## ----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, eval = FALSE--------------------------------------
# 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, eval = FALSE--------------------------------------------------
# msdbDF <- read.table(system.file("extdata/local_ms_db.tsv", package = "phenomis"),
#     header = TRUE,
#     sep = "\t",
#     stringsAsFactors = FALSE
# )

## ----localms_annotation, message=FALSE, warning=FALSE, eval = 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()