## @knitr env, include=FALSE, echo=FALSE, cache=FALSE library("knitr") opts_chunk$set(fig.align = 'center', fig.show = 'hold', par = TRUE, prompt = TRUE, eval = TRUE, comment = NA) options(replace.assign = TRUE, width = 55) suppressPackageStartupMessages(library("MSnbase")) suppressWarnings(suppressPackageStartupMessages(library("pRoloc"))) suppressPackageStartupMessages(library("pRolocdata")) suppressPackageStartupMessages(library("class")) set.seed(1) ## @knitr libraries library("MSnbase") library("pRoloc") library("pRolocdata") ## @knitr setcols setStockcol(paste0(getStockcol(), 70)) ## @knitr readCsvData0 ## The original data for replicate 1, available ## from the pRolocdata package f0 <- dir(system.file("extdata", package = "pRolocdata"), full.names = TRUE, pattern = "pr800866n_si_004-rep1.csv") csv <- read.csv(f0) ## @knitr showOrgCsv head(csv, n=3) ## @knitr readCsvData1, tidy = FALSE ## The quantitation data, from the original data f1 <- dir(system.file("extdata", package = "pRoloc"), full.names = TRUE, pattern = "exprsFile.csv") exprsCsv <- read.csv(f1) ## Feature meta-data, from the original data f2 <- dir(system.file("extdata", package = "pRoloc"), full.names = TRUE, pattern = "fdataFile.csv") fdataCsv <- read.csv(f2) ## Sample meta-data, a new file f3 <- dir(system.file("extdata", package = "pRoloc"), full.names = TRUE, pattern = "pdataFile.csv") pdataCsv <- read.csv(f3) ## @knitr showExprsFile head(exprsCsv, n=3) ## @knitr showFdFile head(fdataCsv, n=3) ## @knitr showPdFile pdataCsv ## @knitr makeMSnSet tan2009r1 <- readMSnSet(exprsFile = f1, featureDataFile = f2, phenoDataFile = f3, sep = ",") tan2009r1 ## @knitr showSubset smallTan <- tan2009r1[1:5, 1:2] dim(smallTan) exprs(smallTan) ## @knitr rmtan, echo=FALSE ## remove manullay constructred data rm(tan2009r1) data(tan2009r1) ## @knitr loadTan1 data(tan2009r1) ## @knitr lookAtTan getMarkers(tan2009r1) getMarkers(tan2009r1, fcol = "PLSDA") ## @knitr realtiveQuants summary(rowSums(exprs(tan2009r1))) ## @knitr norm, echo=TRUE, message=FALSE, cache=TRUE ## create a small illustrative test data data(itraqdata) itraqdata <- quantify(itraqdata, method = "trap", reporters = iTRAQ4, verbose = FALSE, parallel = FALSE) ## the quantification data head(exprs(itraqdata), n = 3) summary(rowSums(exprs(itraqdata))) ## normalising to the sum of feature intensitites itraqnorm <- normalise(itraqdata, method = "sum") processingData(itraqnorm) head(exprs(itraqnorm), n = 3) summary(rowSums(exprs(itraqnorm))) ## @knitr featurenames head(featureNames(tan2009r1)) ## @knitr showplotdist, echo=TRUE, eval=FALSE, prompt=FALSE ## ## indices of the mito markers ## j <- which(fData(tan2009r1)$markers == "mitochondrion") ## ## indices of all proteins assigned to the mito ## i <- which(fData(tan2009r1)$PLSDA == "mitochondrion") ## plotDist(tan2009r1[i, ], ## markers = featureNames(tan2009r1)[j]) ## @knitr plotdist1, dev='pdf', fig.width=9, fig.height=7, echo=FALSE par(mfrow = c(2,2), mar = c(4, 4, 2, 0.1)) cls <- getStockcol()[1:4] plotDist(tan2009r1[which(fData(tan2009r1)$PLSDA == "mitochondrion"), ], markers = featureNames(tan2009r1) [which(fData(tan2009r1)$markers == "mitochondrion")], mcol = cls[1], main = "mitochondrion") plotDist(tan2009r1[which(fData(tan2009r1)$PLSDA == "ER/Golgi"), ], markers = featureNames(tan2009r1) [which(fData(tan2009r1)$markers == "ER")], mcol = cls[2], main = "ER") plotDist(tan2009r1[which(fData(tan2009r1)$PLSDA == "ER/Golgi"), ], markers = featureNames(tan2009r1) [which(fData(tan2009r1)$markers == "Golgi")], mcol = cls[3], main = "Golgi") plotDist(tan2009r1[which(fData(tan2009r1)$PLSDA == "PM"), ], markers = featureNames(tan2009r1) [which(fData(tan2009r1)$markers == "PM")], mcol = cls[4], main = "PM") ## @knitr plot2d, dev='pdf', fig.width=5, fig.height=5, echo=TRUE plot2D(tan2009r1, fcol = "PLSDA", fpch = "markers") addLegend(tan2009r1, fcol = "PLSDA", where = "bottomright", bty = "n", cex = .7) ## @knitr plot2dnull, dev='pdf', fig.width=5, fig.height=5, echo=TRUE plot2D(tan2009r1, fcol = NULL) ## @knitr plotKmeans, dev='pdf', fig.width=8, fig.height=6, echo=TRUE kcl <- MLearn( ~ ., tan2009r1, kmeansI, centers=5) plot(kcl, exprs(tan2009r1)) ## @knitr plotHclust, dev='pdf', fig.width=8, fig.height=6, echo=TRUE, tidy = FALSE hcl <- MLearn( ~ ., tan2009r1, hclustI(distFun = dist, cutParm = list(k = 5))) plot(hcl, labels = FALSE) ## @knitr plotPam, dev='pdf', fig.width=8, fig.height=6, echo=TRUE pcl <- MLearn( ~ ., tan2009r1, pamI(dist), k = 5) plot(pcl, data = exprs(tan2009r1)) ## @knitr svmParamOptim, cache = TRUE, message = FALSE params <- svmOptimisation(tan2009r1, times = 10, xval = 5, verbose = FALSE) params ## @knitr params, dev='pdf', fig.width=4, fig.height=4, echo=TRUE, out.width='.49\\linewidth' plot(params) levelPlot(params) ## @knitr f1count f1Count(params) getParams(params) ## @knitr svmRes0, warning=FALSE, eval = FALSE, tidy = FALSE ## ## manual setting of parameters ## svmres <- svmClassification(tan2009r1, sigma = 1, cost = 1) ## @knitr svmRes, warning=FALSE, tidy = FALSE ## using default best parameters svmres <- svmClassification(tan2009r1, params) processingData(svmres) tail(fvarLabels(svmres), 4) ## @knitr getPredictions p1 <- getPredictions(svmres, fcol = "svm") minprob <- median(fData(svmres)$svm.scores) p2 <- getPredictions(svmres, fcol = "svm", t = minprob) table(p1, p2) ## @knitr predscoresPlot, eval=FALSE ## boxplot(svm.scores ~ svm,data = fData(svmres)) ## abline(h = minprob) ## @knitr svmresfig, dev='pdf', fig.width=5, fig.height=5, echo=TRUE ptsze <- exp(fData(svmres)$svm.scores) - 1 plot2D(svmres, fcol = "svm", fpch = "markers", cex = ptsze) addLegend(svmres, fcol = "svm", where = "bottomright", bty = "n", cex = .5) ## @knitr runPhenoDisco, eval=FALSE, warning=FALSE ## pdres <- phenoDisco(tan2009r1, GS = 10, times = 100, fcol = "PLSDA") ## @knitr loadpdres, echo=FALSE fn <- dir(system.file("extdata", package = "pRoloc"), full.names = TRUE, pattern = "pdres.rda") load(fn) rm(fn) ## @knitr phenoDiscoFvar processingData(pdres) tail(fvarLabels(pdres), 3) ## @knitr pdresfig, dev='pdf', fig.width=5, fig.height=5, echo=TRUE plot2D(pdres, fcol = "pd") addLegend(pdres, fcol = "pd", ncol = 2, where = "bottomright", bty = "n", cex = .5) ## @knitr pdmarkers getMarkers(tan2009r1, fcol = "pd.markers") ## @knitr pdsvm, cache = TRUE, message = FALSE, warning = FALSE, tidy = FALSE w <- table(fData(tan2009r1)[, "pd.markers"]) (w <- 1/w[names(w) != "unknown"]) params <- svmOptimisation(tan2009r1, fcol = "pd.markers", times = 10, xval = 5, class.weights = w, verbose = FALSE) tan2009r1 <- svmClassification(tan2009r1, params, class.weights = w, fcol = "pd.markers") ## @knitr pdres2fig, dev='pdf', fig.width=6, fig.height=6, echo=TRUE ptsze <- exp(fData(tan2009r1)$svm.scores) - 1 plot2D(tan2009r1, fcol = "svm", cex = ptsze) addLegend(tan2009r1, fcol = "svm", where = "bottomright", ncol = 2, bty = "n", cex = .5) ## @knitr sessioninfo, results='asis', echo=FALSE toLatex(sessionInfo())