## ----global_options, include=FALSE-------------------------------------------- knitr::opts_chunk$set(fig.width = 5, fig.height = 5) ## ----reading, message = FALSE------------------------------------------------- library(phenomis) sacurine.se <- reading(system.file("extdata/sacurine", package = "phenomis")) ## ----inspecting--------------------------------------------------------------- sacurine.se <- inspecting(sacurine.se, report.c = "none") ## ----correcting, fig.dim = c(6, 5)-------------------------------------------- sacurine.se <- correcting(sacurine.se, reference.vc = "pool", report.c = "none") ## ----inspecting_variable_filtering-------------------------------------------- sacurine.se <- inspecting(sacurine.se, figure.c = "none", report.c = "none") ## ----discarding_pool_CV_sup_0.3----------------------------------------------- sacurine.se <- sacurine.se[rowData(sacurine.se)[, "pool_CV"] <= 0.3, ] ## ----discarding_pools--------------------------------------------------------- sacurine.se <- sacurine.se[, colData(sacurine.se)[, "sampleType"] != "pool"] print(sacurine.se) ## ----normalizing_osmolality--------------------------------------------------- assay(sacurine.se) <- sweep(assay(sacurine.se), 2, colData(sacurine.se)[, "osmolality"], "/") ## ----transforming------------------------------------------------------------- sacurine.se <- transforming(sacurine.se, method.vc = "log10") ## ----sample_filtering--------------------------------------------------------- sacurine.se <- inspecting(sacurine.se, figure.c = "none", report.c = "none") sacurine.se <- sacurine.se[, colData(sacurine.se)[, "hotel_pval"] >= 0.001 & colData(sacurine.se)[, "miss_pval"] >= 0.001 & colData(sacurine.se)[, "deci_pval"] >= 0.001] ## ----final_check-------------------------------------------------------------- inspecting(sacurine.se, report.c = "none") ## ----hypotesting, warning=FALSE----------------------------------------------- sacurine.se <- hypotesting(sacurine.se, test.c = "ttest", factor_names.vc = "gender", adjust.c = "BH", adjust_thresh.n = 0.05, report.c = "none") ## ----PCA---------------------------------------------------------------------- sacurine.se <- ropls::opls(sacurine.se, info.txt = "none") sacurine.pca <- ropls::getOpls(sacurine.se)[["PCA"]] ropls::plot(sacurine.pca, parAsColFcVn = colData(sacurine.se)[, "gender"], typeVc = "x-score") ropls::plot(sacurine.pca, parAsColFcVn = colData(sacurine.se)[, "age"], typeVc = "x-score") ## ----heatmap------------------------------------------------------------------ sacurine.se <- clustering(sacurine.se, clusters.vi = c(5, 3)) ## ----plsda-------------------------------------------------------------------- sacurine.se <- ropls::opls(sacurine.se, "gender") ## ----plsda_scoreplot, eval = FALSE-------------------------------------------- # sacurine_gender.plsda <- ropls::getOpls(sacurine.se)[["gender_PLSDA"]] # ropls::plot(sacurine_gender.plsda, # parAsColFcVn = colData(sacurine.se)[, "gender"], # typeVc = "x-score") ## ----biosigner, fig.width = 5, fig.height = 5--------------------------------- sacurine.biosign <- biosigner::biosign(sacurine.se, "gender", bootI = 5) ## ----annotating_parameters---------------------------------------------------- annotating_parameters() ## ----chebi_annotation, eval = FALSE------------------------------------------- # sacurine.se <- annotating(sacurine.se, # database.c = "chebi", # param.ls = list(query.type = "mz", # query.col = "mass_to_charge", # ms.mode = "neg", # mz.tol = 10, # mz.tol.unit = "ppm", # max.results = 3, # prefix = "chebiMZ."), # report.c = "none") # knitr::kable(head(rowData(sacurine.se)[, grep("chebiMZ", # colnames(rowData(sacurine.se)))])) ## ----chebi_identifier, eval = FALSE------------------------------------------- # sacurine.se <- annotating(sacurine.se, database.c = "chebi", # param.ls = list(query.type = "chebi.id", # query.col = "database_identifier", # prefix = "chebiID.")) # knitr::kable(head(rowData(sacurine.se)[, grep("chebiID", # colnames(rowData(sacurine.se)))])) # ## ----localdbDF---------------------------------------------------------------- msdbDF <- read.table(system.file("extdata/local_ms_db.tsv", package = "phenomis"), header = TRUE, sep = "\t", stringsAsFactors = FALSE) ## ----localms_annotation, message=FALSE, warning=FALSE------------------------- sacurine.se <- annotating(sacurine.se, database.c = "local.ms", param.ls = list(query.type = "mz", query.col = "mass_to_charge", ms.mode = "neg", mz.tol = 5, mz.tol.unit = "ppm", local.ms.db = msdbDF, prefix = "localMS."), report.c = "none") knitr::kable(rowData(sacurine.se)[!is.na(rowData(sacurine.se)[, "localMS.accession"]), grep("localMS.", colnames(rowData(sacurine.se)), fixed = TRUE)]) ## ----writing, eval = FALSE---------------------------------------------------- # writing(sacurine.se, dir.c = getwd(), prefix.c = "sacurine") ## ----pms_read----------------------------------------------------------------- prometis.mae <- reading(system.file("extdata/prometis", package = "phenomis")) ## ----------------------------------------------------------------------------- head(colData(prometis.mae)) ## ----pms_inspect-------------------------------------------------------------- prometis.mae <- inspecting(prometis.mae, report.c = "none") ## ----pms_limma---------------------------------------------------------------- prometis.mae <- hypotesting(prometis.mae, "limma", "gene", report.c = "none") ## ----pms_plsda---------------------------------------------------------------- prometis.mae <- ropls::opls(prometis.mae, "gene") ## ----pms_biosign, fig.width = 5, fig.height = 5------------------------------- prometis.mae <- biosigner::biosign(prometis.mae, "gene", bootI = 5) ## ----pms_writing, eval = FALSE------------------------------------------------ # writing(prometis.mae, dir.c = getwd(), prefix.c = "prometis") ## ----cll_load----------------------------------------------------------------- data(sCLLex, package = "CLL") cll.eset <- sCLLex[seq_len(1000), ] ## ----cll_inspect, eval = FALSE------------------------------------------------ # cll.eset <- inspecting(cll.eset, report.c = "none") ## ----cll_heatmap, eval = FALSE------------------------------------------------ # Biobase::sampleNames(cll.eset) <- paste0(Biobase::sampleNames(cll.eset), # "_", # substr(Biobase::pData(cll.eset)[, "Disease"], 1, 1)) # # cll.eset <- clustering(cll.eset) ## ----cll_limma, eval = FALSE-------------------------------------------------- # Biobase::pData(cll.eset)[, "Disease"] <- gsub(".", "", # Biobase::pData(cll.eset)[, "Disease"], # fixed = TRUE) # cll.eset <- hypotesting(cll.eset, "limma", "Disease") ## ----mae---------------------------------------------------------------------- data(NCI60, package = "ropls") nci.mae <- NCI60[["mae"]] ## ----mae_focus, message=FALSE, warning=FALSE---------------------------------- library(MultiAssayExperiment) table(nci.mae$cancer) nci.mae <- nci.mae[, nci.mae$cancer %in% c("LE", "ME"), c("agilent", "hgu95")] ## ----mae_clustering, message=TRUE, warning=TRUE, eval = FALSE----------------- # nci.mae <- clustering(nci.mae) ## ----mae_hypotesting, eval = FALSE-------------------------------------------- # nci.mae <- hypotesting(nci.mae, "limma", "cancer", report.c = "none") ## ----se_build----------------------------------------------------------------- # Preparing the data (matrix) and sample and variable metadata (data frames): data(sacurine, package = "ropls") data.mn <- sacurine[["dataMatrix"]] # matrix: samples x variables samp.df <- sacurine[["sampleMetadata"]] # data frame: samples x sample metadata feat.df <- sacurine[["variableMetadata"]] # data frame: features x feature metadata # Creating the SummarizedExperiment (package SummarizedExperiment) library(SummarizedExperiment) sac.se <- SummarizedExperiment(assays = list(sacurine = t(data.mn)), colData = samp.df, rowData = feat.df) # note that colData and rowData main format is DataFrame, but data frames are accepted when building the object stopifnot(validObject(sac.se)) # Viewing the SummarizedExperiment # ropls::view(sac.se) ## ----mae_build_load----------------------------------------------------------- data("NCI60_4arrays", package = "omicade4") ## ----mae_build, message = FALSE, warning=FALSE-------------------------------- library(MultiAssayExperiment) # Building the individual SummarizedExperiment instances experiment.ls <- list() sampleMap.ls <- list() for (set.c in names(NCI60_4arrays)) { # Getting the data and metadata assay.mn <- as.matrix(NCI60_4arrays[[set.c]]) coldata.df <- data.frame(row.names = colnames(assay.mn), .id = colnames(assay.mn), stringsAsFactors = FALSE) # the 'cancer' information will be stored in the main colData of the mae, not the individual SummarizedExperiments rowdata.df <- data.frame(row.names = rownames(assay.mn), name = rownames(assay.mn), stringsAsFactors = FALSE) # Building the SummarizedExperiment assay.ls <- list(se = assay.mn) names(assay.ls) <- set.c se <- SummarizedExperiment(assays = assay.ls, colData = coldata.df, rowData = rowdata.df) experiment.ls[[set.c]] <- se sampleMap.ls[[set.c]] <- data.frame(primary = colnames(se), colname = colnames(se)) # both datasets use identical sample names } sampleMap <- listToMap(sampleMap.ls) # The sample metadata are stored in the colData data frame (both datasets have the same samples) stopifnot(identical(colnames(NCI60_4arrays[[1]]), colnames(NCI60_4arrays[[2]]))) sample_names.vc <- colnames(NCI60_4arrays[[1]]) colData.df <- data.frame(row.names = sample_names.vc, cancer = substr(sample_names.vc, 1, 2)) nci.mae <- MultiAssayExperiment(experiments = experiment.ls, colData = colData.df, sampleMap = sampleMap) stopifnot(validObject(nci.mae)) ## ----eset_build, message = FALSE, warning = FALSE----------------------------- # Preparing the data (matrix) and sample and variable metadata (data frames): data(sacurine, package = "ropls") data.mn <- sacurine[["dataMatrix"]] # matrix: samples x variables samp.df <- sacurine[["sampleMetadata"]] # data frame: samples x sample metadata feat.df <- sacurine[["variableMetadata"]] # data frame: features x feature metadata # Creating the ExpressionSet (package Biobase) sac.set <- Biobase::ExpressionSet(assayData = t(data.mn)) Biobase::pData(sac.set) <- samp.df Biobase::fData(sac.set) <- feat.df stopifnot(validObject(sac.set)) # Viewing the ExpressionSet # ropls::view(sac.set) ## ----mset_build_load---------------------------------------------------------- data("NCI60_4arrays", package = "omicade4") ## ----mset_build, message = FALSE, warning=FALSE------------------------------- library(MultiDataSet) # Creating the MultiDataSet instance nci.mds <- MultiDataSet::createMultiDataSet() # Adding the two datasets as ExpressionSet instances for (set.c in names(NCI60_4arrays)) { # Getting the data expr.mn <- as.matrix(NCI60_4arrays[[set.c]]) pdata.df <- data.frame(row.names = colnames(expr.mn), cancer = substr(colnames(expr.mn), 1, 2), stringsAsFactors = FALSE) fdata.df <- data.frame(row.names = rownames(expr.mn), name = rownames(expr.mn), stringsAsFactors = FALSE) # Building the ExpressionSet eset <- Biobase::ExpressionSet(assayData = expr.mn, phenoData = new("AnnotatedDataFrame", data = pdata.df), featureData = new("AnnotatedDataFrame", data = fdata.df), experimentData = new("MIAME", title = set.c)) # Adding to the MultiDataSet nci.mds <- MultiDataSet::add_eset(nci.mds, eset, dataset.type = set.c, GRanges = NA, warnings = FALSE) } stopifnot(validObject(nci.mds)) ## ----sessionInfo, echo=FALSE-------------------------------------------------- sessionInfo()