View source: R/readQFeaturesFromDIANN.R
readQFeaturesFromDIANN | R Documentation |
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"'.
readQFeaturesFromDIANN(
assayData,
colData = NULL,
quantCols = "Ms1.Area",
runCol = "File.Name",
multiplexing = c("none", "mTRAQ"),
extractedData = NULL,
ecol = NULL,
verbose = TRUE,
...
)
assayData |
A |
colData |
A |
quantCols |
A |
runCol |
For the multi-set case, a |
multiplexing |
A |
extractedData |
A |
ecol |
Same as |
verbose |
A |
... |
Further arguments passed to |
An instance of class QFeatures
. The quantiative data of
each acquisition run is stored in a separate set as a
SummarizedExperiment
object.
Laurent Gatto, Christophe Vanderaa
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
The QFeatures
(see QFeatures()
) class to read about how to
manipulate the resulting QFeatures
object.
The readQFeatures()
function which this one depends on.
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")
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