rWSBIM1322: Companion Package for WSBIM1322 Course

#' CPTAC data
#'
#' The data stems from the 6th study of the Clinical Proteomic
#' Technology Assessment for Cancer (CPTAC). The authors spiked the
#' Sigma Universal Protein Standard mixture 1 (UPS1) containing 48
#' different human proteins in a protein background of 60 ng/microL
#' Saccharomyces cerevisiae strain BY4741. Two different spike-in
#' concentrations were used: 6A (0.25 fmol UPS1 proteins/microL) and
#' 6B (0.74 fmol UPS1 proteins/microL). In this subset, we limited
#' ourselves to the data of LTQ-Orbitrap W at site 56. The data were
#' searched with MaxQuant version 1.5.2.8, and detailed search
#' settings were described in Goeminne et al. (2016). Three replicates
#' peptide quantitation data are available for each concentration.
#'
#' The data are available as `SummarizedExperiment` objects.
#'
#' See the proteomics tutorial from the Bioinformatics Summer School
#' 2019 (https://lgatto.github.io/bioc-ms-prot/bss-lab.html) for
#' scripts on how these data were processed.
#'
#' @md
#'
#' @rdname cptac
#'
#' @aliases cptac_se cptac_se_prot cptac_na_se
#'
#' @examples
#' library("SummarizedExperiment")
#' data(cptac_se)
#' cptac_se
#' data(cptac_se_prot)
#' cptac_se_prot
"cptac_se"


#' Example data for t-tests illustration
#'
#' See `scripts/randata.R` for how these data were generated.
#'
#' @md
#'
#' @aliases tdata2 tdata3 tdata4
#'
#' @examples
#' data(tdata1)
#' head(tdata1)
#'
#' data(tdata2)
#' tdata2
#'
#' data(tdata3)
#' tdata3
#'
#' data(tdata4)
#' tdata4
"tdata1"

#' Example data for PCA illustration
#'
#' See `man/xy.R` for how these data were generated. `xy` is scaled,
#' `xy0` is the orginal data.
#'
#' @md
#'
#' @aliases xy0
#'
#' @examples
#' data(xy)
#'
#' xy0
#'
#' xy
#' pca <- prcomp(xy)
#' summary(pca)
#' plot(pca)
#' biplot(pca)
"xy"

#' Metabolomics data (from MSMB)
#'
#' This data comes from the Modern *Statistics for Modern Biology* book and was
#' originally called `mat1` from the `mat1xcms.RData` file. It contains
#' quantitation data for 399 metabolites and 6 knock-out and 6 wild-type
#' samples.
#'
#' @md
#' @examples
#' data(metab1)
#'
#' dim(metab1)
#' metab1[1:10, 1:3]
"metab1"


#' Expression data
#'
#' This data is a copy of the `iris` data, reframed for biomedical
#' course. It illustrates the expression of 4 genes, BRCA1, BRCA2,
#' TP53 and A1CF, in 150 patients, that have been categorised in 3
#' catégories, A, B and C.
#'
#' @md
#'
#' @aliases giris2
#'
#' @examples
#' head(giris)
#' pairs(giris, col = giris$GRADE)
#'
#' pca2 <- prcomp(giris2[, -5], scale = TRUE)
#' factoextra::fviz_pca_ind(pca2, col.ind = giris2$GRADE)
"giris"


#' Example for distances
#'
#' This is a small toy example providing expression values for 5 genes
#' and three samples from Olga Vitek. It is used to compare euclidean
#' and correlation distances and the effect/importance of scaling.
#'
#' @md
#'
#' @examples
#' data(g3)
#' g3
#' matplot(t(g3), type = "b", xlab = "Samples", ylab = "Expression")
"g3"


#' SummarizedExperiment data
#'
#' This is a small RNA-Seq data set, build from the data returned by
#' the `rWSBIM1207::kem2.tsv()` function.
#'
#' @md
#'
#' @examples
#' library("SummarizedExperiment")
#' data(kem2_se)
#' assay(kem2_se)
#' colData(kem2_se)
#' rowData(kem2_se)
"kem2_se"

#' Recap SummarizedExperiment data
#'
#' Several `SummarizedExperiment` datasets for the recap exercices in
#' the conclusion chapter of the course.
#'
#' @aliases recapSE1 recapSE2
#'
#' @md
#'
#' @examples
#' data(recapSE1)
#' recapSE1
#'
#' data(recapSE2)
#' recapSE2
"recapSE1"