R/LahtiMLData.R

In microbiome/microbiomeDataSets: Experiment Hub based microbiome datasets

#' Obtain the Lahti Microbiome and Lipid data
#'
#' Obtain the microbiome and lipid data from Lahti et al. (2013).
#'
#' @details
#' The LahtiML dataset contains high-throughput profiling data from 389 human
#' blood serum lipids and 130 intestinal genus-level bacteria from 44 samples
#' (22 subjects from 2 time points; before and after probiotic/placebo
#' intervention). The data set can be used to investigate associations between
#' intestinal bacteria and host lipid metabolism
#'
#' This data set is based on the Human Intestinal Tract (HIT)Chip
#' phylogenetic 16S microarray (Rajilic-Stojanovic _et al._ 2009.
#' This profiling technology differs from the more widely used 16S rRNA
#' amplicon sequencing.
#'
#' Column metadata includes the time points, sex, subject identifier, sample
#' identifier and treatment group.
#'
#' Row metadata of the microbiome data contains taxonomic information on the
#' Phylum, Family and Genus level.
#'
#' All data are downloaded from ExperimentHub and cached for local re-use.
#'
#' @return for  \code{LahtiMLData} a \linkS4class{MultiAssayExperiment} object
#'   with \linkS4class{TreeSummarizedExperiment} and a
#'   \linkS4class{SummarizedExperiment},
#'   for  \code{LahtiMData} a \linkS4class{TreeSummarizedExperiment}
#'
#' @author Felix G.M. Ernst and Leo Lahti
#'
#' @references
#' Lahti L et al. (2013): Associations between the human intestinal microbiota,
#' Lactobacillus rhamnosus GG and serum lipids indicated by integrated analysis
#' of high-throughput profiling data.
#' \emph{PeerJ} 1:e32 \url{https://doi.org/10.7717/peerj.32}
#'
#' Rajilic-Stojanovic M, Heilig HG, Molenaar D, Kajander K, Surakka A, Smidt H,
#' de Vos WM (2009). Development and application of the human intestinal tract 
#' chip, a phylogenetic microarray: analysis of universally conserved phylotypes
#' in the abundant microbiota of young and elderly adults.
#' \emph{Environ Microbiol.} 11(7):1736-51
#' \url{https://doi.org/10.1111/j.1462-2920.2009.01900.x}
#'
#' @name LahtiMLData
#' @export
#'
#' @examples
#' mae <- LahtiMLData()
#' tse <- LahtiMData()
LahtiMLData <- function() {
    mae <- .create_mae("3.13/lahti-ml",
                    types = list(microbiome = list("TSE" = c("counts")),
                                    lipids = list("SE" = c("counts"))),
                    coldata = TRUE,
                    samplemap = FALSE,
                    has.rowdata = list(microbiome = TRUE,
                                        lipids = FALSE),
                    has.coldata = list(microbiome = FALSE,
                                        lipids = FALSE))
    mae
}
#' @rdname LahtiMLData
#' @importFrom SummarizedExperiment colData<-
#' @export
LahtiMData <- function() {
    dataset <- "3.13/lahti-ml"
    hub <- ExperimentHub()
    tse <- .create_tse(dataset,
                    hub = hub,
                    assays = c("counts"),
                    has.rowdata = TRUE,
                    has.coldata = FALSE,
                    prefix = "microbiome")
    args <- .get_col_row_map_data(dataset,
                                hub = hub,
                                has.rowdata = FALSE,
                                has.coldata = TRUE)
    colData(tse) <- args$colData
    tse
}

#' @rdname LahtiMLData
#' @export
peerj32 <- LahtiMLData