| Title: | Analysis and quantitation of isobarically tagged MSMS proteomics data |
|---|---|
| Description: | isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. |
| Authors: | Florian P Breitwieser <[email protected]> and Jacques Colinge <[email protected]>, with contributions from Alexey Stukalov <[email protected]>, Xavier Robin <[email protected]> and Florent Gluck <[email protected]> |
| Maintainer: | Florian P Breitwieser <[email protected]> |
| License: | LGPL-2 |
| Version: | 1.53.0 |
| Built: | 2024-10-30 08:36:17 UTC |
| Source: | https://github.com/bioc/isobar |
isobar provides methods for preprocessing, normalization, and
report generation for the analysis of quantitative mass spectrometry
proteomics data labeled withOA isobaric tags, such as iTRAQ and TMT.
| Package: | isobar |
| Version: | 1.1.2 |
| biocViews: | Proteomics, MassSpectrometray, Bioinformatics, MultipleComparisons, QualityControl |
| Depends: | R (>= 2.9.0), Biobase, stats, methods, ggplot2 |
| Imports: | distr, biomaRt |
| Suggests: | MSnbase,XML |
| LazyLoad: | yes |
| License: | LGPL-2 |
| URL: | http://bioinformatics.cemm.oeaw.ac.at |
| Collate: | utils.R ProteinGroup-class.R IBSpectra-class.R NoiseModel-class.R ratio-methods.R sharedpep-methods.R MSnSet-methods.R zzz.R |
Index:
IBSpectra-class IBSpectra objects
NoiseModel-class NoiseModel objects
ProteinGroup-class ProteinGroup objects
do.log Log functions for IBSpectra objects
fitCauchy Fit weighted and unweighted Cauchy and Normal
distributions
groupMemberPeptides Peptide info for protein group members
human.protein.names Info on proteins
ibspiked_set1 Isobar Data packages
isobar-analysis IBSpectra analysis: Protein and peptide ratio
calculation
isobar-import Loading data into IBSpectra objects using
readIBSpectra
isobar-package Analysis and quantitation of isobaric tag
Proteomics data
isobar-plots IBSpectra plots
isobar-preprocessing IBSpectra preprocessing
isobar-reports Isobar reports
maplot.protein MAplot for individual proteins
number.ranges Helper function to transform number lists to
ranges
proteinInfo-methods Methods for Function proteinInfo
proteinRatios protein and peptide ratios
sanitize Helper function for LaTeX export
shared.ratios Shared ratio calculation
shared.ratios.sign Plot and get significantly shared ratios.
Further information is available in the following vignettes:
isobar |
Isobar Overview (source, pdf) |
isobar-devel |
Isobar for developers (source, pdf) |
Florian P Breitwieser <[email protected]> and Jacques Colinge <[email protected]>, with contributions from Xavier Robin <[email protected]>
Maintainer: Florian P Breitwieser <[email protected]>
Calculates delta score from raw search engine score by substracting the best matching hit with the second best matching.
data needs to have not only the best hit per spectrum, but multiple, to be able to calculate the delta score.
filterSpectraDeltaScore calls calc.delta.score and filters spectra below a minum delta score.
calc.delta.score(my.data) filterSpectraDeltaScore(my.data, min.delta.score=10, do.remove=FALSE)calc.delta.score(my.data) filterSpectraDeltaScore(my.data, min.delta.score=10, do.remove=FALSE)
my.data |
IBSpectra data frame. |
min.delta.score |
Minimum delta score. |
do.remove |
If TRUE, spectra below the min.prob threshold are not just set as 'use.for.quant=FALSE' but removed. |
Returns data with additional column 'delta.score'.
Florian P. Breitwieser
Function to recalculate start position of peptide in protein when it is missing or wrong.
calcPeptidePosition(peptide.info, protein.info, calc.il.peptide)calcPeptidePosition(peptide.info, protein.info, calc.il.peptide)
peptide.info |
Peptide info object of ProteinGroup. |
protein.info |
Protein info object of ProteinGroup. |
calc.il.peptide |
Should the 'real' peptide (I/L difference) be calculated? |
Functions for calculating and adjusting ratios and sample p-values. Usually, these are called by proteinRatios or peptideRatios.
calculate.ratio.pvalue(lratio, variance, ratiodistr = NULL) calculate.sample.pvalue(lratio, ratiodistr) calculate.mult.sample.pvalue(lratio, ratiodistr, strict.pval, lower.tail, n.possible.val, n.observed.val) adjust.ratio.pvalue(quant.tbl, p.adjust, sign.level, globally = FALSE)calculate.ratio.pvalue(lratio, variance, ratiodistr = NULL) calculate.sample.pvalue(lratio, ratiodistr) calculate.mult.sample.pvalue(lratio, ratiodistr, strict.pval, lower.tail, n.possible.val, n.observed.val) adjust.ratio.pvalue(quant.tbl, p.adjust, sign.level, globally = FALSE)
lratio |
log 10 protein or peptide ratios. |
ratiodistr |
Fitted ratio distribution/ |
variance |
Variance of lratios. |
strict.pval |
If FALSE, missing ratios are ignored. If TRUE, missing ratios are penalized by giving them a sample.pval of 0.5. |
lower.tail |
lower.tail of distribution? |
n.possible.val |
Number of possible ratios. |
n.observed.val |
Number of observed ratios. |
quant.tbl |
Quantification table (from proteinRatios or peptideRatios). |
p.adjust |
p-value adjustment method (see ?p.adjust). |
sign.level |
Ratio significance level. |
globally |
Whether the p-values should be adjusted over all conditions, or individually in each condition. |
Florian P. Breitwieser
lratio <- c(-1,-1,seq(from=-1,to=1,by=.25),1,1) variance <- c(0,1,rep(0.1,9),0,1) ratiodistr.precise <- new("Norm",mean=0,sd=.25) ratiodistr.wide <- new("Norm",mean=0,sd=.5) # ratio p-value is impacted only by the variance # sample p-value captures whether the ratio distribution is narrow ('precise') # or wide data.frame(lratio, variance, ratio.pvalue=calculate.ratio.pvalue(lratio, variance), sample.pvalue.precise=calculate.sample.pvalue(lratio,ratiodistr.precise), sample.pvalue.wide=calculate.sample.pvalue(lratio,ratiodistr.wide))lratio <- c(-1,-1,seq(from=-1,to=1,by=.25),1,1) variance <- c(0,1,rep(0.1,9),0,1) ratiodistr.precise <- new("Norm",mean=0,sd=.25) ratiodistr.wide <- new("Norm",mean=0,sd=.5) # ratio p-value is impacted only by the variance # sample p-value captures whether the ratio distribution is narrow ('precise') # or wide data.frame(lratio, variance, ratio.pvalue=calculate.ratio.pvalue(lratio, variance), sample.pvalue.precise=calculate.sample.pvalue(lratio,ratiodistr.precise), sample.pvalue.wide=calculate.sample.pvalue(lratio,ratiodistr.wide))
Distributed normalized spectral abundance factor (dNSAF) is a label free quantitative measure of protein abundance based on spectral counts which are corrected for peptides shared by multiple proteins. Original publication: Zhang Y et al., Analytical Chemistry (2010).
calculate.dNSAF(protein.group, use.mw = FALSE, normalize = TRUE, combine.f = mean)calculate.dNSAF(protein.group, use.mw = FALSE, normalize = TRUE, combine.f = mean)
protein.group |
ProteinGroup object. Its |
use.mw |
Use MW to account for protein size |
normalize |
Normalize dSAF to dNSAF? |
combine.f |
How to handle proteins seen only with shared peptides? |
Named numeric vector of dNSAF values.
Florian P Breitwieser
Zhang Y et al., Analytical Chemistry (2010)
proteinInfo,
getProteinInfoFromUniprot,
calculate.emPAI,
ProteinGroup
data(ibspiked_set1) protein.group <- proteinGroup(ibspiked_set1) calculate.dNSAF(protein.group)data(ibspiked_set1) protein.group <- proteinGroup(ibspiked_set1) calculate.dNSAF(protein.group)
The Exponentially Modified Protein Abundance Index (emPAI) is a label free quantitative measure of protein abundance based on protein coverage by peptide matches. The original publication is Ishihama Y, et al., Proteomics (2005).
calculate.emPAI(protein.group, protein.g = reporterProteins(protein.group), normalize = FALSE, observed.pep = c("pep", "mod.charge.pep"), use.mw = FALSE, combine.f = mean, ..., nmc = 0, report.all = FALSE) n.observable.peptides(...) observable.peptides(seq, nmc = 1, min.length = 6, min.mass = 600, max.mass = 4000, custom = list(code = c("B", "Z", "J", "U"), mass = c(164.554862, 278.61037, 213.12392, 150.953636)), ...)calculate.emPAI(protein.group, protein.g = reporterProteins(protein.group), normalize = FALSE, observed.pep = c("pep", "mod.charge.pep"), use.mw = FALSE, combine.f = mean, ..., nmc = 0, report.all = FALSE) n.observable.peptides(...) observable.peptides(seq, nmc = 1, min.length = 6, min.mass = 600, max.mass = 4000, custom = list(code = c("B", "Z", "J", "U"), mass = c(164.554862, 278.61037, 213.12392, 150.953636)), ...)
protein.group |
ProteinGroup object. Its |
protein.g |
Protein group identifiers. |
normalize |
Normalize to sum = 1?. |
observed.pep |
What counts as observed peptide? |
report.all |
TOADD |
use.mw |
Use MW to normalize for protein size |
combine.f |
How to handle proteins seen only with shared peptides? |
seq |
Protein sequence. |
nmc |
Number of missed cleavages. |
min.length |
Minimum length of peptide. |
min.mass |
Minimum mass of peptide. |
max.mass |
Maximum mass of peptide. |
custom |
User defined residue for |
... |
Further arguments to |
The formula is
N_observed is the number of observed peptides - we use the count
of unique peptide without consideration of charge state.
N_observable is the number of observable peptides.
Sequence cleavage is done using Digest.
Named numeric vector of emPAI values.
Florian P Breitwieser
Ishihama Y, et al., Proteomics (2005)
Digest,
proteinInfo,
getProteinInfoFromUniprot,
calculate.dNSAF,
ProteinGroup
data(ibspiked_set1) protein.group <- proteinGroup(ibspiked_set1) calculate.emPAI(protein.group,protein.g=protein.g(protein.group,"CERU"))data(ibspiked_set1) protein.group <- proteinGroup(ibspiked_set1) calculate.emPAI(protein.group,protein.g=protein.g(protein.group,"CERU"))
Correct peptide ratios with protein ratios from a separate experiment.
correct.peptide.ratios(ibspectra, peptide.quant.tbl, protein.quant.tbl, protein.group.combined, adjust.variance = TRUE, correlation = 0, recalculate.pvalue = TRUE)correct.peptide.ratios(ibspectra, peptide.quant.tbl, protein.quant.tbl, protein.group.combined, adjust.variance = TRUE, correlation = 0, recalculate.pvalue = TRUE)
ibspectra |
IBSpectra object. |
peptide.quant.tbl |
Calculated with peptideRatios. |
protein.quant.tbl |
Calculated with proteinRatios. |
protein.group.combined |
ProteinGroup object generated on both PTM and protein data. |
adjust.variance |
Adjust variance of ratios. |
correlation |
Assumed correlation between peptide and protein ratios for variance adjustment. |
recalculate.pvalue |
Recalculate p-value after variance adjustment. |
Florian P. Breitwieser
calcProbXGreaterThanY calculates the probability that X >= Y. calcProbXDiffNormals calculates the probabilities of a set of normals, defined by the vectors mu_Y and sd_Y are greater or less than the reference distribution Y.
calcProbXGreaterThanY(X, Y, rel.tol = .Machine$double.eps^0.25, subdivisions = 100L) calcProbXDiffNormals(X, mu_Y, sd_Y, ..., alternative = c("greater", "less", "two-sided"), progress = FALSE) #calcCumulativeProbXGreaterThanY(Xs, mu_Ys, sd_Ys, alternative = c("greater", "less", "two-sided"), rel.tol = .Machine$double.eps^0.25, subdivisions = 100L) distrprint(X, round.digits = 5) twodistr.plot(X, Y, n.steps = 1000, min.q = 10^-3)calcProbXGreaterThanY(X, Y, rel.tol = .Machine$double.eps^0.25, subdivisions = 100L) calcProbXDiffNormals(X, mu_Y, sd_Y, ..., alternative = c("greater", "less", "two-sided"), progress = FALSE) #calcCumulativeProbXGreaterThanY(Xs, mu_Ys, sd_Ys, alternative = c("greater", "less", "two-sided"), rel.tol = .Machine$double.eps^0.25, subdivisions = 100L) distrprint(X, round.digits = 5) twodistr.plot(X, Y, n.steps = 1000, min.q = 10^-3)
X |
Object of the class Distribution. |
Y |
Object of the class Distribution. |
min.q |
minimum quantile |
n.steps |
Number of steps. |
mu_Y |
Numeric vector of parameter mu of a Normal. |
sd_Y |
Numeric vector of parameter sd of a Normal. |
subdivisions |
the maximum number of subintervals |
rel.tol |
relative accuracy requested |
... |
Additional arguments to calcProbXGreaterThanY. |
alternative |
"less", "greater", or "two-sided". |
progress |
Show text progress bar? |
round.digits |
Round digits for printing. |
Florian P. Breitwieser
library(distr) calcProbXGreaterThanY(Norm(0,.25),Norm(1,.25))library(distr) calcProbXGreaterThanY(Norm(0,.25),Norm(1,.25))
Functions to fit the probability density functions on ratio distribution.
fitCauchy(x) fitNorm(x, portion = 0.75) fitWeightedNorm(x, weights) fitNormalCauchyMixture(x) fitGaussianMixture(x, n = 500) fitTlsd(x)fitCauchy(x) fitNorm(x, portion = 0.75) fitWeightedNorm(x, weights) fitNormalCauchyMixture(x) fitGaussianMixture(x, n = 500) fitTlsd(x)
x |
Ratios |
weights |
Weights |
portion |
Central portion of data to take for computation |
n |
number of sampling steps |
Florian P Breitwieser, Jacques Colinge.
library(distr) data(ibspiked_set1) data(noise.model.hcd) # calculate protein ratios of Trypsin and CERU_HUMAN. Note: this is only # for illustration purposes. For estimation of sample variability, data # from all protein should be used pr <- proteinRatios(ibspiked_set1,noise.model=noise.model.hcd, cl=as.character(c(1,1,2,2)),combn.method="intraclass",protein=c("136429","P00450")) # fit a Cauchy distribution ratiodistr <- fitCauchy(pr$lratio) plot(ratiodistr)library(distr) data(ibspiked_set1) data(noise.model.hcd) # calculate protein ratios of Trypsin and CERU_HUMAN. Note: this is only # for illustration purposes. For estimation of sample variability, data # from all protein should be used pr <- proteinRatios(ibspiked_set1,noise.model=noise.model.hcd, cl=as.character(c(1,1,2,2)),combn.method="intraclass",protein=c("136429","P00450")) # fit a Cauchy distribution ratiodistr <- fitCauchy(pr$lratio) plot(ratiodistr)
Gets neighboring amino acids around modification which can be used to find enriched motifs.
getPeptideModifContext(protein.group, modif, n.aa.up = 7, n.aa.down = 7)getPeptideModifContext(protein.group, modif, n.aa.up = 7, n.aa.down = 7)
protein.group |
ProteinGroup object. |
modif |
Modification of interest. |
n.aa.up |
Number of AA downstream to report. |
n.aa.down |
Number of AA upstream to report. |
Get phosphorylation site localization probabilities by calling PhosphoRS
and parsing its output. getPhosphoRSProbabilities generates a XML
input file for PhosphoRS calling writePhosphoRSInput, then executes
phosphoRS.jar with java, and parses the XML result file with
readPhosphoRSOutput.
getPhosphoRSProbabilities(id.file, mgf.file, massTolerance, activationType, simplify = FALSE, mapping.file = NULL, mapping = c(peaklist = "even", id = "odd"), pepmodif.sep = "##.##", besthit.only = TRUE, phosphors.cmd = paste("java -jar", system.file("phosphors", "phosphoRS.jar", package = "isobar")), file.basename = tempfile("phosphors.")) writePhosphoRSInput(phosphoRS.infile, id.file, mgf.file, massTolerance, activationType, mapping.file = NULL, mapping = c(peaklist = "even", id = "odd"), pepmodif.sep = "##.##", modif.masses = rbind(c("PHOS", "1", "1:Phospho:Phospho:79.966331:PhosphoLoss:97.976896:STY"), c("Oxidation_M", "2", "2:Oxidation:Oxidation:15.994919:null:0:M"), c("Cys_CAM", "3", "3:Carbamidomethylation:Carbamidomethylation:57.021464:null:0:C"), c("iTRAQ4plex", "4", "4:iTRAQ4:iTRAQ4:144.1544:null:0:KX"), c("iTRAQ8plex", "5", "5:iTRAQ8:iTRAQ8:304.308:null:0:KX"), c("TMT6plex", "7", "7:TMT6:TMT6:229.162932:null:0:KX"), c("TMTsixplex", "6", "6:TMT6:TMT6:229.162932:null:0:KX"))) readPhosphoRSOutput(phosphoRS.outfile, simplify = FALSE, pepmodif.sep = "##.##", besthit.only = TRUE) filterSpectraPhosphoRS(id.file, mgf.file, ..., min.prob = NULL, do.remove=FALSE)getPhosphoRSProbabilities(id.file, mgf.file, massTolerance, activationType, simplify = FALSE, mapping.file = NULL, mapping = c(peaklist = "even", id = "odd"), pepmodif.sep = "##.##", besthit.only = TRUE, phosphors.cmd = paste("java -jar", system.file("phosphors", "phosphoRS.jar", package = "isobar")), file.basename = tempfile("phosphors.")) writePhosphoRSInput(phosphoRS.infile, id.file, mgf.file, massTolerance, activationType, mapping.file = NULL, mapping = c(peaklist = "even", id = "odd"), pepmodif.sep = "##.##", modif.masses = rbind(c("PHOS", "1", "1:Phospho:Phospho:79.966331:PhosphoLoss:97.976896:STY"), c("Oxidation_M", "2", "2:Oxidation:Oxidation:15.994919:null:0:M"), c("Cys_CAM", "3", "3:Carbamidomethylation:Carbamidomethylation:57.021464:null:0:C"), c("iTRAQ4plex", "4", "4:iTRAQ4:iTRAQ4:144.1544:null:0:KX"), c("iTRAQ8plex", "5", "5:iTRAQ8:iTRAQ8:304.308:null:0:KX"), c("TMT6plex", "7", "7:TMT6:TMT6:229.162932:null:0:KX"), c("TMTsixplex", "6", "6:TMT6:TMT6:229.162932:null:0:KX"))) readPhosphoRSOutput(phosphoRS.outfile, simplify = FALSE, pepmodif.sep = "##.##", besthit.only = TRUE) filterSpectraPhosphoRS(id.file, mgf.file, ..., min.prob = NULL, do.remove=FALSE)
id.file |
Database search results file in |
mgf.file |
Peaklist file |
massTolerance |
Fragment ion mass tolerance (in Da) |
activationType |
Activation types of spectra. CID, HCD, or ETD. |
simplify |
If |
mapping.file |
Mapping file. See also |
mapping |
Mapping columns. |
besthit.only |
Only show best hit, simplifies result to data.frame instead of list. |
phosphors.cmd |
PhosphoRS script. |
file.basename |
Base name for creating phosphoRS input and output files. |
phosphoRS.infile |
PhosphoRS input XML file name. |
phosphoRS.outfile |
PhosphoRS output XML file name. |
pepmodif.sep |
separator of peptide and modification in XML id |
modif.masses |
masses and ID used for PhosphoRS |
min.prob |
Threshold for PhosphoRS peptide probability to consider it for quantification |
... |
Further arguments to getPhosphoRSProbabilities |
do.remove |
If TRUE, spectra below the min.prob threshold are not just set as 'use.for.quant=FALSE' but removed. |
PhosphoRS is described in Taus et al., 2011. It can be downloaded from http://cores.imp.ac.at/protein-chemistry/download/ and used as Freeware. Java is required at runtime.
If simplify=TRUE, a data.frame with the following columns:
spectrum, peptide, modif, PepScore, PepProb, seqpos
If simplify=FALSE, a list (of spectra) of lists (of peptide
identifications) of lists (with information about identification
and localization).
spectrum -> peptide 1, peptides 2, ... -> peptide.
First level:
- spectrum
Second level:
- peptide identifications for spectrum (might be more than one)
Third level:
- peptide: vector with peptide sequence and modification stirng
- site.probs: matrix with site probabilities for each phospho site
- isoforms: peptide score and probabilities for each isoform
Florian P Breitwieser
Taus et al., 2011
Get PTM site information for idenfied proteins from public databases.
getPtmInfoFromPhosphoSitePlus(protein.group, file.name = NULL, modif = "PHOS", psp.url = "http://www.phosphosite.org/downloads/", mapping = c(PHOS = "Phosphorylation_site_dataset.gz", ACET = "Acetylation_site_dataset.gz", METH = "Methylation_site_dataset.gz", SUMO = "Sumoylation_site_dataset.gz", UBI = "Ubiquitination_site_dataset.gz")) getPtmInfoFromNextprot(protein.group, nextprot.url = "http://www.nextprot.org/rest/entry/NX_XXX/ptm?format=json", url.wildcard = "XXX")getPtmInfoFromPhosphoSitePlus(protein.group, file.name = NULL, modif = "PHOS", psp.url = "http://www.phosphosite.org/downloads/", mapping = c(PHOS = "Phosphorylation_site_dataset.gz", ACET = "Acetylation_site_dataset.gz", METH = "Methylation_site_dataset.gz", SUMO = "Sumoylation_site_dataset.gz", UBI = "Ubiquitination_site_dataset.gz")) getPtmInfoFromNextprot(protein.group, nextprot.url = "http://www.nextprot.org/rest/entry/NX_XXX/ptm?format=json", url.wildcard = "XXX")
protein.group |
ProteinGroup object. |
file.name |
File name to save downloaded data, defaults to the original file name (see mapping). |
modif |
Selects dataset to download (see mapping). |
psp.url |
PhosphoSitePlus main URL for datasets. |
mapping |
Names of PhosphoSitePlus modification datasets, mapped by modif name. |
nextprot.url |
URL for fetching Nextprot results. |
url.wildcard |
wildcard to replace with Uniprot protein AC in |
PhosphoSitePlus datasets are downloaded and written to the working directory with its original name (see mapping) unless a file with that name exists, which is then parsed into a data.frame of suitable format.
data.frame with (at least) the columns: isoform_ac, description, evidence, position
PhosphoSitePlus is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License and is freely available for non-commercial purpose, see http://www.phosphosite.org/staticDownloads.do.
neXtProt is licensed under the Creative Commons Attribution-NoDerivs License, see: http://creativecommons.org/licenses/by-nd/3.0.
Please read the conditions and use the data only if you agree.
Florian P. Breitwieser
PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Hornbeck PV, Kornhauser JM, Tkachev S, Zhang B, Skrzypek E, Murray B, Latham V, Sullivan M. Nucleic Acids Res. 2012 Jan;40(Database issue):D261-70. Epub 2011 Dec 1.
neXtProt: a knowledge platform for human proteins. Lane L, Argoud-Puy G, Britan A, Cusin I, Duek PD, Evalet O, Gateau A, Gaudet P, Gleizes A, Masselot A, Zwahlen C, Bairoch A. Nucleic Acids Res. 2012 Jan;40(Database issue):D76-83. Epub 2011 Dec 1.
## Not run: data(ib_phospho) ptm.info.np <- getPtmInfoFromNextprot(proteinGroup(ib_phospho)) ptm.info.np <- ptm.info.np[grep("Phospho",ptm.info.np$modification.name),] ptm.info.psp <- getPtmInfoFromPhosphoSitePlus(proteinGroup(ib_phospho),modif="PHOS") str(ptm.info.np) str(ptm.info.psp) ## End(Not run)## Not run: data(ib_phospho) ptm.info.np <- getPtmInfoFromNextprot(proteinGroup(ib_phospho)) ptm.info.np <- ptm.info.np[grep("Phospho",ptm.info.np$modification.name),] ptm.info.psp <- getPtmInfoFromPhosphoSitePlus(proteinGroup(ib_phospho),modif="PHOS") str(ptm.info.np) str(ptm.info.psp) ## End(Not run)
For a given reporter protein group identifier, information on its peptides is returned. It contains information on how the peptides are shared and in which member they occur.
groupMemberPeptides(x, reporter.protein.g, ordered.by.pos = TRUE, only.first.pos = TRUE)groupMemberPeptides(x, reporter.protein.g, ordered.by.pos = TRUE, only.first.pos = TRUE)
x |
ProteinGroup object |
reporter.protein.g |
group reporter protein |
ordered.by.pos |
if TRUE, start position of peptides in proteins is exported and peptides are ordered by position |
only.first.pos |
if TRUE, only first occurence of peptide in protein is reported |
list of two: [1] peptide.info: data.frame peptide specificity n.shared.groups n.shared.proteins start.pos [2] group.member.peptides: data.frame each column corresponds to a group member, and each row to a peptide
Florian P Breitwieser
data(ibspiked_set1) protein.group <- proteinGroup(ibspiked_set1) ceru.rat <- protein.g(protein.group,"CERU_RAT") groupMemberPeptides(protein.group,ceru.rat) ## find protein groups with members t <- table(proteinGroupTable(protein.group)$reporter.protein) t[t>2] protein.g <- names(t)[t>2][1] groupMemberPeptides(protein.group,protein.g)data(ibspiked_set1) protein.group <- proteinGroup(ibspiked_set1) ceru.rat <- protein.g(protein.group,"CERU_RAT") groupMemberPeptides(protein.group,ceru.rat) ## find protein groups with members t <- table(proteinGroupTable(protein.group)$reporter.protein) t[t>2] protein.g <- names(t)[t>2][1] groupMemberPeptides(protein.group,protein.g)
Gather human readable information from protein group codes.
my.protein.info(x, protein.g) human.protein.names(my.protein.info)my.protein.info(x, protein.g) human.protein.names(my.protein.info)
x |
ProteinGroup object |
protein.g |
protein |
my.protein.info |
Return value of function my.protein.info |
Florian P Breitwieser
This class represents a quantitative MS proteomics
experiment labeled using Isobaric tags (iTRAQ, TMT). IBSpectra
is a abstract class which is implemented in the
IBSpectraTypes classes iTRAQ4plexSpectra,
iTRAQ8plexSpectra, TMT2plexSpectra, TMT6plexSpectra and TMT10plexSpectra.
It contains per-spectrum meassurements of the reporter tag intensity and m/z in assayData, and protein grouping in proteinGroup.
IBSpectra objects are typically created using the
readIBSpectra method or by calls of the form
new("iTRAQ4plexSpectra",data=NULL,data.ions=NULL,...).
IBSpectra extends eSet which
is a container for high-throughput assays and experimental metadata.
Slots introduced in eSet (for more details on slots and methods
refer to eSet help):
assayData:Contains matrices 'ions' and 'mass
storing reporter tag intensities and m/z values for each tag and
spectrum. Can be accessed by reporterIntensities
and reporterMasses. Class: AssayData
phenoData:Contains experimenter-supplied variables
describing phenotypes behind reporter tags.
Class: AnnotatedDataFrame-class
featureData:Describes the spectra's retention time,
charge, peptide sequence, etc and can be accessed by
fData. Class: AnnotatedDataFrame
experimentData:Contains details of experimental
methods. Class: MIAME
annotation:UNUSED. Label associated with the annotation
package used in the experiment. Class: character
protocolData:UNUSED. Contains equipment-generated
variables describing reporter tags. Class: AnnotatedDataFrame
log:character matrix logging isotope impurity
correction, normalization, etc.
Slots introduced in IBSpectra:
proteinGroup:A ProteinGroup
object describing peptide and protein identifications
grouped by shared peptides.
reporterTagNames:A character vector denoting the reporter tag labels.
reporterMasses:The 'true' m/z of the reporter tags in the MS/MS spectrum, used to isolate m/z-intensity pairs from peaklist.
isotopeImpurities:Manufacturer supplied isotope
impurities, need to be set per batch and used for correction
by correctIsotopeImpurities.
See readIBSpectra for creation based on peaklist
(e.g. MGF format) and identification files (Mascot and Phenyx output).
new(type,data):Creates a IBSpectra object.
typeDenotes the type of IBSpectra, either 'iTRAQ4plexSpectra','iTRAQ8plexSpectra','TMT2plexSpectra', 'TMT6plexSpectra' or 'TMT10plexSpectra'. Call IBSpectraTypes() to see a list of the implemented types.
dataA 'data.frame' in a ibspectra-csv format.
In the code snippets below, x is a IBSpectra object.
IBSpectra object can be coerced to
as(x, "data.frame"):Creates a data.frame containing all identification and quantitation information. Peptide matching to multiple proteins produce multiple lines.
ibSpectra.as.concise.data.frame(x): Creates a data.frame
containing all identification and
quantitation information. Proteins are concatenated - so the
resulting data.frame has one line per spectrum.
as(x, "MSnSet"):as(msnset,"IBSpectra"):Coerces a MSnSet to IBSpectra object.
In the following code snippets, x is a IBSpectra object.
proteinGroup(x):Gets and sets the ProteinGroup.
isotopeImpurities(x):Gets and sets the isotope impurities of the isobaric tags as defined by the manufacturers per batch.
reporterData(x,element="ions",na.rm=FALSE,na.rm.f='any',...):Gets and sets the element ('ions' or 'mass') for each tag and spectrum. '...' is handed down to spectrumSel, so it is possible to select for peptides or proteins. If na.rm is TRUE, than spectra missing quantitative information in 'any' or 'all' channels (parameter na.rm.f) are removed.
reporterIntensities(x,...):Convenience function, calls reporterData(...,element="ions")
reporterMasses(x,...):Convenience function, calls reporterData(...,element="mass")
spectrumTitles(x,...):Gets the spectrum titles. '...' is passed down to spectrumSel.
classLabels(x):Gets and sets the class labels in phenoData. Used for
summarization, see also estimateRatio and
phenoData.
In the following code snippets, x is a IBSpectra object.
subsetIBSpectra(x, protein=NULL, peptide=NULL, direction="exclude",specificity):Get a 'subset' of IBSpectra: include or exclude proteins
or peptides. When selection is based on proteins, it can be
defined to exclude only peptides which are specific to the protein
('reporter-specific'), specific to the group ('group-specific') or
which are shared with other proteins ('unspecific'). See subsetIBSpectra.
spectrumSel(x,peptide,protein,specificity="reporter-specific"):Gets a boolean vector selecting the corresponding spectra:
If peptide is given, all spectra assigned to this peptide.
If protein is given, all spectra assigned to peptides of this
protein with specificity 'specificity'.
See also ProteinGroup.
Florian P. Breitwieser
ProteinGroup, isobar-preprocessing, isobar-analysis, isobar-plots
data(ibspiked_set1) ibspiked_set1 head(reporterIntensities(ibspiked_set1)) head(reporterMasses(ibspiked_set1)) proteinGroup(ibspiked_set1) isotopeImpurities(ibspiked_set1) # create new object set.seed(123) data <- data.frame(spectrum=letters, peptide=sample(c("pepA","pepB","pepC"),26,TRUE), start.pos=1, modif=sample(c("::X:::",":Y::::","::Z:::"),26,TRUE), accession=c("protein1","protein2")) data.ions <- matrix(rnorm(26*2,1000,50), ncol=2,dimnames=list(letters,NULL)) data.mass <- matrix(rep(c(126.1,127.1),26), ncol=2,byrow=TRUE,dimnames=list(letters,NULL)) ib <- new("TMT2plexSpectra",data,data.ions,data.mass) ib reporterIntensities(ib) isotopeImpurities(ib) <- matrix(c(0.8,0.1,0.2,0.9),nrow=2) reporterIntensities(correctIsotopeImpurities(ib))data(ibspiked_set1) ibspiked_set1 head(reporterIntensities(ibspiked_set1)) head(reporterMasses(ibspiked_set1)) proteinGroup(ibspiked_set1) isotopeImpurities(ibspiked_set1) # create new object set.seed(123) data <- data.frame(spectrum=letters, peptide=sample(c("pepA","pepB","pepC"),26,TRUE), start.pos=1, modif=sample(c("::X:::",":Y::::","::Z:::"),26,TRUE), accession=c("protein1","protein2")) data.ions <- matrix(rnorm(26*2,1000,50), ncol=2,dimnames=list(letters,NULL)) data.mass <- matrix(rep(c(126.1,127.1),26), ncol=2,byrow=TRUE,dimnames=list(letters,NULL)) ib <- new("TMT2plexSpectra",data,data.ions,data.mass) ib reporterIntensities(ib) isotopeImpurities(ib) <- matrix(c(0.8,0.1,0.2,0.9),nrow=2) reporterIntensities(correctIsotopeImpurities(ib))
The slot log of IBSpectra objects contains a matrix with
two columns which contain a timestamp and message. Rownames
relate to the item logged.
Used by correctIsotopeImpurities and normalize.
do.log(x, name, msg) get.log(x, name) is.logged(x, name)do.log(x, name, msg) get.log(x, name) is.logged(x, name)
x |
IBSpectra object |
name |
Name of property to be logged (translates to row name). |
msg |
Message to be logged for name. |
A warning message will be displayed if a already logged property is logged again.
do.log: IBSpectra object with updated log. get.log:
Florian P Breitwieser
IBSpectra-class
data(ibspiked_set1) ib <- normalize(correctIsotopeImpurities(ibspiked_set1)) ib@logdata(ibspiked_set1) ib <- normalize(correctIsotopeImpurities(ibspiked_set1)) ib@log
Utility functions. paste0 as a shorthand to paste(...,sep="") in versions of R pre 2.14.
paste0(..., sep = "") a %inrange% bpaste0(..., sep = "") a %inrange% b
... |
Arguments to paste. |
sep |
Separator. |
a |
values. |
b |
range. |
Florian P Breitwieser
1:101:10
Calculates the relative abundance of a peptide or protein in one tag compared to another.
estimateRatio(ibspectra, noise.model = NULL, channel1, channel2, protein, peptide, ...) estimateRatioForPeptide(peptide, ibspectra, noise.model, channel1, channel2, combine = TRUE, ...) estimateRatioForProtein(protein, ibspectra, noise.model, channel1, channel2, combine = TRUE, method = "isobar", specificity = REPORTERSPECIFIC, quant.w.grouppeptides = NULL, ...) ## S4 method for signature 'numeric,numeric,missing' estimateRatioNumeric(channel1,channel2,summarize.f=median, ...) ## S4 method for signature 'numeric,numeric,NoiseModel' estimateRatioNumeric(channel1,channel2,noise.model,ratiodistr=NULL,variance.function="maxi", sign.level=0.05,sign.level.rat=sign.level,sign.level.sample=sign.level, remove.outliers=TRUE,outliers.args=list(method = "iqr", outliers.coef = 1.5), method="isobar",fc.threshold=1.3, channel1.raw=NULL,channel2.raw=NULL,use.na=FALSE,preweights=NULL) ## S4 method for signature ## 'IBSpectra,ANY,character,character,character,missing' estimateRatio(ibspectra,noise.model,channel1,channel2, protein,peptide,...) ## S4 method for signature 'IBSpectra,ANY,character,character,character,NULL' estimateRatio(ibspectra,noise.model,channel1,channel2, protein,peptide=NULL,...) ## S4 method for signature ## 'IBSpectra,ANY,character,character,missing,character' estimateRatio(ibspectra,noise.model,channel1,channel2,protein,peptide,...) ## S4 method for signature 'IBSpectra,ANY,character,character,NULL,character' estimateRatio(ibspectra,noise.model,channel1,channel2,protein=NULL,peptide,...)estimateRatio(ibspectra, noise.model = NULL, channel1, channel2, protein, peptide, ...) estimateRatioForPeptide(peptide, ibspectra, noise.model, channel1, channel2, combine = TRUE, ...) estimateRatioForProtein(protein, ibspectra, noise.model, channel1, channel2, combine = TRUE, method = "isobar", specificity = REPORTERSPECIFIC, quant.w.grouppeptides = NULL, ...) ## S4 method for signature 'numeric,numeric,missing' estimateRatioNumeric(channel1,channel2,summarize.f=median, ...) ## S4 method for signature 'numeric,numeric,NoiseModel' estimateRatioNumeric(channel1,channel2,noise.model,ratiodistr=NULL,variance.function="maxi", sign.level=0.05,sign.level.rat=sign.level,sign.level.sample=sign.level, remove.outliers=TRUE,outliers.args=list(method = "iqr", outliers.coef = 1.5), method="isobar",fc.threshold=1.3, channel1.raw=NULL,channel2.raw=NULL,use.na=FALSE,preweights=NULL) ## S4 method for signature ## 'IBSpectra,ANY,character,character,character,missing' estimateRatio(ibspectra,noise.model,channel1,channel2, protein,peptide,...) ## S4 method for signature 'IBSpectra,ANY,character,character,character,NULL' estimateRatio(ibspectra,noise.model,channel1,channel2, protein,peptide=NULL,...) ## S4 method for signature ## 'IBSpectra,ANY,character,character,missing,character' estimateRatio(ibspectra,noise.model,channel1,channel2,protein,peptide,...) ## S4 method for signature 'IBSpectra,ANY,character,character,NULL,character' estimateRatio(ibspectra,noise.model,channel1,channel2,protein=NULL,peptide,...)
ibspectra |
IBSpectra object. |
noise.model |
NoiseModel object. |
channel1 |
Tag channel 1. Can either be a character denoting a 'reporter name' or a numeric vector whose value should be summarized.Ratio is calculated as channel2/channel1. |
channel2 |
Tag channel 2. Can either be a character denoting a 'reporter name' or a numeric vector whose value should be summarized. Ratio is calculated as channel2/channel1. |
protein |
Protein(s) of interest. If present, channel1 and channel2 must be reporter names. Provide either proteins or peptides. |
peptide |
Peptide(s) of interest. If present, channel1 and channel2 must be reporter names. Provide either proteins or peptides. |
combine |
If true, a single ratio is returned even for multiple peptides/spectra. If false, a data.frame with a row for each peptide/protein is returned. |
specificity |
See |
quant.w.grouppeptides |
Proteins which should be quantified with group specific peptides. Normally, only reporter specific peptides are used. |
ratiodistr |
|
variance.function |
Defines how the variance for ratio is calculated. 'ev' is the estimator variance and thus 1/sum(1/variances). 'wsv' is the weighted sample variance. 'maxi' method takes the maximum of the former two variances. |
sign.level |
Significiance level. |
sign.level.rat |
Signal p-value significiance level. |
sign.level.sample |
Sample p-value significiance level. |
remove.outliers |
Should outliers be removed? |
outliers.args |
Arguments for outlier removal, see OUTLIERS function (TODO). |
method |
method taken for ratio computation and selection: one of 'isobar','libra','multiq','pep','ttest' and 'compare.all'. |
fc.threshold |
When method equals fc, takes this as fold change threshold. |
summarize.f |
A method for summarizing spectrum ratios when no other
information is available. For example |
channel1.raw |
When given, noise estimation is based on channel1.raw and channel2.raw. These are the intensities of the channels before normalization. |
channel2.raw |
See channel1.raw. |
use.na |
Use NA values to calculate ratio. Experimental feature - use with caution. |
preweights |
Specifies weigths for each spectrum. Experimental feature - use with caution. |
... |
Passed down to |
In general, a named character vector with the following elements: - lratio: log ratio - variance - n.spectra: number of spectra available in the ratio calculation - p.value.rat: Signal p-value. NA if called w/o ratiodistr - p.value.sample: Sample p-value. NA if called w/o ratiodistr - is.significant: NA if called w/o ratiodistr
If combine=FALSE, estimateRatio returns a data.frame, with columns as described above.
Florian P. Breitwieser, Jacques Colinge
ProteinGroup, IBSpectra, isobar-preprocessing, isobar-plots proteinRatios
data(ibspiked_set1) data(noise.model.hcd) ceru.human <- protein.g(proteinGroup(ibspiked_set1),"CERU_HUMAN") ceru.rat <- protein.g(proteinGroup(ibspiked_set1),"CERU_RAT") ceru.mouse <- protein.g(proteinGroup(ibspiked_set1),"CERU_MOUSE") ceru.proteins <- c(ceru.human,ceru.rat,ceru.mouse) ## Calculate ratio based on all spectra of peptides specific ## to CERU_HUMAN, CERU_RAT or CERU_MOUSE. Returns a named ## numeric vector. 10^estimateRatio(ibspiked_set1,noise.model.hcd, channel1="114",channel2="115", protein=ceru.proteins)['lratio'] ## If argument 'combine=FALSE', estimateRatio returns a data.frame ## with one row per protein 10^estimateRatio(ibspiked_set1,noise.model.hcd, channel1="114",channel2="115", protein=ceru.proteins,combine=FALSE)[,'lratio'] ## spiked material channel 115 vs 114: ## CERU_HUMAN (P00450): 1 ## CERU_RAT (P13635): 2 ## CERU_MOUSE (Q61147): 0.5data(ibspiked_set1) data(noise.model.hcd) ceru.human <- protein.g(proteinGroup(ibspiked_set1),"CERU_HUMAN") ceru.rat <- protein.g(proteinGroup(ibspiked_set1),"CERU_RAT") ceru.mouse <- protein.g(proteinGroup(ibspiked_set1),"CERU_MOUSE") ceru.proteins <- c(ceru.human,ceru.rat,ceru.mouse) ## Calculate ratio based on all spectra of peptides specific ## to CERU_HUMAN, CERU_RAT or CERU_MOUSE. Returns a named ## numeric vector. 10^estimateRatio(ibspiked_set1,noise.model.hcd, channel1="114",channel2="115", protein=ceru.proteins)['lratio'] ## If argument 'combine=FALSE', estimateRatio returns a data.frame ## with one row per protein 10^estimateRatio(ibspiked_set1,noise.model.hcd, channel1="114",channel2="115", protein=ceru.proteins,combine=FALSE)[,'lratio'] ## spiked material channel 115 vs 114: ## CERU_HUMAN (P00450): 1 ## CERU_RAT (P13635): 2 ## CERU_MOUSE (Q61147): 0.5
Read ibspectra-csv files and peaklist files as an
IBSpectra object of type 'type' (see IBSpectra,
e.g. iTRAQ4plexSpectra or TMT6plexSpectra). If
peaklist.file is missing, it is assumed that id.file contains
intensity and m/z columns for the reporter tags.
## S4 method for signature 'character,character' readIBSpectra(type,id.file) # reads id file ## S4 method for signature 'character,character,character' readIBSpectra( type, id.file, peaklist.file, sep = "\t", mapping.file = NULL, mapping = c(quantification.spectrum = "hcd", identification.spectrum = "cid"), id.file.domap = NULL, identifications.format = NULL, decode.titles = FALSE, ...) # reads peaklist file ## S4 method for signature 'character,data.frame,character' readIBSpectra( type, id.file, peaklist.file, annotate.spectra.f = NULL, peaklist.format = NULL, scan.lines = 0, fragment.precision = NULL, fragment.outlier.prob = NULL, ...)## S4 method for signature 'character,character' readIBSpectra(type,id.file) # reads id file ## S4 method for signature 'character,character,character' readIBSpectra( type, id.file, peaklist.file, sep = "\t", mapping.file = NULL, mapping = c(quantification.spectrum = "hcd", identification.spectrum = "cid"), id.file.domap = NULL, identifications.format = NULL, decode.titles = FALSE, ...) # reads peaklist file ## S4 method for signature 'character,data.frame,character' readIBSpectra( type, id.file, peaklist.file, annotate.spectra.f = NULL, peaklist.format = NULL, scan.lines = 0, fragment.precision = NULL, fragment.outlier.prob = NULL, ...)
type |
Name of class of new IBSpectra object:
|
id.file |
Database search results file in |
peaklist.file |
Peaklist file, typically in MGF format, see |
mapping.file |
If defined, spectum titles from the peaklist file are linked to the identifications via this file. This can be used when running HCD runs for quantification and CID runs for identification. See Koecher et al., 2009 for details. |
mapping |
Named character vector defining the names of columns in mapping.file. The names must be 'peaklist' and 'id', and the values must correspond to colnames of the mapping files. |
id.file.domap |
When using HCD-CID or a method akin and every spectrum is used for
identification, the ID result files of the HCD run can be
specfied in |
annotate.spectra.f |
Function which changes or annotates the spectra feature data before it is written to IBpectra object. This can be used to calculate and threshold additional scores, for example localization scores of post- translational modifications such as Delta Score (filterSpectraDeltaScore) or PhosphoRS site localization probabilities (annotateSpectraPhosphoRS). |
peaklist.format |
"mgf" (Mascot Generic format) or "mcn" (iTracker Machine Readable output). When NULL, it detects the format on file name extension. |
identifications.format |
"ibspectra.csv" or "mzid" (PSI MzIdentML format). When NULL, file format is guessed based on extension. |
fragment.precision |
Fragment precision for extraction of reporter tags: for each tag and spectrum the m/z-intensity pair with it's mass closest to the known reporter tag mass is extracted within the window true_mass +/- fragment.precision/2. |
fragment.outlier.prob |
Fragment outlier probability filter: After all m/z-intensity pairs have been extracted, those pairs with the fragment.outlier.prob/2 most unprecise m/z values are filtered out. |
decode.titles |
Boolean. Decode spectrum titles in identification file using |
scan.lines |
Read files sequentially scan.lines lines at a time. Can help in case of memory issues, set to 10000 or higher, for example. |
sep |
sep argument of read.table |
... |
Further arguments handed down to |
Florian P. Breitwieser, Jacques Colinge
ProteinGroup, IBSpectra, isobar-preprocessing, isobar-analysis, isobar-plots
data(ibspiked_set1) # get identifier for Ceruplasmin proteins ceru.acs <- protein.g(proteinGroup(ibspiked_set1),"CERU") # create a smaller ibspectra w/ only Ceruplasmins ib.ceru <- subsetIBSpectra(ibspiked_set1,protein=ceru.acs,direction="include") # write it to a file tf <- tempfile("isobar") write.table(as.data.frame(ib.ceru),sep="\t",file=tf,quote=FALSE) # read it again into an IBSpectra object ib.ceru2 <- readIBSpectra("iTRAQ4plexSpectra",tf,identifications.format="ibspectra") ib.ceru2 unlink(tf)data(ibspiked_set1) # get identifier for Ceruplasmin proteins ceru.acs <- protein.g(proteinGroup(ibspiked_set1),"CERU") # create a smaller ibspectra w/ only Ceruplasmins ib.ceru <- subsetIBSpectra(ibspiked_set1,protein=ceru.acs,direction="include") # write it to a file tf <- tempfile("isobar") write.table(as.data.frame(ib.ceru),sep="\t",file=tf,quote=FALSE) # read it again into an IBSpectra object ib.ceru2 <- readIBSpectra("iTRAQ4plexSpectra",tf,identifications.format="ibspectra") ib.ceru2 unlink(tf)
Various plots are implement to assure data quality, and accompany preprocessing and analysis.
reporterMassPrecision(x):Calculates and displays the deviation from the 'true' tag mass - as specified in the IBSpectra object - of each channel.
reporterIntensityPlot(x):Displays boxplots of intensity of channels before and after normalization - useful to check the result of normalization.
raplot(x,...):Ratio-Absolute intensity plot - will be deprecated by maplot
xIBSpectra object
...Parameters to plot function.
plotRatio(x,channel1,channel2,protein,...):Plots abundances of one protein
xIBSpectra object
channel1channel2protein...Parameters to plot function.
maplot(x,channel1,channel2,...):Creates a ratio-versus-intensity plot.
xIBSpectra object.
maplot2():Florian P. Breitwieser, Jacques Colinge
IBSpectra, isobar-preprocessing isobar-analysis
data(ibspiked_set1) maplot(ibspiked_set1,main="IBSpiked, not normalized") maplot(normalize(ibspiked_set1),main="IBSpiked, normalized")data(ibspiked_set1) maplot(ibspiked_set1,main="IBSpiked, not normalized") maplot(normalize(ibspiked_set1),main="IBSpiked, normalized")
Preprocessing is a necessary step prior to analysis of data. In a sequential order, it is often neccassary to correct isotope impurities, to normalize, and subtract additive noise.
correctIsotopeImpurities(x):Returns impurity corrected IBSpectra object by solving a linear system of equations. See also isotopeImpurities.
normalize(x,f=median,target="intensity",exclude.protein=NULL,
use.protein=NULL,f.doapply=TRUE,log=TRUE,channels=NULL,na.rm=FALSE):Normalizes the intensities for multiplicative errors. Those changes are most likely produced by pipetting errors, and different hybridization efficencies, but can also be due to biological reasons. By default, tag intensities are multiplied by a factor so that the median intensity is equal across tags.
f:f is applied to each column, unless f.doapply is FALSE.
Then f is supposed to compute column-wise statistics of the matrix of
intensities. E.g. colSums and colMeans.
target:One of "intensity" and "ratio".
exclude.proteinsSpectra of peptides which might come from these proteins are excluded. Use for example for contaminants and proteins depleted in the experiment.
use.protein:If specified, only spectra coming from this protein are used. Use when a protein is spiked-in as normalization control.
f.isglobal:If true, f is applied on each column.
If false, f is supposed to compute column-wise statistics of the matrix
of intensities. E.g. colSums and colMeans.
log:Used when target=ratio.
subtractAdditiveNoise(x,method="quantile",shared=TRUE,prob=0.01):method'quantile' method is supported for now. It take's the prob (0.01) quantile to estimate the noise level. This value is subtracted from all intensities, and all remaining intensities have to be at least that value.
probSee 'method'.
sharedIf channels are assumed similar in intensity and hence a shared noise level is reasonable. If not, then one level per channel is necessary.
exclude(x,proteins.to.exclude):Removes spectra which are assigned to proteins in protein.to.exclude from the object. This can be useful to remove contaminants. It create a new grouping based on the data which is left.
proteins.to.excludeProteins to exclude.
Florian P. Breitwieser, Jacques Colinge
ProteinGroup, IBSpectra, isobar-analysis, isobar-plots
data(ibspiked_set1) maplot(ibspiked_set1,main="IBSpiked, not normalized") maplot(normalize(ibspiked_set1),main="IBSpiked, normalized")data(ibspiked_set1) maplot(ibspiked_set1,main="IBSpiked, not normalized") maplot(normalize(ibspiked_set1),main="IBSpiked, normalized")
Generation of LaTeX and XLS reports is helped with
functions which facilitate the gathering of relevant information and
creation of tikz plots. create.reports parses properties (by
calling load.properties) and initialize environments and
computations (by calling initialize.env) required by the
reports, calls Sweave and pdflatex.
create.reports(properties.file = "properties.R", global.properties.file = system.file("report","properties.R", package = "isobar"), args = NULL, ..., recreate.properties.env = TRUE, recreate.report.env = TRUE) load.properties(properties.file = "properties.R", global.properties.file = system.file("report","properties.R",package="isobar"), args = NULL, ...) initialize.env(env, properties.env)create.reports(properties.file = "properties.R", global.properties.file = system.file("report","properties.R", package = "isobar"), args = NULL, ..., recreate.properties.env = TRUE, recreate.report.env = TRUE) load.properties(properties.file = "properties.R", global.properties.file = system.file("report","properties.R",package="isobar"), args = NULL, ...) initialize.env(env, properties.env)
properties.file |
File which holds the parameters for data
analysis and report generation. It is parsed as R code after the
global report configuration file |
global.properties.file |
|
args |
Additional (command line) arguments which overrids those
in |
... |
Additional properties. |
recreate.properties.env |
Whether a properties.env existing in the global environment should be used, or it should be recreated. |
recreate.report.env |
Whether a report.env existing in the global environment should be used, or it should be recreated. |
env |
Item to be initialized. |
properties.env |
Environment into which properties are read. |
The directory inst in the isobar installation directory
system.file("inst",package="isobar") contains R, Sweave, and LaTeX
files as examples of how to create XLS and PDF reports using isobar.
Call with Rscript. It is the main file which
parses command line options. --compile and
--zip are parsed directly and given as arguments to
create.reports. Other arguments are given
load.properties.
calls a perl script to generate a XLS report
generates a LaTeX quality control and analysis report
for the XLS report the script pl/tab2xls.pl is used, which concetenates CSV files to a XLS. See Perl requirements. Sweave is called on report/isobar-qc.Rnw and report/isobar-analysis.Rnw. All files are written the working directory.
Quality control Sweave file.
Data analysis Sweave file.
Default configuration for data analysis.
LaTeX functions for plotting tikz graphics, etc.
Florian P Breitwieser
IBSpectra, isobar-preprocessing isobar-analysis
ibspiked_set1 and ibspiked_set2 are objects of class iTRAQ4plexSpectra. It contains over 160 protein groups, over 1600 peptides from about 15,000 spectra each, mainly from background proteins and three spiked-in Ceruplasmins (CERU_HUMAN, CERU_MOUSE, CERU_RAT).
data(ibspiked_set1) data(ibspiked_set2) data(ib_phospho)data(ibspiked_set1) data(ibspiked_set2) data(ib_phospho)
iTRAQ4plexSpectra objects.
isobar publication. Acquired on Orbitrap instrument w/ 20 offline-fractions and HCD fragmentation.
data(ibspiked_set1) print(ibspiked_set1)data(ibspiked_set1) print(ibspiked_set1)
Plots ratio-versus-intensity for a selected protein against a reference channel.
maplot.protein(x, relative.to, protein, noise.model = NULL, channels = NULL, xlim = NULL, ylim = NULL, identify = FALSE, add = FALSE, pchs = NULL, log="xy", legend.pos = "topright", names = NULL, legend.cex = 0.8, cols = pchs, ltys = 1, main = protein, xlab = NULL, ylab = NULL, type="ma", show.lm = FALSE, ...)maplot.protein(x, relative.to, protein, noise.model = NULL, channels = NULL, xlim = NULL, ylim = NULL, identify = FALSE, add = FALSE, pchs = NULL, log="xy", legend.pos = "topright", names = NULL, legend.cex = 0.8, cols = pchs, ltys = 1, main = protein, xlab = NULL, ylab = NULL, type="ma", show.lm = FALSE, ...)
x |
IBSpectra object |
relative.to |
a character vector specifying reporter tag names. Either of length 1 or same length as channels. |
protein |
Protein group identifier. |
noise.model |
NoiseModel object. |
channels |
Reporter tag names. |
xlim |
See par. |
ylim |
See par. |
identify |
boolean. If |
add |
|
pchs |
a vector of the same length as |
log |
a character string which contains |
legend.pos |
see |
names |
a character string of the same length as |
legend.cex |
see |
cols |
a vector of the same length as |
ltys |
a vector of the same length as |
main |
a main title for the plot |
xlab |
a label for the x axis, defaults to a description of |
ylab |
a label for the y axis, defaults to a description of |
type |
type of plot |
... |
passed to |
show.lm |
show LM |
Florian P. Breitwieser
A NoiseModel represent the technical variation which is dependent on signal intensity.
new(type,ibspectra,reporterTagNames=NULL,one.to.one=TRUE,min.spectra=10,plot=FALSE,
pool=FALSE):Creates a new NoiseModel object based on ibspectra object.
type:A non-virtual class deriving from NoiseModel:
ExponentialNoiseModel, ExponentialNoANoiseModel,
InverseNoiseModel, InverseNoANoiseModel
reporterTagNames:When NULL, all channels from ibspectra are taken
(i.e. sampleNames(ibspectra)). Otherwise, specify
subset of names, or a matrix which defines the desireed combination of channels (nrow=2).
one.to.one:Set to false to learn noise model one a non one-to-one dataset
min.spectra:When one.to.one=FALSE, only take proteins with min.spectra to learn noise model.
plot:Set to true to plot data the noise model is learnt on.
pool:If false, a NoiseModel is estimated on each combination of channels indivdually, and then the parameters are averaged. If true, the ratios of all channels are pooled and then a NoiseModel is estimated.
noiseFunction:Gets the noise function.
parameter:Gets and sets the parameters for the noise function.
variance:Gets the variance for data points based on the noise function and parameters.
stddev:Convenience function, sqrt(variance(...)).
lowIntensity:Gets and sets the low intensity slot, denoting the noise region.
naRegion:Gets and sets the na.region slot.
data(ibspiked_set1) ceru.proteins <- protein.g(proteinGroup(ibspiked_set1),"CERU") # normalize ibspiked_set1 <- normalize(correctIsotopeImpurities(ibspiked_set1)) # remove spiked proteins ibspiked_set1.noceru <- exclude(ibspiked_set1,ceru.proteins) ibspiked_set1.justceru <- subsetIBSpectra(ibspiked_set1,protein=ceru.proteins,direction="include") # learn noise models nm.i <- new("InverseNoiseModel",ibspiked_set1.noceru) nm.e <- new("ExponentialNoiseModel",ibspiked_set1.noceru) #learn on non-one.to.one data: not normalized, with spiked proteins nm.n <- new("ExponentialNoiseModel",ibspiked_set1.justceru,one.to.one=FALSE) maplot(ibspiked_set1,noise.model=c(nm.e,nm.i,nm.n),ylim=c(0.1,10))data(ibspiked_set1) ceru.proteins <- protein.g(proteinGroup(ibspiked_set1),"CERU") # normalize ibspiked_set1 <- normalize(correctIsotopeImpurities(ibspiked_set1)) # remove spiked proteins ibspiked_set1.noceru <- exclude(ibspiked_set1,ceru.proteins) ibspiked_set1.justceru <- subsetIBSpectra(ibspiked_set1,protein=ceru.proteins,direction="include") # learn noise models nm.i <- new("InverseNoiseModel",ibspiked_set1.noceru) nm.e <- new("ExponentialNoiseModel",ibspiked_set1.noceru) #learn on non-one.to.one data: not normalized, with spiked proteins nm.n <- new("ExponentialNoiseModel",ibspiked_set1.justceru,one.to.one=FALSE) maplot(ibspiked_set1,noise.model=c(nm.e,nm.i,nm.n),ylim=c(0.1,10))
1,2,3,4,5,8,9,10 -> 1-5,8-10
number.ranges(numbers)number.ranges(numbers)
numbers |
numeric |
character
Florian P Breitwieser
number.ranges(c(1,2,3,9,3,10,8,11))number.ranges(c(1,2,3,9,3,10,8,11))
Functions to display the modification sites observed for each protein isoform and count the number of modified residues per protein.
observedKnownSites(protein.group, protein.g, ptm.info, modif, modification.name = NULL) modif.site.count(protein.group, protein.g = reporterProteins(protein.group), modif, take = max) modif.sites(protein.group, protein.g = reporterProteins(protein.group), modif)observedKnownSites(protein.group, protein.g, ptm.info, modif, modification.name = NULL) modif.site.count(protein.group, protein.g = reporterProteins(protein.group), modif, take = max) modif.sites(protein.group, protein.g = reporterProteins(protein.group), modif)
protein.group |
ProteinGroupb object. |
protein.g |
protein group identifier. |
ptm.info |
ptm information data.frame, see ?getPtmInfo. |
modif |
Modification to track, e.g. 'PHOS'. |
modification.name |
Value to filter 'modification.name' column in ptm.info. |
take |
should be either max or min: When multiple isoforms are present, which value should be taken for the count? |
Florian P. Breitwieser
data(ib_phospho) data(ptm.info) # Modification sites of reporter proteins: # a list of protein groups, # containing sub-lists of identified sites for each isoform protein.modif.sites <- sort(modif.site.count(proteinGroup(ib_phospho),modif="PHOS")) # Details on modification sites of proteins # detected with most modifications modif.sites(proteinGroup(ib_phospho),modif="PHOS",protein.g=names(tail(protein.modif.sites))) # How many sites are known, and how many known sites have been observed? observedKnownSites(proteinGroup(ib_phospho),modif="PHOS",protein.g=names(tail(protein.modif.sites)),ptm.info=ptm.info,modification.name="Phospho")data(ib_phospho) data(ptm.info) # Modification sites of reporter proteins: # a list of protein groups, # containing sub-lists of identified sites for each isoform protein.modif.sites <- sort(modif.site.count(proteinGroup(ib_phospho),modif="PHOS")) # Details on modification sites of proteins # detected with most modifications modif.sites(proteinGroup(ib_phospho),modif="PHOS",protein.g=names(tail(protein.modif.sites))) # How many sites are known, and how many known sites have been observed? observedKnownSites(proteinGroup(ib_phospho),modif="PHOS",protein.g=names(tail(protein.modif.sites)),ptm.info=ptm.info,modification.name="Phospho")
Report the peptide count, spectral count and sequence coverage for supplied proteins.
peptide.count(protein.group, protein.g = reporterProteins(protein.group), specificity = c("reporter-specific", "group-specific", "unspecific"), ...) spectra.count(protein.group, protein.g = reporterProteins(protein.group), specificity = c("reporter-specific", "group-specific", "unspecific"), modif = NULL, ...) sequence.coverage(protein.group, protein.g = reporterProteins(protein.group), specificity = c("reporter-specific", "group-specific", "unspecific"), simplify = TRUE, ...)peptide.count(protein.group, protein.g = reporterProteins(protein.group), specificity = c("reporter-specific", "group-specific", "unspecific"), ...) spectra.count(protein.group, protein.g = reporterProteins(protein.group), specificity = c("reporter-specific", "group-specific", "unspecific"), modif = NULL, ...) sequence.coverage(protein.group, protein.g = reporterProteins(protein.group), specificity = c("reporter-specific", "group-specific", "unspecific"), simplify = TRUE, ...)
protein.group |
ProteinGroup object. |
protein.g |
Protein group identifier. |
specificity |
Specificity of peptides. |
modif |
Only count peptides having a certain modification. |
simplify |
If simplify=TRUE, a named numeric vector is returned, with the mean sequence coverage of the ACs of each protein.g supplied. Else, a list with the length of protein.g is returned having the sequence coverage for each protein AC. |
... |
Further arguments to |
Florian P Breitwieser
calculate.emPAI,
calculate.dNSAF,
ProteinGroup
data(ibspiked_set1) sc <- spectra.count(proteinGroup(ibspiked_set1)) pc <- peptide.count(proteinGroup(ibspiked_set1)) plot(jitter(sc),jitter(pc),log="xy")data(ibspiked_set1) sc <- spectra.count(proteinGroup(ibspiked_set1)) pc <- peptide.count(proteinGroup(ibspiked_set1)) plot(jitter(sc),jitter(pc),log="xy")
A set of functions to create ratios within groups and summarize them.
proteinRatios serves as hub and calls combn.matrix,
combn.protein.tbl and summarize.ratios successively. It can be
used to calculate intra-class and inter-class ratios, to assess ratios and
variability within and over cases.
proteinRatios(ibspectra, noise.model, reporterTagNames = NULL, proteins = reporterProteins(proteinGroup(ibspectra)), peptide = NULL, cl = classLabels(ibspectra), combn.method = "global", combn.vs = NULL, symmetry = FALSE, summarize = FALSE, summarize.method = "mult.pval", min.detect = NULL, strict.sample.pval = TRUE, strict.ratio.pval = TRUE, orient.div = 0, sign.level = 0.05, sign.level.rat = sign.level, sign.level.sample = sign.level, ratiodistr = NULL, zscore.threshold = NULL, variance.function = "maxi", combine = FALSE, p.adjust = NULL, reverse = FALSE, cmbn = NULL, before.summarize.f = NULL, ...) peptideRatiosNotQuant(ibspectra, ..., peptide = unique(fData(ibspectra)[!fData(ibspectra)[["use.for.quant"]], c("peptide", "modif", "site.probs")])) peptideRatios(ibspectra, ..., peptide = peptides(proteinGroup(ibspectra), columns = c("peptide", "modif"))) combn.matrix(x, method = "global", cl = NULL, vs = NULL) combn.protein.tbl(cmbn, reverse = FALSE, ...) summarize.ratios(ratios, by.column = "ac", summarize.method = "mult.pval", min.detect = NULL, n.combination = NULL, strict.sample.pval = TRUE, strict.ratio.pval = TRUE, orient.div = 0, sign.level = 0.05, sign.level.rat = sign.level, sign.level.sample = sign.level, variance.function = "maxi", ratiodistr = NULL)proteinRatios(ibspectra, noise.model, reporterTagNames = NULL, proteins = reporterProteins(proteinGroup(ibspectra)), peptide = NULL, cl = classLabels(ibspectra), combn.method = "global", combn.vs = NULL, symmetry = FALSE, summarize = FALSE, summarize.method = "mult.pval", min.detect = NULL, strict.sample.pval = TRUE, strict.ratio.pval = TRUE, orient.div = 0, sign.level = 0.05, sign.level.rat = sign.level, sign.level.sample = sign.level, ratiodistr = NULL, zscore.threshold = NULL, variance.function = "maxi", combine = FALSE, p.adjust = NULL, reverse = FALSE, cmbn = NULL, before.summarize.f = NULL, ...) peptideRatiosNotQuant(ibspectra, ..., peptide = unique(fData(ibspectra)[!fData(ibspectra)[["use.for.quant"]], c("peptide", "modif", "site.probs")])) peptideRatios(ibspectra, ..., peptide = peptides(proteinGroup(ibspectra), columns = c("peptide", "modif"))) combn.matrix(x, method = "global", cl = NULL, vs = NULL) combn.protein.tbl(cmbn, reverse = FALSE, ...) summarize.ratios(ratios, by.column = "ac", summarize.method = "mult.pval", min.detect = NULL, n.combination = NULL, strict.sample.pval = TRUE, strict.ratio.pval = TRUE, orient.div = 0, sign.level = 0.05, sign.level.rat = sign.level, sign.level.sample = sign.level, variance.function = "maxi", ratiodistr = NULL)
ibspectra |
IBSpectra object |
x |
for combn.matrix: reporter names. See reporterTagNames. argument of proteinRatios. |
ratios |
result of combn.protein.tbl |
by.column |
Column(s) which are the identifiers. Usually 'ac', 'peptide' or c('peptide','modif') |
cmbn |
result of combn.matrix |
before.summarize.f |
Function which is called after calculating ratios before summarizing them. |
noise.model |
NoiseModel for spectra variances |
reporterTagNames |
Reporter tags to use. By default all reporterTagNames of ibspectra object. |
proteins |
proteins for which ratios are calculated - defaults to all proteins with peptides specific to them. |
peptide |
peptides for which ratios are calculated. |
cl |
Class labels. See also ?classLabels. |
vs |
Class label or reporter tag name. When |
combn.method |
"global", "interclass", "intra-class", "versus.class" or "versus.channel". Defines which ratios are computed, based on class labels cl |
method |
See combn.method |
combn.vs |
vs argument for combn, if combn.method is "versus.class" or "versus.channel". |
symmetry |
If true, reports also the inverse ratio |
summarize |
If true, ratios for each protein are summarized. |
summarize.method |
"isobar", for now. |
min.detect |
How many times must a ratio for a protein be present when summarizing? When NULL, defaults to the maximum number of combinations. |
strict.sample.pval |
If true, missing ratios are penalized by giving them a sample.pval of 0.5. |
strict.ratio.pval |
If true, take all ratios into account. If false, only take ratios into account which are in the same direction as the majority of ratios |
orient.div |
Number of ratios which might go in the wrong direction. |
sign.level |
Significance level |
sign.level.rat |
Significance level on ratio p-value |
sign.level.sample |
Significance level on sample p-value |
ratiodistr |
Protein ratio distribution |
variance.function |
Variance function |
zscore.threshold |
z-score threshold to apply |
... |
Passed to estimateRatio() |
combine |
If true, a single ratio for all proteins and peptides, resp., is calculated. See |
p.adjust |
Set to one of p.adjust.methods to adjust ratio p-values for multiple comparisions. See |
reverse |
reverse |
n.combination |
number of combinations possible |
'data.frame': 11 variables:
lratio |
log ratio |
variance |
variance |
n.spectra |
Number of spectra used for quantification |
p.value.rat |
Signal p-value (NA if ratiodistr is missing) |
p.value.sample |
Sample p-value (NA if ratiodistr is missing) |
is.significant |
Is the ratio significant? (NA if ratiodistr is missing) |
protein |
Protein quantified |
r1 |
r1 |
r2 |
r2 |
Florian P Breitwieser, Jacques Colinge
IBSpectra, isobar-preprocessing isobar-analysis
combn.matrix(114:117,method="interclass",cl=as.character(c(1,1,2,2))) combn.matrix(114:117,method="interclass",cl=as.character(c(1,1,2,2))) combn.matrix(114:117,method="global") data(ibspiked_set1) data(noise.model.hcd) ceru.proteins <- c("P13635","Q61147") proteinRatios(ibspiked_set1,noise.model=noise.model.hcd,proteins=ceru.proteins,cl=c("T","T","C","C"),combn.method="interclass",summarize=TRUE)combn.matrix(114:117,method="interclass",cl=as.character(c(1,1,2,2))) combn.matrix(114:117,method="interclass",cl=as.character(c(1,1,2,2))) combn.matrix(114:117,method="global") data(ibspiked_set1) data(noise.model.hcd) ceru.proteins <- c("P13635","Q61147") proteinRatios(ibspiked_set1,noise.model=noise.model.hcd,proteins=ceru.proteins,cl=c("T","T","C","C"),combn.method="interclass",summarize=TRUE)
The ProteinGroup class is a container for identified peptides and proteins, and groups them to distinguish proteins with specific peptides.
ProteinGroup(from,template=NULL,proteinInfo=data.frame()) protein.ac(x, protein.g) protein.g(x, pattern, variables=c("AC","name"), ...)ProteinGroup(from,template=NULL,proteinInfo=data.frame()) protein.ac(x, protein.g) protein.g(x, pattern, variables=c("AC","name"), ...)
from |
|
template |
'template' ProteinGroup object for grouping. |
x |
ProteinGroup object |
protein |
character string |
proteinInfo |
data.frame for proteinInfo slot |
protein.g |
character string, denoting a 'protein group'. |
pattern |
character string, see |
variables |
|
... |
Passed on to |
The ProteinGroup class stores spectrum to peptide to protein mapping.
The proteins are grouped by their evidence, i. e. peptides:
Peptides with changes only from Leucin to Isoleucin are considered the same, as they cannot be distinguished by MS.
Proteins which are detected with the same peptides are grouped together to a 'indistinguishable protein'- normally these are splice variants.
Proteins with specific peptides are 'reporters'.
Proteins with no specific peptides are grouped under these 'reporters.
This information is stored in six slots:
spectra.n.peptidesa named 'character' vector, names being spectrum identifier and values are peptides.
peptide.n.proteinsa 'data.frame' containing the number of proteins the peptides could derive from.
peptide.n.proteina character 'matrix' linking peptides to proteins.
indistinguishable.proteinsa 'matrix' contain.
ProteinGroup(tbl.prot.pep,template=NULL):
Creates a ProteinGroup object.
tbl.prot.pepA 'data.frame' with three columns: 1. Protein, 2. Peptide, 3. Spectrum.
templateOptional ProteinGroup object the grouping is based upon.
In the code snippets below, x is a ProteinGroup object.
as(from, "ProteinGroup"):Creates a ProteinGroup object from a data.frame.
as.data.frame(x, row.names = NULL, optional = FALSE):Creates a data.frame with columns protein (character),
peptide (character), spectrum.
as.concise.data.frame(from):Creates a 'concise' data.frame with one spectrum per row, and protein ACs combined
In the following code snippets, x is a ProteinGroup object.
spectrumToPeptide(x):Gets spectrum to peptide assignment.
peptideInfo(x):Peptide information such as protein start position.
peptideSpecificity(x):Gets a 'data.frame' containing the peptide specificity: they can be reporter-specific, group-specific, or non-specific.
peptideNProtein(x):Gets peptide to protein assignment.
indistinguishableProteins(x):Gets the proteins which cannot be distinguished based on peptide evidence.
proteinGroupTable:Gets the protein grouping, listing reporters and group members.
peptides(x,protein=NULL,specificity=c("reporter-specific",
"group-specific","unspecific"),columns="peptide",set=union):Gets all peptides detected, or just those for a protein with the
defined specificity. columns might define multiple columns of
peptideSpecificity(x). set=union returns the union of
peptides of all proteins defined, set=intersect returns the intersection.
Florian P. Breitwieser
tbl <- data.frame(spectrum=1:14,peptide=c(rep(letters[1:3],4),"a","x"), modif=":",start.pos=1, protein=c(rep(c("A","B"),each=6),"C","D")) pg <- ProteinGroup(tbl) pg proteinGroupTable(pg) data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) ceru.proteins <- protein.g(pg,"CERU") ## all ceru peptides peptides(pg,ceru.proteins) ## peptides shared by all ceru proteins peptides(pg,ceru.proteins, set=intersect)tbl <- data.frame(spectrum=1:14,peptide=c(rep(letters[1:3],4),"a","x"), modif=":",start.pos=1, protein=c(rep(c("A","B"),each=6),"C","D")) pg <- ProteinGroup(tbl) pg proteinGroupTable(pg) data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) ceru.proteins <- protein.g(pg,"CERU") ## all ceru peptides peptides(pg,ceru.proteins) ## peptides shared by all ceru proteins peptides(pg,ceru.proteins, set=intersect)
proteinInfo slot in Proteingroup objects contains information about proteins.
proteinInfo method allows to get and set it.
getProteinInfoFromUniprot downloads information of contained proteins
from Uniprot, getProteinInfoFromBiomart from Biomart.
## S4 method for signature 'ProteinGroup' proteinInfo(x) ## S4 method for signature 'ProteinGroup,character,missing' proteinInfo(x, protein.g, select="name", collapse=", ", simplify = TRUE, do.warn = TRUE) ## S4 method for signature 'ProteinGroup,missing,character' proteinInfo(x, protein.ac, select="name", collapse=", ", simplify = TRUE, do.warn = TRUE) proteinInfoIsOnSpliceVariants(protein.info) # getProteinInfoFromUniprot(x, splice.by = 200, fields = c(accession = "id", name # = "entry%20name", protein_name = "protein%20names", # gene_name = "genes", organism = "organism", length = # "length", sequence = "sequence")) getProteinInfoFromTheInternet(x) getProteinInfoFromNextProt(x) getProteinInfoFromBiomart(x, database = "Uniprot") getProteinInfoFromBioDb(x, ..., con = NULL) getProteinInfoFromEntrez(x, splice.by = 200)## S4 method for signature 'ProteinGroup' proteinInfo(x) ## S4 method for signature 'ProteinGroup,character,missing' proteinInfo(x, protein.g, select="name", collapse=", ", simplify = TRUE, do.warn = TRUE) ## S4 method for signature 'ProteinGroup,missing,character' proteinInfo(x, protein.ac, select="name", collapse=", ", simplify = TRUE, do.warn = TRUE) proteinInfoIsOnSpliceVariants(protein.info) # getProteinInfoFromUniprot(x, splice.by = 200, fields = c(accession = "id", name # = "entry%20name", protein_name = "protein%20names", # gene_name = "genes", organism = "organism", length = # "length", sequence = "sequence")) getProteinInfoFromTheInternet(x) getProteinInfoFromNextProt(x) getProteinInfoFromBiomart(x, database = "Uniprot") getProteinInfoFromBioDb(x, ..., con = NULL) getProteinInfoFromEntrez(x, splice.by = 200)
x |
ProteinGroup object |
protein.g |
Protein group identifier. If supplied, only information for these proteins is returned. |
protein.ac |
Protein ACs. If supplied, only information for these proteins is returned. |
select |
indicating columns to select. See Details. |
collapse |
passed to |
simplify |
If true, a vector or matrix is returned, with the pasted protein information. If false, a list is returned. |
do.warn |
If true, report diagnostic warning messages. |
splice.by |
Chunk size for query of Uniprot database. |
database |
database from which the ACs stem from. Only Uniprot is supported for now. |
con |
database connection |
fields |
mapping of CSV field names to proteinInfo field names |
... |
arguments to build database connection. |
protein.info |
protein info data.frame |
proteinInfo contains columns accession, name,
gene_name, protein_name, and possibly length
and sequence. accession is mapped with
the entry AC is mapped to the entry AC in the database.
getProteinInfoFromUniprot is the preferred methods to get the information.
getProteinInfoFromBioDb is an example how to implement the query on a
local database. Depending on the database, protein information might be
available on protein ACs or also on the specific splice variants. This can be
queried with the proteinInfoIsOnSpliceVariants function.
data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) ## Not run: proteinInfo(pg) <- getProteinInfoFromUniprot(pg) proteinInfo(pg) <- getProteinInfoFromBiomart(pg) ## End(Not run) proteinInfo(pg,protein.g="P13635") protein.g(pg,"CERU")data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) ## Not run: proteinInfo(pg) <- getProteinInfoFromUniprot(pg) proteinInfo(pg) <- getProteinInfoFromBiomart(pg) ## End(Not run) proteinInfo(pg,protein.g="P13635") protein.g(pg,"CERU")
Convenience functions to retrieve protein gene names and description for a list of protein group identifiers.
proteinNameAndDescription(protein.group, protein.g = reporterProteins(protein.group), collapse = FALSE) proteinGeneName(protein.group, protein.g = reporterProteins(protein.group)) proteinDescription(protein.group, protein.g = reporterProteins(protein.group)) proteinID(protein.group, protein.g = reporterProteins(protein.group))proteinNameAndDescription(protein.group, protein.g = reporterProteins(protein.group), collapse = FALSE) proteinGeneName(protein.group, protein.g = reporterProteins(protein.group)) proteinDescription(protein.group, protein.g = reporterProteins(protein.group)) proteinID(protein.group, protein.g = reporterProteins(protein.group))
protein.group |
ProteinGroup object. |
protein.g |
protein group identifier. |
collapse |
If TRUE, the information for all protein.gs is combined. |
Florian P Breitwieser
data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) protein.gs <- protein.g(pg,"CERU") protein.gs proteinNameAndDescription(pg,protein.gs) proteinNameAndDescription(pg,protein.gs,collapse=TRUE) proteinGeneName(pg,protein.gs) proteinDescription(pg,protein.gs) proteinID(pg,protein.gs)data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) protein.gs <- protein.g(pg,"CERU") protein.gs proteinNameAndDescription(pg,protein.gs) proteinNameAndDescription(pg,protein.gs,collapse=TRUE) proteinGeneName(pg,protein.gs) proteinDescription(pg,protein.gs) proteinID(pg,protein.gs)
Reshape output of proteinRatios into wide format
ratiosReshapeWide(quant.tbl, vs.class = NULL, sep = ".", cmbn = NULL, short.names = FALSE)ratiosReshapeWide(quant.tbl, vs.class = NULL, sep = ".", cmbn = NULL, short.names = FALSE)
quant.tbl |
Output of proteinRatios or peptideRatios. |
vs.class |
Only return ratios where class1 is vs.class |
sep |
Separator for column names in the reshape. |
cmbn |
Not functional. |
short.names |
If vs.class is set and short.names=TRUE, then the comparision name will be i.e. 'class2' instead of 'class2/class1'. |
Florian P. Breitwieser
Methods for function reporter.protein in package isobar
signature(x = "ProteinGroup", protein.g = "character")Get reporter protein for protein group identifier.
Sanitizes strings for LaTeX
sanitize(str, dash = TRUE)sanitize(str, dash = TRUE)
str |
character string to be escaped |
dash |
shoud a dash ('-') should be escaped to a '\nobreakdash-'? |
escaped character
iQuantitator,Florian P Breitwieser
sanitize("\textbf{123-123}")sanitize("\textbf{123-123}")
Peptides can appear in multiple proteins and therefore have different specificities.
reporter specific: peptides specific to reporter. group specific: peptides specific to the group. unspecific: peptides shared with other proteins.
Spectral count for peptides and proteins in ProteinGroup objects.
It can - other than spectra.count - quantify the spectra count on
the level of peptides, potenitally modifed, too,
spectra.count2(ibspectra, value = reporterProteins(protein.group), type = "protein.g", specificity = c("reporter-specific", "group-specific", "unspecific"), modif = NULL, combine = FALSE, subset = NULL, require.quant = NULL, ...)spectra.count2(ibspectra, value = reporterProteins(protein.group), type = "protein.g", specificity = c("reporter-specific", "group-specific", "unspecific"), modif = NULL, combine = FALSE, subset = NULL, require.quant = NULL, ...)
ibspectra |
IBSpectra object. |
value |
List of protein group identifiers or peptides. |
type |
Either 'protein.g' or 'peptide'. |
specificity |
Specificity of peptides. |
modif |
Only count peptides having a certain modification. |
combine |
If TRUE, only one combined result is returned. |
subset |
Allows to specify an |
require.quant |
If not NULL, it may be 'any' or 'all' to only consider spectra with quantitative information in at least one or all channels. |
... |
Further arguments to |
Florian P Breitwieser
data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) protein.gs <- protein.g(pg,"CERU") sc <- spectra.count2(ibspiked_set1,protein.gs) sc.ik <- spectra.count2(ibspiked_set1,protein.gs,modif="iTRAQ4plex_K") rbind(spectra.counts=sc,spectra.counts_iTRAQk=sc.ik)data(ibspiked_set1) pg <- proteinGroup(ibspiked_set1) protein.gs <- protein.g(pg,"CERU") sc <- spectra.count2(ibspiked_set1,protein.gs) sc.ik <- spectra.count2(ibspiked_set1,protein.gs,modif="iTRAQ4plex_K") rbind(spectra.counts=sc,spectra.counts_iTRAQk=sc.ik)
Returns an IBSpectra object which is a subset of the input, excluding or exclusively containing the peptides or proteins supplied.
subsetIBSpectra(x, protein = NULL, peptide = NULL, direction = "exclude", specificity = c(REPORTERSPECIFIC, GROUPSPECIFIC, UNSPECIFIC), ...)subsetIBSpectra(x, protein = NULL, peptide = NULL, direction = "exclude", specificity = c(REPORTERSPECIFIC, GROUPSPECIFIC, UNSPECIFIC), ...)
x |
IBSpectra object. |
protein |
Protein group identifiers. Use |
peptide |
Peptide sequences. |
direction |
either 'include' or 'exclude'. |
specificity |
When 'protein' is supplied: Which peptides should
be selected? See |
... |
Further arguments passed to |
Florian P Breitwieser
protein.g, spectrumSel, specificities
data(ibspiked_set1) # get Keratin proteins keratin.proteins <- protein.g(proteinGroup(ibspiked_set1),"Keratin") # exclude Keratin proteins subsetIBSpectra(ibspiked_set1,protein=keratin.proteins,direction="exclude")data(ibspiked_set1) # get Keratin proteins keratin.proteins <- protein.g(proteinGroup(ibspiked_set1),"Keratin") # exclude Keratin proteins subsetIBSpectra(ibspiked_set1,protein=keratin.proteins,direction="exclude")
"Tlsd"
Location scale family T distribution, based on the original T function.
Objects can be created by calls of the form new("Tlsd", df, location, scale).
gaps:Object of class "OptionalMatrix" ~~
img:Object of class "rSpace" ~~
param:Object of class "OptionalParameter" ~~
r:Object of class "function" ~~
d:Object of class "OptionalFunction" ~~
p:Object of class "OptionalFunction" ~~
q:Object of class "OptionalFunction" ~~
.withSim:Object of class "logical" ~~
.withArith:Object of class "logical" ~~
.logExact:Object of class "logical" ~~
.lowerExact:Object of class "logical" ~~
Symmetry:Object of class "DistributionSymmetry" ~~
Class "AbscontDistribution", directly.
Class "UnivariateDistribution", by class "AbscontDistribution", distance 2.
Class "AcDcLcDistribution", by class "AbscontDistribution", distance 2.
Class "Distribution", by class "AbscontDistribution", distance 3.
Class "UnivDistrListOrDistribution", by class "AbscontDistribution", distance 3.
No methods defined with class "Tlsd" in the signature.
Florian P. Breitwieser, based on original T distribution class.
showClass("Tlsd")showClass("Tlsd")
"TlsParameter"
The parameter of a location scale t distribution, used by Tlsd-class
Objects can be created by calls of the form new("TlsParameter", ...).
Usually an object of this class is not needed on its own, it is generated automatically when an object of the class
Tlsd is instantiated.
df:Object of class "numeric" ~~
location:Object of class "numeric" ~~
scale:Object of class "numeric" ~~
name:Object of class "character" ~~
Class "Parameter", directly.
Class "OptionalParameter", by class "Parameter", distance 2.
No methods defined with class "TlsParameter" in the signature.
Florian P. Breitwieser, based on original TParameter class.
showClass("TlsParameter")showClass("TlsParameter")
Write identifications into a format suitable for Hscore.
writeHscoreData(outfile, ids, massfile = "defs.txt")writeHscoreData(outfile, ids, massfile = "defs.txt")
outfile |
Output file. |
ids |
IBSpectra identifications data.frame (ie fData). |
massfile |
Definition file for Hscore. |
Florian P. Breitwieser
Write IBSpectra file using write.table with defaults in a format readable by readIBSpectra.
writeIBSpectra(ibspectra, file, sep = "\t", row.names = FALSE, ...)writeIBSpectra(ibspectra, file, sep = "\t", row.names = FALSE, ...)
ibspectra |
IBSpectra object |
file |
file name. |
sep |
field separator string. |
row.names |
indicates whether row.names should be written. |
... |
further arguments to |
Florian P Breitwieser