## ----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()