R/tabsex4.R

In metamicrobiomeR: Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI & Random Effects

#' Combined data for meta-analysis.
#'
#' Result outputs of differential abundance analysis using GAMLSS_BEZI
#' from "taxa.compare" function combined from
#' 4 studies for meta-analysis. The comparison was between gender
#' adjusted for age of infants at sample collection.
#'
#' @docType data
#'
#' @usage data(tabsex4)
#'
#' @format A dataframe with 701 rows and 23 variables.
#'
#' @keywords datasets
#'
#' @references Subramanian et al. Nature. 2014 Jun 19; 510(7505): 417–421.
#' (\href{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4189846/}{PubMed})
#'
#' Bender et al. Sci Transl Med. 2016 Jul 27; 8(349): 349ra100.
#' (\href{https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5301310/}{PubMed})
#'
#' Pannaraj et al. JAMA Pediatr. 2017;90095(7):647–54.
#' (\href{https://pubmed.ncbi.nlm.nih.gov/28492938/}{PubMed})
#'
#' Thompson et al. Front Cell Infect Microbiol. 2015;5:3.
#' (\href{https://pubmed.ncbi.nlm.nih.gov/25705611/}{PubMed})
#'
#' @source \href{https://gordonlab.wustl.edu/supplemental-data/supplemental-data-portal/subramanian-et-al-2014/}{Gordon Lab}
#'
#' @examples
#' # load saved GAMLSS-BEZI results of four studies
#' # for the comparison of bacterial taxa relative abundance between
#' # genders adjusted for breastfeeding and infant age at sample collection
#' data(tabsex4)
#' #select only taxonomies of a small phylum for meta-analysis example
#' # (to save running time)
#' tlm<-tabsex4$id[grep("k__bacteria.p__fusobacteria",tabsex4$id)]
#' # meta-analysis
#' metab.sex<-meta.taxa(taxcomdat=tabsex4[tabsex4$id %in% tlm,],
#' summary.measure="RR", pool.var="id", studylab="study",
#' backtransform=FALSE, percent.meta=0.5, p.adjust.method="fdr")
#' #show results by table and plot
#' #phylum table
#' metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4[tabsex4$id %in% tlm,],
#' tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="table")
"tabsex4"