, 14, 17)]

## Create a QFeatures object with a single psms set
qf1 <- readQFeatures(hlpsms, quantCols = 1:10, name = "psms")
qf1
colData(qf1)

######################################
## Single-set case with colData.

(coldat <- data.frame(var = rnorm(10),
                      quantCols = names(hlpsms)[1:10]))
qf2 <- readQFeatures(hlpsms, colData = coldat)
qf2
colData(qf2)

######################################
## Multi-set case.

## Let's simulate 3 different files/batches for that same input
## data.frame, and define a colData data.frame.

hlpsms$file <- paste0("File", sample(1:3, nrow(hlpsms), replace = TRUE))
hlpsms[1:10, c(1, 2, 10:11, 14, 17, 29)]

qf3 <- readQFeatures(hlpsms, quantCols = 1:10, runCol = "file")
qf3
colData(qf3)


######################################
## Multi-set case with colData.

(coldat <- data.frame(runCol = rep(paste0("File", 1:3), each = 10),
                      var = rnorm(10),
                      quantCols = names(hlpsms)[1:10]))
qf4 <- readQFeatures(hlpsms, colData = coldat, runCol = "file")
qf4
colData(qf4)

rformassspectrometry/QFeatures documentation built on Sept. 18, 2024, 10:39 p.m.