This function allows importing protein identification and quantification results from Fragpipe
which were previously exported as tabulated text (tsv). Quantification data and other relevant information will be extracted similar like the other import-functions from this package.
The final output is a list containing the elements: $annot, $raw and $quant, or a data.frame with the quantication data and a part of the annotation if argument separateAnnot=FALSE.
readFragpipeFile(
fileName,
path = NULL,
normalizeMeth = "median",
sampleNames = NULL,
read0asNA = TRUE,
quantCol = "Intensity$",
annotCol = NULL,
refLi = NULL,
separateAnnot = TRUE,
FDRCol = list("Protein.Probability", lim = 0.99),
groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
plotGraph = TRUE,
titGraph = "FragPipe",
wex = 1.6,
specPref = c(conta = "CON_|LYSC_CHICK", mainSpecies = "OS=Homo sapiens"),
gr = NULL,
sdrf = NULL,
suplAnnotFile = FALSE,
silent = FALSE,
debug = FALSE,
callFrom = NULL
)This function returns a list with $raw (initial/raw abundance values), $quant with final normalized quantitations, $annot, $counts an array with number of peptides, $quantNotes
and $notes; or if separateAnnot=FALSE the function returns a data.frame with annotation and quantitation only
(character) name of file to be read
(character) path of file to be read
(character) normalization method, defaults to median, for more details see normalizeThis)
(character) custom column-names for quantification data; this argument has priority over suplAnnotFile
(logical) decide if initial quntifications at 0 should be transformed to NA (thus avoid -Inf in log2 results)
(character or integer) exact col-names, or if length=1 content of quantCol will be used as pattern to search among column-names for $quant using grep
(character) column names to be read/extracted for the annotation section (default c("Accession","Description","Gene","Contaminant","Sum.PEP.Score","Coverage....","X..Peptides","X..PSMs","X..Unique.Peptides", "X..AAs","MW..kDa.") )
(character or integer) custom specify which line of data is main species, if character (eg 'mainSpe'), the column 'SpecType' in $annot will be searched for exact match of the (single) term given
(logical) if TRUE output will be organized as list with $annot, $abund for initial/raw abundance values and $quant with final log2 (normalized) quantitations
(list) optional indication to search for protein FDR information
(list) additional parameters for interpreting meta-data to identify structure of groups (replicates), will be passed to readSampleMetaData.
May contain lowNumberOfGroups=FALSE for automatically choosing a rather elevated number of groups if possible (defaults to low number of groups, ie higher number of samples per group)
May contain chUnit (logical or character) to be passed to readSampleMetaData() for (optional) adjustig of unit-prefixes in meta-data group labels, in case multiple different unit-prefixes
are used (eg '100pMol' and '1nMol').
(logical or integer) optional plot of type vioplot of initial and normalized data (using normalizeMeth); if integer, it will be passed to layout when plotting
(character) custom title to plot of distribution of quantitation values
(integer) relative expansion factor of the violin-plot (will be passed to vioplotW)
(character or list) define characteristic text for recognizing (main) groups of species (1st for comtaminants - will be marked as 'conta', 2nd for main species- marked as 'mainSpe',
and optional following ones for supplemental tags/species - maked as 'species2','species3',...);
if list and list-element has multiple values they will be used for exact matching of accessions (ie 2nd of argument annotCol)
(character or factor) custom defined pattern of replicate association, will override final grouping of replicates from sdrf and/or suplAnnotFile (if provided)
(character, list or data.frame) optional extraction and adding of experimenal meta-data: if character, this may be the ID at ProteomeExchange,
the second & third elements may give futher indicatations for automatic organization of groups of replicates.
Besides, the output from readSdrf or a list from defineSamples may be provided;
if gr is provided, gr gets priority for grouping of replicates;
if sdrfOrder=TRUE the output will be put in order of sdrf
(logical or character) optional reading of supplemental files; however, if gr is provided, gr gets priority for grouping of replicates;
if character the respective file-name (relative or absolute path)
(logical) suppress messages
(logical) additional messages for debugging
(character) allow easier tracking of messages produced
This function has been developed using Fragpipe versions 18.0 and 19.0.
Using the argument suplAnnotFile it is possible to specify a specific file (or search for default file) to read for extracting file-names as sample-names and other experiment related information.
read.table, normalizeThis) , readMaxQuantFile, readProtDiscovFile, readProlineFile
FPproFi1 <- "tinyFragpipe1.tsv.gz"
path1 <- system.file("extdata", package="wrProteo")
## let's define the main species and allow tagging some contaminants
specPref1 <- c(conta="conta|CON_|LYSC_CHICK", mainSpecies="MOUSE")
dataFP <- readFragpipeFile(path1, file=FPproFi1, specPref=specPref1, tit="Tiny Fragpipe Data")
summary(dataFP$quant)
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