Package 'wrProteo'

Description

This fuction returns the molecular mass based of main elements found in biology/proteomics as average and mono-isotopic mass. The result includes H, C, N, O, P, S, Se and the electrone. The values are bsed on http://www.ionsource.com/Card/Mass/mass.htm in ref to http://physics.nist.gov/Comp (as of 2019).

Usage

.atomicMasses()

Value

This function returns a numeric matrix with mass values

See Also

massDeFormula

Examples

.atomicMasses()

Description

This function allows checking presence of knitr and rmarkdown

Usage

.checkKnitrProt(tryF = FALSE)

Arguments

Value

This function returns a logical value

See Also

presenceFilt

Examples

.checkKnitrProt()

Description

This (low-level) function performs an additional/final chek & adjustments to sample-names after readSampleMetaData()

Usage

.checkSetupGroups(
  abund,
  setupSd,
  gr = NULL,
  sampleNames = NULL,
  quantMeth = NULL,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns an enlaged/updated list 'setupSd' (set setupSd$sampleNames, setupSd$groups)

See Also

used in readProtDiscovererFile, readMaxQuantFile, readProlineFile, readFragpipeFile

Examples

set.seed(2021)

Description

This (low-level) function allows accessing matrix with UniProt abbreviations for species frequently used in research. This information may be used to harmonize species descriptions or extract species information out of protein-names.

Usage

.commonSpecies()

Value

This function returns a 2-column matrix with species names

See Also

used eg in readProtDiscovererFile, readMaxQuantFile, readProlineFile, readFragpipeFile

Examples

.commonSpecies()

Description

This (low-level) function creates the column annot[,'SpecType'] which may help distinguishing different lines/proteins. This information may, for example, be used to normalize only to all proteins of a common backgroud matrix (species). In order to compare specPref a species-column will be added to the annotation (annot) - if not already present If $mainSpecies or $conta: match to annot[,"Species"], annot[,"EntryName"], annot[,"GeneName"], if length==1 grep in annot[,"Species"]

Usage

.extrSpecPref(
  specPref,
  annot,
  useColumn = c("Species", "EntryName", "GeneName", "Accession"),
  suplInp = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Different to readSampleMetaData this function also considers the main annotation as axtracted with main quantification data. For example, this function can complement protein annotation data if columns 'Accession','EntryName' or 'SpecType' are missing

Value

This function returns a matrix with additional column 'SpecType'

See Also

used in readProtDiscovererFile, readMaxQuantFile, readProlineFile, readFragpipeFile

Examples

annot1 <- cbind( Leading.razor.protein=c("sp|P00925|ENO2_YEAST",
  "sp|Q3E792|RS25A_YEAST", "sp|P09938|RIR2_YEAST", "sp|P09938|RIR2_YEAST",
  "sp|Q99186|AP2M_YEAST", "sp|P00915|CAH1_HUMAN"), 
  Species= rep(c("Saccharomyces cerevisiae","Homo sapiens"), c(5,1)))
specPref1 <- list(conta="CON_|LYSC_CHICK", 
  mainSpecies="OS=Saccharomyces cerevisiae", spike="P00915")   # MQ type
.extrSpecPref(specPref1, annot1, useColumn=c("Species","Leading.razor.protein"))

Description

This (lower-level) function allows to perfom the basic NA-imputaton. Note, at this point the information from argument gr is not used.

Usage

.imputeNA(
  dat,
  gr = NULL,
  impParam,
  exclNeg = TRUE,
  inclLowValMod = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list with $data and $datImp

See Also

for more complex treatment matrixNAneighbourImpute;

Examples

dat1 <- matrix(11:22, ncol=4)
dat1[3:4] <- NA
.imputeNA(dat1, impParam=c(mean(dat1, na.rm=TRUE), 0.1))

Description

This (low-level) function allows (generic) plotting of density distribution for quantitation import-functions

Usage

.plotQuantDistr(
  abund,
  quant,
  custLay = NULL,
  normalizeMeth = NULL,
  softNa = NULL,
  refLi = NULL,
  refLiIni = NULL,
  notLogAbund = NA,
  figMarg = c(3.5, 3.5, 3, 1),
  tit = NULL,
  las = NULL,
  cexAxis = 0.8,
  nameSer = NULL,
  cexNameSer = NULL,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns logical value (if data were valid for plotting) and produces a density dustribution figure (if data were found valid)

See Also

used in readProtDiscovererFile, readMaxQuantFile, readProlineFile, readFragpipeFile

Examples

set.seed(2018);  datT8 <- matrix(round(rnorm(800) +3,1), nc=8, dimnames=list(paste(
  "li",1:100,sep=""), paste(rep(LETTERS[1:3],c(3,3,2)),letters[18:25],sep="")))
.plotQuantDistr(datT8, quant=NULL, refLi=NULL, tit="Synthetic Data Distribution")

Description

Calculate molecular mass based on atomic composition

Usage

AAmass(massTy = "mono", inPept = TRUE, inclSpecAA = FALSE)

Arguments

Value

This function returns a vector with masses for all amino-acids (argument 'massTy' to switch from mono-isotopic to average mass)

See Also

massDeFormula, convToNum

Examples

massDeFormula(c("12H12O","HO"," 2H 1 Se, 6C 2N","HSeCN"," ","e"))
AAmass()

Description

This function calculates the AUC (area under the curve) from ROC data in matrix of specificity and sensitivity values, as provided in the output from summarizeForROC.

Usage

AucROC(
  dat,
  useCol = c("spec", "sens"),
  returnIfInvalid = NA,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix including imputed values or list of final and matrix with number of imputed by group (plus optional plot)

See Also

preparing ROC data summarizeForROC, (re)plot the ROC figure plotROC; note that numerous other packages also provide support for working with ROC-curves : Eg rocPkgShort, ROCR, pROC or ROCit, etc.

Examples

set.seed(2019); test1 <- list(annot=cbind(Species=c(rep("b",35), letters[sample.int(n=3,
  size=150,replace=TRUE)])), BH=matrix(c(runif(35,0,0.01), runif(150)), ncol=1))
roc1 <- summarizeForROC(test1, spec=c("a","b","c"), annotCol="Species")
AucROC(roc1)

Description

This function allows to manipulate sample-names (ie colnames of abundance data) in a batch-wise manner from data stored as multiple matrixes or data.frames of a list. Import functions such as readMaxQuantFile() organize initial flat files into lists (of matrixes) of the different types of data. Many times all column names in such lists carry long names including redundant information, like the overall experiment name or date, etc. The aim of this function is to facilitate 'cleaning' the sample- (ie column-) names to obtain short and concise names. Character terms to be removed (via argument rem) and/or replaced/subsitituted (via argument subst) should be given as they are, characters with special behaviour in grep (like '.') will be protected internally. Note, that the character substitution part will be done first, and the removal part (without character replacement) afterwards.

Usage

cleanListCoNames(
  dat,
  rem = NULL,
  subst = c("-", "_"),
  lstE = c("raw", "quant", "counts"),
  mathOper = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list (equivalent to input dat)

See Also

grep

Examples

dat1 <- matrix(1:12, ncol=4, dimnames=list(1:3, paste0("sample_R.",1:4)))
dat1 <- list(raw=dat1, quant=dat1, notes="other..")
cleanListCoNames(dat1, rem=c(left="sample_"), c(".","-"))

Description

In most omics data-analysis one needs to employ a certain number of filtering strategies to avoid getting artifacts to the step of statistical testing. combineMultFilterNAimput takes on one side the origial data and on the other side NA-imputed data to create several differnet filters and to finally combine them. A filter aiming to take away the least abundant values (using the imputede data) can be fine-tuned by the argument abundThr. This step compares the means for each group and line, at least one grou-mean has to be > the threshold (based on hypothesis that if all conditions represent extrememy low measures their diffrenetial may not be determined with certainty). In contrast, the filter addressing the number of missing values (NA) uses the original data, the arguments colTotNa,minSpeNo and minTotNo are used at this step. Basically, this step allows defining a minimum content of 'real' (ie non-NA) values for further considering the measurements as reliable. This part uses internally presenceFilt for filtering elevated content of NA per line. Finally, this function combines both filters (as matrix of FALSE and TRUE) on NA-imputed and original data and retruns a vector of logical values if corresponding lines passe all filter criteria.

Usage

combineMultFilterNAimput(
  dat,
  imputed,
  grp,
  annDat = NULL,
  abundThr = NULL,
  colRazNa = NULL,
  colTotNa = NULL,
  minSpeNo = 1,
  minTotNo = 2,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a vector of logical values if corresponding line passes filter criteria

See Also

presenceFilt

Examples

set.seed(2013)
datT6 <- matrix(round(rnorm(300)+3,1), ncol=6,
  dimnames=list(paste0("li",1:50), letters[19:24]))
datT6 <- datT6 +matrix(rep(1:nrow(datT6),ncol(datT6)), ncol=ncol(datT6))
datT6[6:7,c(1,3,6)] <- NA
datT6[which(datT6 < 11 & datT6 > 10.5)] <- NA
datT6[which(datT6 < 6 & datT6 > 5)] <- NA
datT6[which(datT6 < 4.6 & datT6 > 4)] <- NA
datT6b <- matrixNAneighbourImpute(datT6, gr=gl(2,3))
datT6c <- combineMultFilterNAimput(datT6, datT6b, grp=gl(2,3), abundThr=2)

Description

This function calculates the molecular mass of one-letter code amion-acid sequences.

Usage

convAASeq2mass(
  x,
  massTy = "mono",
  seqName = TRUE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

This functions returns a vector with masses for all amino-acids (argument 'massTy' to switch form mono-isotopic to average mass)

See Also

massDeFormula, AAmass, convToNum

Examples

convAASeq2mass(c("PEPTIDE","fPROTEINES"))
pep1 <- c(aa="AAAA", de="DEFDEF")
convAASeq2mass(pep1, seqN=FALSE)

Description

This function orders columns in list of matrixes (or matrix) according to argument sampNames and also offers an option for changing names of columns. It was (initially) designed to adjust/correct the order of samples after import using readMaxQuantFile(), readProteomeDiscovererFile() etc. The input may also be MArrayLM-type object from package limma or from functions moderTestXgrp or moderTest2grp.

Usage

corColumnOrder(
  dat,
  sampNames,
  replNames = NULL,
  useListElem = c("quant", "raw", "counts"),
  annotElem = "sampleSetup",
  newNames = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns an object of same class as input dat (ie matrix, list or MArrayLM-object from limma)

See Also

readMaxQuantFile, readProteomeDiscovererFile; moderTestXgrp or moderTest2grp

Examples

grp <- factor(rep(LETTERS[c(3,1,4)], c(2,3,3)))
dat1 <- matrix(1:15, ncol=5, dimnames=list(NULL,c("D","A","C","E","B")))
corColumnOrder(dat1, sampNames=LETTERS[1:5])

dat2 <- list(quant=dat1, raw=dat1)
dat2
corColumnOrder(dat2, sampNames=LETTERS[1:5])
corColumnOrder(dat2, sampNames=LETTERS[1:5], replNames=c("Dd","Aa","Cc","Ee","Bb"))

Compare in-silico digested proteomes for unique and shared peptides, counts per protein or as peptides Compare in-silico digested proteomes for unique and shared peptides, counts per protein or as peptides. The in-silico digestion may be performed separately using the package cleaver. Note: input must be list (or multiple names lists) of proteins with their respective peptides (eg by in-silico digestion).

Description

Compare in-silico digested proteomes for unique and shared peptides, counts per protein or as peptides

Compare in-silico digested proteomes for unique and shared peptides, counts per protein or as peptides. The in-silico digestion may be performed separately using the package cleaver. Note: input must be list (or multiple names lists) of proteins with their respective peptides (eg by in-silico digestion).

Usage

countNoOfCommonPeptides(
  ...,
  prefix = c("Hs", "Sc", "Ec"),
  sep = "_",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list with $byPep as list of logical matrixes for each peptide (as line) and unique/shared/etc for each species; $byProt as list of matrixes with count data per proten (as line) for each species; $tab with simple summary-type count data

See Also

readFasta2 and/or cleave-methods in package cleaver

Examples

## The example mimics a proteomics experiment where extracts form E coli and 
## Saccharomyces cerevisiae were mixed, thus not all peptdes may occur unique.  
(mi2 = countNoOfCommonPeptides(Ec=list(E1=letters[1:4],E2=letters[c(3:7)],
  E3=letters[c(4,8,13)],E4=letters[9]),Sc=list(S1=letters[c(2:3,6)], 
  S2=letters[10:13],S3=letters[c(5,6,11)],S4=letters[c(11)],S5="n")))
##  a .. uni E, b .. inteR, c .. inteR(+intra E), d .. intra E  (no4), e .. inteR, 
##  f .. inteR +intra E   (no6), g .. uni E, h .. uni E  no 8), i .. uni E, 
##  j .. uni S (no10), k .. intra S  (no11), l .. uni S (no12), m .. inteR  (no13)
lapply(mi2$byProt,head)
mi2$tab

Description

This function allows exporting objects created from wrProteo to the format of Wombat-P Wombat-P.

Usage

exportAsWombatP(
  wrProtObj,
  path = ".",
  combineFractions = "mean",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function creates a set of files (README.md, test_params.yml), plus a sud-directory containig file(s) (stand_prot_quant_method.csv); finally the function returns (NULL),

See Also

readMaxQuantFile, readProteomeDiscovererFile; moderTestXgrp or moderTest2grp

Examples

path1 <- system.file("extdata", package="wrProteo")
fiNa <- "proteinGroupsMaxQuant1.txt.gz"
specPr <- c(conta="conta|CON_|LYSC_CHICK", mainSpecies="YEAST", spike="HUMAN_UPS")
dataMQ <- readMaxQuantFile(path1, file=fiNa, specPref=specPr, tit="tiny MaxQuant")

exportAsWombatP(dataMQ, path=tempdir())

Description

Sample/experimental annotation meta-data form MaxQuant that was previously import can now be formatted in sdrf-style and exported using this function to write a draft-sdrf-file. Please note that this information will not _complete_ in respect to all information used in data-bases like Pride. Sdrf-files provide additional meta-information about samles and MS-runs in a standardized format, they may also be part of submissions to Pride.

Usage

exportSdrfDraft(
  lst,
  fileName = "sdrfDraft.tsv",
  correctFileExtension = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Gathering as much as possible information about samples and MS-runs requires that the additional files created from software, like MaxQuant using readMaxQuantFile, is present and was imported when calling the import-function (eg using the argument _suplAnnotFile=TRUE_). Please note that this functionality was designed for the case where no (external) sdrf-file is available. Thus, when data was imported including exteranl sdrf (uinsg the _sdrf=_ argument), exporting incomplete annotation-data from MaxQuant-produced files does not make any sense and therefore won't be possible.

After exporting the draft sdrf the user is advised to check and complete the information in the resulting file. Unfortunately, not all information present in a standard sdrf-file (like on Pride) cannot be gathered automatically, but key columns are already present and thus may facilitate completing. Please note, that the file-format has been defined as .tsv, thus columns/fields should be separated by tabs. At manual editing and completion, some editing- or tabulator-software may change the file-extesion to .tsv.txt, in this case the final files should be renamed as .tsv to remain compatible with Pride.

At this point only the import of data from MaxQuant via readMaxQuantFile has been developed to extract information for creating a draft-sdrf. Other data/file-import functions may be further developed to gather as much as possible equivalent information in the future.

Value

This function writes an Sdrf draft to file

See Also

This function may be used after reading/importig data by readMaxQuantFile in absence of sdrf

Examples

path1 <- system.file("extdata", package="wrProteo")
fiNaMQ <- "proteinGroups.txt.gz"
dataMQ <- readMaxQuantFile(path1, file=fiNaMQ, refLi="mainSpe", sdrf=FALSE, suplAnnotFile=TRUE)
## Here we'll write simply in the current temporary directory of this R-session
exportSdrfDraft(dataMQ, file.path(tempdir(),"testSdrf.tsv"))

Description

This function allows convenient access to results produced using the functions moderTest2grp or moderTestXgrp. The user can define the threshold which type of multiple testing correction should be used (as long as the multiple testing correction method was actually performed as part of testing).

Usage

extractTestingResults(
  stat,
  compNo = 1,
  statTy = "BH",
  thrsh = 0.05,
  FCthrs = 1.5,
  annotCol = c("Accession", "EntryName", "GeneName"),
  nSign = 6,
  addTy = c("allMeans"),
  filename = NULL,
  fileTy = "csvUS",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a limma-type MA-object (which can be handeled just like a list)

See Also

testRobustToNAimputation, moderTestXgrp or moderTest2grp

Examples

grp <- factor(rep(LETTERS[c(3,1,4)],c(2,3,3)))
set.seed(2017); t8 <- matrix(round(rnorm(208*8,10,0.4),2), ncol=8,
  dimnames=list(paste(letters[],rep(1:8,each=26),sep=""), paste(grp,c(1:2,1:3,1:3),sep="")))
t8[3:6,1:2] <- t8[3:6,1:2] +3                    # augment lines 3:6 (c-f) 
t8[5:8,c(1:2,6:8)] <- t8[5:8,c(1:2,6:8)] -1.5    # lower lines 
t8[6:7,3:5] <- t8[6:7,3:5] +2.2                  # augment lines 
## expect to find C/A in c,d,g, (h)
## expect to find C/D in c,d,e,f
## expect to find A/D in f,g,(h) 
library(wrMisc)     # for testing we'll use this package
test8 <- moderTestXgrp(t8, grp) 
extractTestingResults(test8)

Description

extrSpeciesAnnot identifies species-related annotation (as suffix to identifyers) for data comnining multiple species and returns alternative (short) names. This function also suppresses extra heading or tailing space or punctuation characters. In case multiple tags are found, the last tag is reported and a message of alert may be displayed.

Usage

extrSpeciesAnnot(
  annot,
  spec = c("_CONT", "_HUMAN", "_YEAST", "_ECOLI"),
  shortNa = c("cont", "H", "S", "E"),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a character vector with single (last of multiple) term if found in argument annot

See Also

grep

Examples

spec <- c("keratin_CONT","AB_HUMAN","CD_YEAST","EF_G_HUMAN","HI_HUMAN_ECOLI","_YEAST_012")
extrSpeciesAnnot(spec)

Description

NOTE : This function is deprecated, please use foldChangeArrow instead !! This function was made for adding an arrow indicating a fold-change to MA- or Volcano-plots. When comparing mutiple concentratios of standards in benchmark-tests it may be useful to indicate the expected ratio in a pair-wise comparison. In case of main input as list or MArrayLM-object (as generated from limma), the colum-names of multiple pairwise comparisons can be used for extracting a numeric content (supposed as concentrations in sample-names) which will be used to determine the expected ratio used for plotting. Optionally the ratio used for plotting can be returned as numeric value.

Usage

foldChangeArrow2(
  FC,
  useComp = 1,
  isLin = TRUE,
  asX = TRUE,
  col = 1,
  arr = c(0.005, 0.15),
  lwd = NULL,
  addText = c(line = -0.9, cex = 0.7, txt = "expected", loc = "toright"),
  returnRatio = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

The argument addText also allows specifying a fixed position when using addText=c(loc="bottomleft"), also bottomright, topleft, topright, toleft and toright may be used. In this case the elemts side and adjust will be redefined to accomodate the text in the corner specified.

Ultimately this function will be integated to the package wrGraph.

Value

plots arrow only (and explicative text), if returnRatio=TRUE also returns numeric value for extracted ratio

See Also

new version : foldChangeArrow; used with MAplotW, VolcanoPlotW

Examples

plot(rnorm(20,1.5,0.1),1:20)
#deprecated# foldChangeArrow2(FC=1.5)

Description

This function allows combining up to 3 separate data-sets previously imported using wrProteo.

Usage

fuseProteomicsProjects(
  x,
  y,
  z = NULL,
  columnNa = "Accession",
  NA.rm = TRUE,
  listNa = c(quant = "quant", annot = "annot"),
  all = FALSE,
  textModif = NULL,
  shortNa = NULL,
  retProtLst = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Some quantification software way give some identifyers multiple times, ie as multiple lines (eg for different modifictions or charge states, etc). In this case this function tries first to summarize all lines with identical identifyers (using the function combineRedundLinesInList which used by default the median value). Thus, it is very important to know your data and to understand when lines that appear with the same identifyers should/may be fused/summarized without doing damage to the later biological interpretation ! The user may specify for each dataset the colum out of the protein/peptide-annotation to use via the argument columnNa. Then, this content will be matched as identical match, so when combining data from different software special care shoud be taken !

Please note, that (at this point) the data from different series/objects will be joined as they are, ie without any additional normalization. It is up to the user to inspect the resulting data and to decide if and which type of normalization may be suitable !

Please do NOT try combining protein and peptide quntification data.

Value

This function returns a list with the same number of list-elements as $x, ie typically this contains : $raw (initial/raw abundance values), $quant with final normalized quantitations, $annot, optionally $counts an array with number of peptides, $quantNotes or $notes

See Also

sd

Examples

path1 <- system.file("extdata", package="wrProteo")
dataMQ <- readMaxQuantFile(path1, specPref=NULL, normalizeMeth="median")
MCproFi1 <- "tinyMC.RData"
dataMC <- readMassChroQFile(path1, file=MCproFi1, plotGraph=FALSE)
dataFused <- fuseProteomicsProjects(dataMQ, dataMC)
dim(dataMQ$quant)
dim(dataMC$quant)
dim(dataFused$quant)

Description

UPS1 (see https://www.sigmaaldrich.com/FR/en/product/sigma/ups1) and UPS2 are commerical products consisting of a mix of 48 human (purified) proteins. They are frequently used as standard in spike-in experiments, available from Sigma-Aldrich. This function allows accessing their protein accession numbers and associated names on UniProt

Usage

getUPS1acc(updated = TRUE)

Arguments

Details

Please note that the UniProt accession 'P62988' for 'UBIQ_HUMAN' (as originally cited by Sigma-Aldrich) has been withdrawn and replaced in 2010 by UniProt by the accessions 'P0CG47', 'P0CG48', 'P62979', and 'P62987'. This initial accession is available via getUPS1acc()$acOld, now getUPS1acc()$ac contains 'P0CG47'.

Value

This function returns data.frame with accession-numbers as stated by the supplier ($acFull), trimmed accession-numbers, ie without version numbers ($ac), and associated (UniProt) entry-names ($EntryName) from UniProt as well as the species designation for the collection of 48 human UPS1 or UPS2 proteins.

Examples

head(getUPS1acc())

Description

This function inspects its main argument to convert a species indication to the scientific name or to return all protein-accession numbers for a name of a standard collection like UPS1.

Usage

inspectSpeciesIndic(x, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a character vector

See Also

getUPS1acc;

Examples

inspectSpeciesIndic("Human")
inspectSpeciesIndic("UPS1")

Description

This functions extracts all replicate-values where at least one of the replicates is NA and sorts by number of NAs per group. A list with all NA-neighbours organized by the number of NAs gets returned.

Usage

isolNAneighb(mat, gr, silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a list with NA-neighbours sorted by number of NAs in replicate group

See Also

This function gets used by matrixNAneighbourImpute and testRobustToNAimputation; estimation of mode stableMode; detection of NAs na.fail

Examples

mat1 <- c(22.2, 22.5, 22.2, 22.2, 21.5, 22.0, 22.1, 21.7, 21.5, 22, 22.2, 22.7,
  NA, NA, NA, NA, NA, NA, NA, 21.2,   NA, NA, NA, NA,
  NA, 22.6, 23.2, 23.2,  22.4, 22.8, 22.8, NA,  23.3, 23.2, NA, 23.7,
  NA, 23.0, 23.1, 23.0,  23.2, 23.2, NA, 23.3,  NA, NA, 23.3, 23.8)
mat1 <- matrix(mat1, ncol=12, byrow=TRUE)
gr4 <- gl(3, 4)
isolNAneighb(mat1, gr4)

Description

Calculate molecular mass based on atomic composition

Usage

massDeFormula(
  comp,
  massTy = "mono",
  rmEmpty = FALSE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector with mass

See Also

convToNum

Examples

massDeFormula(c("12H12O","HO"," 2H 1 Se, 6C 2N","HSeCN"," ","e"))

Description

matrixNAinspect makes histograms of the full data and shows sub-population of NA-neighbour values. The aim of this function is to investigate the nature of NA values in matrix (of experimental measures) where replicate measurements are available. If a given element was measured twice, and one of these measurements revealed a NA while the other one gave a (finite) numeric value, the non-NA-value is considered a NA-neighbour. The subpopulation of these NA-neighbour values will then be highlighted in the resulting histogram. In a number of experimental settiongs some actual measurements may not meet an arbitrary defined baseline (as 'zero') or may be too low to be distinguishable from noise that associated measures were initially recorded as NA. In several types of measurments in proteomics and transcriptomics this may happen. So this fucntion allows to collect all NA-neighbour values and compare them to the global distribution of the data to investigate if NA-neighbours are typically very low values. In case of data with multiple replicates NA-neighbour values may be distinguished for the case of 2 NA per group/replicate-set. The resulting plots are typically used to decide if and how NA values may get replaced by imputed random values or wether measues containing NA-values should rather me omitted. Of course, such decisions do have a strong impact on further steps of data-analysis and should be performed with care.

Usage

matrixNAinspect(
  dat,
  gr = NULL,
  retnNA = TRUE,
  xLab = NULL,
  tit = NULL,
  xLim = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function produces a graphic (to the current graphical device)

See Also

hist, na.fail, naOmit

Examples

set.seed(2013)
datT6 <- matrix(round(rnorm(300)+3,1), ncol=6, 
  dimnames=list(paste("li",1:50,sep=""), letters[19:24]))
datT6 <- datT6 +matrix(rep(1:nrow(datT6),ncol(datT6)), ncol=ncol(datT6))
datT6[6:7,c(1,3,6)] <- NA
datT6[which(datT6 < 11 & datT6 > 10.5)] <- NA
datT6[which(datT6 < 6 & datT6 > 5)] <- NA
datT6[which(datT6 < 4.6 & datT6 > 4)] <- NA
matrixNAinspect(datT6, gr=gl(2,3))

Description

It is assumed that NA-values appear in data when quantitation values are very low (as this appears eg in quantitative shotgun proteomics). Here, the concept of (technical) replicates is used to investigate what kind of values appear in the other replicates next to NA-values for the same line/protein. Groups of replicate samples are defined via argument gr which descibes the columns of dat). Then, they are inspected for each line to gather NA-neighbour values (ie those values where NAs and regular measures are observed the same time). Eg, let's consider a line contains a set of 4 replicates for a given group. Now, if 2 of them are NA-values, the remaining 2 non-NA-values will be considered as NA-neighbours. Ultimately, the aim is to replaces all NA-values based on values from a normal distribution ressembling theire respective NA-neighbours.

Usage

matrixNAneighbourImpute(
  dat,
  gr,
  imputMethod = "mode2",
  retnNA = TRUE,
  avSd = c(0.15, 0.5),
  avSdH = NULL,
  NAneigLst = NULL,
  plotHist = c("hist", "mode"),
  xLab = NULL,
  xLim = NULL,
  yLab = NULL,
  yLim = NULL,
  tit = NULL,
  figImputDetail = TRUE,
  seedNo = NULL,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Details

By default a histogram gets plotted showing the initial, imputed and final distribution to check the global hypothesis that NA-values arose from very low measurements and to appreciate the impact of the imputed values to the overall final distribution.

There are a number of experimental settings where low measurements may be reported as NA. Sometimes an arbitrary defined baseline (as 'zero') may provoke those values found below being unfortunately reported as NA or as 0 (in case of MaxQuant). In quantitative proteomics (DDA-mode) the presence of numerous high-abundance peptides will lead to the fact that a number of less intense MS-peaks don't get identified properly and will then be reported as NA in the respective samples, while the same peptides may by correctly identified and quantified in other (replicate) samples. So, if a given protein/peptide gets properly quantified in some replicate samples but reported as NA in other replicate samples one may thus speculate that similar values like in the successful quantifications may have occored. Thus, imputation of NA-values may be done on the basis of NA-neighbours.

When extracting NA-neighbours, a slightly more focussed approach gets checked, too, the 2-NA-neighbours : In case a set of replicates for a given protein contains at least 2 non-NA-values (instead of just one) it will be considered as a (min) 2-NA-neighbour as well as regular NA-neighbour. If >300 of these (min) 2-NA-neighbours get found, they will be used instead of the regular NA-neighbours. For creating a collection of normal random values one may use directly the mode of the NA-neighbours (or 2-NA-neighbours, if >300 such values available). To do so, the first value of argument avSd must be set to NA. Otherwise, the first value avSd will be used as quantile of all data to define the mean for the imputed data (ie as quantile(dat, avSd[1], na.rm=TRUE)). The sd for generating normal random values will be taken from the sd of all NA-neighbours (or 2-NA-neighbours) multiplied by the second value in argument avSd (or avSd, if >300 2-NA-neighbours), since the sd of the NA-neighbours is usually quite high. In extremely rare cases it may happen that no NA-neighbours are found (ie if NAs occur, all replicates are NA). Then, this function replaces NA-values based on the normal random values obtained as dscribed above.

Value

This function returns a list with $data .. matrix of data where NA are replaced by imputed values, $nNA .. number of NA by group, $randParam .. parameters used for making random data

See Also

this function gets used by testRobustToNAimputation; estimation of mode stableMode; detection of NAs na.fail

Examples

set.seed(2013)
datT6 <- matrix(round(rnorm(300)+3,1), ncol=6, dimnames=list(paste("li",1:50,sep=""),
  letters[19:24]))
datT6 <- datT6 +matrix(rep(1:nrow(datT6), ncol(datT6)), ncol=ncol(datT6))
datT6[6:7, c(1,3,6)] <- NA
datT6[which(datT6 < 11 & datT6 > 10.5)] <- NA
datT6[which(datT6 < 6 & datT6 > 5)] <- NA
datT6[which(datT6 < 4.6 & datT6 > 4)] <- NA
datT6b <- matrixNAneighbourImpute(datT6, gr=gl(2,3))
head(datT6b$data)

Description

plotROC plots ROC curves based on results from summarizeForROC. This function plots only, it does not return any data. It allows printing simultaneously multiple ROC curves from different studies, it is also compatible with data from 3 species mix as in proteomics benchmark. Input can be prepared using moderTest2grp followed by summarizeForROC.

Usage

plotROC(
  dat,
  ...,
  useColumn = 2:3,
  methNames = NULL,
  col = NULL,
  pch = 1,
  bg = NULL,
  tit = NULL,
  xlim = NULL,
  ylim = NULL,
  point05 = 0.05,
  pointSi = 0.85,
  nByMeth = NULL,
  speciesOrder = NULL,
  txtLoc = NULL,
  legCex = 0.72,
  las = 1,
  addSuplT = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns only a plot with ROC curves

See Also

summarizeForROC, moderTest2grp

Examples

roc0 <- cbind(alph=c(2e-6,4e-5,4e-4,2.7e-3,1.6e-2,4.2e-2,8.3e-2,1.7e-1,2.7e-1,4.1e-1,5.3e-1,
	 6.8e-1,8.3e-1,9.7e-1), spec=c(1,1,1,1,0.957,0.915,0.915,0.809,0.702,0.489,0.362,0.234,
  0.128,0.0426), sens=c(0,0,0.145,0.942,2.54,2.68,3.33,3.99,4.71,5.87,6.67,8.04,8.77,
  9.93)/10, n.pos.a=c(0,0,0,0,2,4,4,9,14,24,36,41) )
plotROC(roc0)

Description

razorNoFilter filters based on either a) number of total peptides and specific peptides or b) numer of razor petides. This function was designed for filtering using a mimimum number of (PSM-) count values following the common practice to consider results with 2 or more peptide counts as reliable. The function be (re-)run independently on each of various questions (comparisons). Note: Non-integer data will be truncated to integer (equivalent to floor).

Usage

razorNoFilter(
  annot,
  speNa = NULL,
  totNa = NULL,
  minRazNa = NULL,
  minSpeNo = 1,
  minTotNo = 2,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a vector of logical values if corresponding line passes filter criteria

See Also

presenceFilt

Examples

set.seed(2019); datT <- matrix(sample.int(20,60,replace=TRUE), ncol=6,
  dimnames=list(letters[1:10], LETTERS[1:6])) -3
datT[,2] <- datT[,2] +2
datT[which(datT <0)] <- 0
razorNoFilter(datT, speNa="A", totNa="B")

Description

Protein quantification results from AlphaPept can be read using this function. Input files compressed as .gz can be read as well. The protein abundance values (XIC) get extracted. Since protein annotation is not very extensive with this format of data, the function allows reading the initial fasta files (from the directory above the quantitation-results) allowing to extract more protein-annotation (like species). Sample-annotation (if available) can be extracted from sdrf files, too. The protein abundance values may be normalized using multiple methods (median normalization as default), the determination of normalization factors can be restricted to specific proteins (normalization to bait protein(s), or to invariable matrix of spike-in experiments). The protein annotation data gets parsed to extract specific fields (ID, name, description, species ...). Besides, a graphical display of the distribution of protein abundance values may be generated before and after normalization.

Usage

readAlphaPeptFile(
  fileName = "results_proteins.csv",
  path = NULL,
  fasta = NULL,
  isLog2 = FALSE,
  normalizeMeth = "none",
  quantCol = "_LFQ$",
  contamCol = NULL,
  read0asNA = TRUE,
  refLi = NULL,
  sampleNames = NULL,
  specPref = NULL,
  extrColNames = NULL,
  remRev = TRUE,
  remConta = FALSE,
  separateAnnot = TRUE,
  gr = NULL,
  sdrf = NULL,
  suplAnnotFile = NULL,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  titGraph = NULL,
  wex = 1.6,
  plotGraph = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Meta-data describing the samples and experimental setup may be available from a sdrf-file (from the directory above the analysis/quantiication results) If available, the meta-data will be examined for determining groups of replicates and the results thereof can be found in $sampleSetup$levels. Alternatively, a dataframe formatted like sdrf-files (ie for each sample a separate line, see also function readSdrf) may be given, too.

This import-function has been developed using AlphaPept version x.x. The final output is a list containing these elements: $raw, $quant, $annot, $counts, $sampleSetup, $quantNotes, $notes, or (if separateAnnot=FALSE) data.frame with annotation- and main quantification-content. If sdrf information has been found, an add-tional list-element setup will be added containg the entire meta-data as setup$meta and the suggested organization as setup$lev.

Value

This function returns a list with $raw (initial/raw abundance values), $quant with final normalized quantitations, $annot (columns ), $counts an array with 'PSM' and 'NoOfRazorPeptides', $quantNotes, $notes and optional setup for meta-data from sdrf; or a data.frame with quantitation and annotation if separateAnnot=FALSE

See Also

read.table, normalizeThis) , readProteomeDiscovererFile; readProlineFile (and other import-functions), matrixNAinspect

Examples

path1 <- system.file("extdata", package="wrProteo")
# Here we'll load a short/trimmed example file
fiNaAP <- "tinyAlpaPeptide.csv.gz"
dataAP <- readAlphaPeptFile(file=fiNaAP, path=path1, tit="tiny AlphaPaptide ")
summary(dataAP$quant)

Description

This function allows importing protein identification and quantification results from DIA-NN. Data should be exported as tabulated text (tsv) as protein-groups (pg) to allow import by thus function. Quantification data and other relevant information will be parsed and extracted (similar to the other import-functions from this package). The final output is a list containing as (main) elements: $annot, $raw and $quant, or a data.frame with the quantication data and a part of the annotation if argument separateAnnot=FALSE.

Usage

readDiaNNFile(
  fileName,
  path = NULL,
  normalizeMeth = "median",
  sampleNames = NULL,
  read0asNA = TRUE,
  quantCol = "\\.raw$",
  annotCol = NULL,
  refLi = NULL,
  separateAnnot = TRUE,
  FDRCol = NULL,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  plotGraph = TRUE,
  titGraph = "DiaNN",
  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
)

Arguments

Details

This function has been developed using DIA-NN version 1.8.x. Note, reading gene-group (gg) files is in priciple possible, but resulting files typically lack protein-identifiers which may be less convenient in later steps of analysis. Thus, it is suggested to rather read protein-group (pg) files.

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.

Value

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

See Also

read.table, normalizeThis) , readMaxQuantFile, readProtDiscovFile, readProlineFile

Examples

diaNNFi1 <- "tinyDiaNN1.tsv.gz"   
## This file contains much less identifications than one may usually obtain
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="HUMAN")
dataNN <- readDiaNNFile(path1, file=diaNNFi1, specPref=specPref1, tit="Tiny DIA-NN Data")
summary(dataNN$quant)

Description

This function allows importing peptide identification and quantification results from DiaNN. Data should be exported as tabulated text (tsv) to allow import by thus function. 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 as (main) elements: $annot, $raw and $quant, or a data.frame with the quantication data and a part of the annotation if argument separateAnnot=FALSE.

Usage

readDiaNNPeptides(
  fileName,
  path = NULL,
  normalizeMeth = "median",
  sampleNames = NULL,
  read0asNA = TRUE,
  quantCol = "\\.raw$",
  annotCol = NULL,
  refLi = NULL,
  separateAnnot = TRUE,
  FDRCol = NULL,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  plotGraph = TRUE,
  titGraph = "DiaNN",
  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
)

Arguments

Details

This function has been developed using DiaNN version 1.8.x.

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.

Value

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

See Also

read.table, normalizeThis) , readMaxQuantFile, readProtDiscovFile, readProlineFile

Examples

diaNNFi1 <- "tinyDiaNN1.tsv.gz"
## This file contains much less identifications than one may usually obtain
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="HUMAN")
dataNN <- readDiaNNFile(path1, file=diaNNFi1, specPref=specPref1, tit="Tiny DIA-NN Data")
summary(dataNN$quant)

Description

Read fasta formatted file (from UniProt) to extract (protein) sequences and name. If tableOut=TRUE output may be organized as matrix for separating meta-annotation (eg uniqueIdentifier, entryName, proteinName, GN) in separate columns.

Usage

readFasta2(
  filename,
  delim = "|",
  databaseSign = c("sp", "tr", "generic", "gi"),
  removeEntries = NULL,
  tableOut = FALSE,
  UniprSep = c("OS=", "OX=", "GN=", "PE=", "SV="),
  strictSpecPattern = TRUE,
  cleanCols = TRUE,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns (depending on argument tableOut) a simple character vector (of sequences) with (entire) Uniprot annotation as name or b) a matrix with columns: 'database','uniqueIdentifier','entryName','proteinName','sequence' and further columns depending on argument UniprSep

See Also

writeFasta2 for writing as fasta; for reading scan or read.fasta from the package seqinr

Examples

## Tiny example with common contaminants
path1 <- system.file('extdata', package='wrProteo')
fiNa <-  "conta1.fasta.gz"
fasta1 <- readFasta2(file.path(path1, fiNa))
## now let's read and further separate annotation-fields
fasta2 <- readFasta2(file.path(path1, fiNa), tableOut=TRUE)
str(fasta1)

Description

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.

Usage

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
)

Arguments

Details

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.

Value

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

See Also

read.table, normalizeThis) , readMaxQuantFile, readProtDiscovFile, readProlineFile

Examples

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)

Description

This function allows importing initial petide identification and quantification results from Ionbot which were exported as tabulated tsv can be imported and relevant information extracted. The final output is a list containing 3 main elements: $annot, $raw and optional $quant, or returns data.frame with entire content of file if separateAnnot=FALSE.

Usage

readIonbotPeptides(
  fileName,
  path = NULL,
  normalizeMeth = "median",
  sampleNames = NULL,
  gr = NULL,
  sdrf = NULL,
  read0asNA = TRUE,
  quantCol = "^Abundances*",
  annotCol = NULL,
  contamCol = "Contaminant",
  refLi = NULL,
  separateAnnot = TRUE,
  FDRCol = list(c("^Protein.FDR.Confidence", "High"), c("^Found.in.Sample.", "High")),
  plotGraph = TRUE,
  suplAnnotFile = TRUE,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  titGraph = "Ionbot",
  wex = 1.6,
  specPref = c(conta = "CON_|LYSC_CHICK", mainSpecies = "OS=Homo sapiens"),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

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 realted information.

Value

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

See Also

/link[utils]{read.table}, /link{readMaxQuantFile}, /link{readProteomeDiscovererFile}, /link[wrMisc]{normalizeThis})

Examples

path1 <- system.file("extdata", package="wrProteo")
fiIonbot <- "tinyIonbotFile1.tsv.gz"
datIobPep <- readIonbotPeptides(fiIonbot, path=path1)

Description

Quantification results using MassChroQ should be initially treated using the R-package MassChroqR (both distributed by the PAPPSO at http://pappso.inrae.fr/) for initial normalization on peptide-level and combination of peptide values into protein abundances.

Usage

readMassChroQFile(
  fileName,
  path = NULL,
  normalizeMeth = "median",
  sampleNames = NULL,
  refLi = NULL,
  separateAnnot = TRUE,
  titGraph = "MassChroQ",
  wex = NULL,
  specPref = c(conta = "CON_|LYSC_CHICK", mainSpecies = "OS=Homo sapiens"),
  gr = NULL,
  sdrf = NULL,
  suplAnnotFile = FALSE,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  plotGraph = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

The final output of this fucntion is a list containing 3 elements: $annot, $raw, $quant and $notes, or returns data.frame with entire content of file if separateAnnot=FALSE. Other list-elements remain empty to keep format compatible to other import functions.

This function has been developed using MassChroQ version 2.2 and R-package MassChroqR version 0.4.0. Both are distributed by the PAPPSO (http://pappso.inrae.fr/). When saving quantifications generated in R as RData (with extension .rdata or .rda) using the R-packages associated with MassChroq, the ABUNDANCE_TABLE produced by mcq.get.compar(XICAB) should be used.

After import data get (re-)normalized according to normalizeMeth and refLi, and boxplots or vioplots drawn.

Value

This function returns 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

See Also

read.table, normalizeThis) , readProlineFile

Examples

path1 <- system.file("extdata", package="wrProteo")
fiNa <- "tinyMC.RData"
dataMC <- readMassChroQFile(file=fiNa, path=path1)

Description

Protein quantification results from MaxQuant can be read using this function and relevant information extracted. Input files compressed as .gz can be read as well. The protein abundance values (XIC), peptide counting information like number of unique razor-peptides or PSM values and sample-annotation (if available) can be extracted, too. The protein abundance values may be normalized using multiple methods (median normalization as default), the determination of normalization factors can be restricted to specific proteins (normalization to bait protein(s), or to invariable matrix of spike-in experiments). The protein annotation data gets parsed to extract specific fields (ID, name, description, species ...). Besides, a graphical display of the distribution of protein abundance values may be generated before and after normalization.

Usage

readMaxQuantFile(
  path,
  fileName = "proteinGroups.txt",
  normalizeMeth = "median",
  quantCol = "LFQ.intensity",
  contamCol = "Potential.contaminant",
  pepCountCol = c("Razor + unique peptides", "Unique peptides", "MS.MS.count"),
  read0asNA = TRUE,
  refLi = NULL,
  sampleNames = NULL,
  extrColNames = c("Majority.protein.IDs", "Fasta.headers", "Number.of.proteins"),
  specPref = c(conta = "conta|CON_|LYSC_CHICK", mainSpecies = "OS=Homo sapiens"),
  remRev = TRUE,
  remConta = FALSE,
  separateAnnot = TRUE,
  gr = NULL,
  sdrf = NULL,
  suplAnnotFile = NULL,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  titGraph = NULL,
  wex = 1.6,
  plotGraph = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

MaxQuant is proteomics quantification software provided by the MaxPlanck institute. By default MaxQuant writes the results of each run to the path combined/txt, from there (only) the files 'proteinGroups.txt' (main quantitation at protein level), 'summary.txt' and 'parameters.txt' will be used.

Meta-data describing the samples and experimental setup may be available from two sources : a) The file summary.txt which gets produced by MaxQuant in the same folder as the main quantification data. b) Furthermore, meta-data deposited as sdrf at Pride can be retreived (via the respective github page) when giving the accession number in argument sdrf. Then, the meta-data will be examined for determining groups of replicates and the results thereof can be found in $sampleSetup$levels. Alternatively, a dataframe formatted like sdrf-files (ie for each sample a separate line, see also function readSdrf) may be given. In tricky cases it is also possible to precise the column-name to use for defining the groups of replicates or the method for automatically choosing the most suited column via the 2nd value of the argument sdrf. Please note, that sdrf is still experimental and only a small fraction of proteomics-data on Pride have been annotated accordingly. If a valid sdrf is furnished, it's information has priority over the information extracted from the MaxQuant produced file summary.txt.

This import-function has been developed using MaxQuant versions 1.6.10.x to 2.0.x, the format of the resulting file 'proteinGroups.txt' is typically well conserved between versions. The final output is a list containing these elements: $raw, $quant, $annot, $counts, $sampleSetup, $quantNotes, $notes, or (if separateAnnot=FALSE) data.frame with annotation- and main quantification-content. If sdrf information has been found, an add-tional list-element setup will be added containg the entire meta-data as setup$meta and the suggested organization as setup$lev.

Value

This function returns a list with $raw (initial/raw abundance values), $quant with final normalized quantitations, $annot (columns ), $counts an array with 'PSM' and 'NoOfRazorPeptides', $quantNotes, $notes and optional setup for meta-data from sdrf; or a data.frame with quantitation and annotation if separateAnnot=FALSE

See Also

read.table, normalizeThis) , readProteomeDiscovererFile; readProlineFile (and other imprtfunctions), matrixNAinspect

Examples

path1 <- system.file("extdata", package="wrProteo")
# Here we'll load a short/trimmed example file (thus not the MaxQuant default name)
fiNa <- "proteinGroupsMaxQuant1.txt.gz"
specPr <- c(conta="conta|CON_|LYSC_CHICK", mainSpecies="YEAST", spike="HUMAN_UPS")
dataMQ <- readMaxQuantFile(path1, file=fiNa, specPref=specPr, tit="tiny MaxQuant")
summary(dataMQ$quant)
matrixNAinspect(dataMQ$quant, gr=gl(3,3))

Description

Peptide level identification and quantification data produced by MaxQuant can be read using this function and relevant information extracted. Input files compressed as .gz can be read as well. The peptide abundance values (XIC), peptide counting information and sample-annotation (if available) can be extracted, too.

Usage

readMaxQuantPeptides(
  path,
  fileName = "peptides.txt",
  normalizeMeth = "median",
  quantCol = "Intensity",
  contamCol = "Potential.contaminant",
  pepCountCol = "Experiment",
  refLi = NULL,
  sampleNames = NULL,
  extrColNames = c("Sequence", "Proteins", "Leading.razor.protein", "Start.position",
    "End.position", "Mass", "Missed.cleavages", "Unique..Groups.", "Unique..Proteins.",
    "Charges"),
  specPref = c(conta = "conta|CON_|LYSC_CHICK", mainSpecies = "HUMAN"),
  remRev = TRUE,
  remConta = FALSE,
  separateAnnot = TRUE,
  gr = NULL,
  sdrf = NULL,
  suplAnnotFile = NULL,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  titGraph = NULL,
  wex = 1.6,
  plotGraph = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

The peptide annotation data gets parsed to extract specific fields (ID, name, description, species ...). Besides, a graphical display of the distribution of peptide abundance values may be generated before and after normalization.

MaxQuant is proteomics quantification software provided by the MaxPlanck institute. By default MaxQuant write the results of each run to the path combined/txt, from there (only) the files 'peptides.txt' (main quantitation at peptide level), 'summary.txt' and 'parameters.txt' will be used for this function.

Meta-data describing the samples and experimental setup may be available from two sources : a) The file summary.txt which gets produced by MaxQuant in the same folder as the main quantification data. b) Furthermore, meta-data deposited as sdrf at Pride can be retreived (via the respective github page) when giving the accession number in argument sdrf. Then, the meta-data will be examined for determining groups of replicates and the results thereof can be found in $sampleSetup$levels. Alternatively, a dataframe formatted like sdrf-files (ie for each sample a separate line, see also function readSdrf) may be given. In tricky cases it is also possible to precise the column-name to use for defining the groups of replicates or the method for automatically choosing the most suited column via the 2nd value of the argument sdrf, see also the function defineSamples (which gets used internally). Please note, that sdrf is still experimental and only a small fraction of proteomics-data on Pride have been annotated accordingly. If a valid sdrf is furnished, it's information has priority over the information extracted from the MaxQuant produced file summary.txt.

This function has been developed using MaxQuant versions 1.6.10.x to 2.0.x, the format of the resulting file 'peptides.txt' is typically well conserved between versions. The final output is a list containing these elements: $raw, $quant, $annot, $counts, $sampleSetup, $quantNotes, $notes, or (if separateAnnot=FALSE) data.frame with annotation- and main quantification-content. If sdrf information has been found, an add-tional list-element setup will be added containg the entire meta-data as setup$meta and the suggested organization as setup$lev.

Value

This function returns a list with $raw (initial/raw abundance values), $quant with final normalized quantitations, $annot (columns ), $counts an array with 'PSM' and 'NoOfRazorPeptides', $quantNotes, $notes and optional setup for meta-data from sdrf; or a data.frame with quantitation and annotation if separateAnnot=FALSE

See Also

read.table, normalizeThis), for reading protein level readMaxQuantFile, readProlineFile

Examples

# Here we'll load a short/trimmed example file (thus not the MaxQuant default name)
MQpepFi1 <- "peptides_tinyMQ.txt.gz"
path1 <- system.file("extdata", package="wrProteo")
specPref1 <- c(conta="conta|CON_|LYSC_CHICK", mainSpecies="YEAST", spec2="HUMAN")
dataMQpep <- readMaxQuantPeptides(path1, file=MQpepFi1, specPref=specPref1,
  tit="Tiny MaxQuant Peptides")
summary(dataMQpep$quant)

Description

Protein quantification results form OpenMS which were exported as .csv can be imported and relevant information extracted. Peptide data get summarized by protein by top3 or sum methods. The final output is a list containing the elements: $annot, $raw, $quant ie normaized final quantifications, or returns data.frame with entire content of file if separateAnnot=FALSE.

Usage

readOpenMSFile(
  fileName = NULL,
  path = NULL,
  normalizeMeth = "median",
  refLi = NULL,
  sampleNames = NULL,
  quantCol = "Intensity",
  sumMeth = "top3",
  minPepNo = 1,
  protNaCol = "ProteinName",
  separateAnnot = TRUE,
  plotGraph = TRUE,
  tit = "OpenMS",
  wex = 1.6,
  specPref = c(conta = "LYSC_CHICK", mainSpecies = "OS=Homo sapiens"),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

This function has been developed based on the OpenMS peptide-identification and label-free-quantification module. Csv input files may also be compresses as .gz.

Note: With this version the information about protein-modifications (PTMs) may not yet get exploited fully.

Value

This function returns a list with $raw (initial/raw abundance values), $quant with final normalized quantitations, $annot, $counts an array with number of peptides, $quantNotes,$expSetup and $notes; or if separateAnnot=FALSE the function returns a data.frame with annotation and quantitation only

See Also

read.table, normalizeThis) , readMaxQuantFile, readProlineFile, readProtDiscovFile

Examples

path1 <- system.file("extdata", package="wrProteo")
fiNa <- "OpenMS_tiny.csv.gz"
dataOM <- readOpenMSFile(file=fiNa, path=path1, tit="tiny OpenMS example")
summary(dataOM$quant)

Description

Quantification results from Proline Proline and MS-Angel exported as xlsx format can be read directly using this function. Besides, files in tsv, csv (European and US format) or tabulated txt can be read, too. Then relevant information gets extracted, the data can optionally normalized and displayed as boxplot or vioplot. The final output is a list containing 6 elements: $raw, $quant, $annot, $counts, $quantNotes and $notes. Alternatively, a data.frame with annotation and quantitation data may be returned if separateAnnot=FALSE. Note: There is no normalization by default since quite frequently data produced by Proline are already sufficiently normalized. The figure produced using the argument plotGraph=TRUE may help judging if the data appear sufficiently normalized (distribtions should align).

Usage

readProlineFile(
  fileName,
  path = NULL,
  normalizeMeth = "median",
  logConvert = TRUE,
  sampleNames = NULL,
  quantCol = "^abundance_",
  annotCol = c("accession", "description", "is_validated", "protein_set_score",
    "X.peptides", "X.specific_peptides"),
  remStrainNo = TRUE,
  pepCountCol = c("^psm_count_", "^peptides_count_"),
  trimColnames = FALSE,
  refLi = NULL,
  separateAnnot = TRUE,
  plotGraph = TRUE,
  titGraph = NULL,
  wex = 2,
  specPref = c(conta = "_conta\\|", mainSpecies = "OS=Homo sapiens"),
  gr = NULL,
  sdrf = NULL,
  suplAnnotFile = TRUE,
  groupPref = list(lowNumberOfGroups = TRUE, chUnit = TRUE),
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Details

This function has been developed using Proline version 1.6.1 coupled with MS-Angel 1.6.1. The classical way of using ths function consists in exporting results produced by Proline and MS-Angel as xlsx file. Besides, other formats may be read, too. This includes csv (eg the main sheet/table of ths xlsx exported file saved as csv). WOMBAT represents an effort to automatize quantitative proteomics experiments, using this route data get exported as txt files which can be read, too.

Value

This function returns a list with $raw (initial/raw abundance values), $quant with final normalized quantitations, $annot (columns ), $counts an array with 'PSM' and 'NoOfPeptides', $quantNotes and $notes; or a data.frame with quantitation and annotation if separateAnnot=FALSE

See Also

read.table

Examples

path1 <- system.file("extdata", package="wrProteo")
fiNa <- "exampleProlineABC.csv.gz"
dataABC <- readProlineFile(path=path1, file=fiNa)
summary(dataABC$quant)

Description

This function has been depreciated and replaced by readProteomeDiscovererPeptides (from this package).

Usage

readProtDiscovererPeptides(...)

Arguments

Value

This function returns NULL

See Also

readProteomeDiscovererFile, readProteomeDiscovererPeptides

Description

Depreciated old version of Protein identification and quantification results from Thermo ProteomeDiscoverer which were exported as tabulated text can be imported and relevant information extracted. The final output is a list containing 3 elements: $annot, $raw and optional $quant, or returns data.frame with entire content of file if separateAnnot=FALSE. Please use readProteomeDiscovererFile() from the same package instead !

Usage

readProtDiscovFile(
  fileName,
  path = NULL,
  normalizeMeth = "median",
  sampleNames = NULL,
  read0asNA = TRUE,
  quantCol = "^Abundances*",
  annotCol = NULL,
  contamCol = "Contaminant",
  refLi = NULL,
  separateAnnot = TRUE,
  FDRCol = list(c("^Protein.FDR.Confidence", "High"), c("^Found.in.Sample.", "High")),
  gr = NULL,
  sdrf = NULL,
  suplAnnotFile = TRUE,
  groupPref = list(lowNumberOfGroups = TRUE),
  specPref = c(conta = "CON_|LYSC_CHICK", mainSpecies = "OS=Homo sapiens"),
  plotGraph = TRUE,
  wex = 1.6,
  titGraph = "Proteome Discoverer",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

This function has been replaced by readProteomeDiscovererFile (from the same package) ! The syntax and strcuture of output has remained the same, you can simply replace the name of the function called.

This function has been developed using Thermo ProteomeDiscoverer versions 2.2 to 2.5. The format of resulting files at export also depends which columns are chosen as visible inside ProteomeDiscoverer and subsequently get chosen for export. 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 realted information. If a column named contamCol is found, the data will be lateron filtered to remove all contaminants, set to NULL for keeping all contaminants This function replaces the depreciated function readPDExport.

Value

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

See Also

read.table, normalizeThis) , readMaxQuantFile, readProlineFile, readFragpipeFile

Examples

path1 <- system.file("extdata", package="wrProteo")
fiNa <- "tinyPD_allProteins.txt.gz"
## Please use the function readProteinDiscovererFile(), as shown below (same syntax)
dataPD <- readProteomeDiscovererFile(file=fiNa, path=path1, suplAnnotFile=FALSE)
summary(dataPD$quant)

Description

Depreciated old version of Peptide identification and quantification results from Thermo ProteomeDiscoverer which were exported as tabulated text can be imported and relevant information extracted. The final output is a list containing 3 elements: $annot, $raw and optional $quant, or returns data.frame with entire content of file if separateAnnot=FALSE.

Usage

readProtDiscovPeptides(
  fileName,
  path = NULL,
  normalizeMeth = "median",
  sampleNames = NULL,
  suplAnnotFile = TRUE,
  gr = NULL,
  sdrf = NULL,
  read0asNA = TRUE,
  quantCol = "^Abundances*",
  annotCol = NULL,
  contamCol = "Contaminant",
  refLi = NULL,
  separateAnnot = TRUE,
  FDRCol = list(c("^Protein.FDR.Confidence", "High"), c("^Found.in.Sample.", "High")),
  plotGraph = TRUE,
  titGraph = "Proteome Discoverer",
  wex = 1.6,
  specPref = c(conta = "CON_|LYSC_CHICK", mainSpecies = "OS=Homo sapiens"),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments