R/yeast_long.R

In nclRcourses: Functions for R courses at Newcastle University

#' Yeast data set
#' 
#' The data set we are going to use in the next few chapters comes from Gillespie et al., 2010.
#' This paper is a tutorial for analysing time-course data in Bioconductor. The paper and associated 
#' R code can be downloaded from \url{https://github.com/csgillespie/bmc-microarray}
#' The data were collected according to the experimental protocol described in 
#' Greenal et al.. Briefly, three biological replicates 
#' were studied on each of a wild-type (WT) yeast strain and a strain carrying the 
#' cdc13-1 temperature sensitive mutation (in which telomere uncapping is
#' induced by growth at temperatures above around 27 degrees C). These replicates 
#' were sampled initially at 23 degrees C (at which cdc13-1 has 
#' essentially WT telomeres) and then at 1, 2, 3 and 4 hours after a shift to
#' 30 degrees C to induce telomere uncapping. The thirty resulting RNA samples
#' were hybridised to Affymetrix yeast2 arrays. The microarray data are available 
#' in the ArrayExpress database under accession numberE-MEXP-1551.
#' 

#' 
#' The two data sets contain the same data, but in wide and long data frames.
#' @aliases yeast_long yeast_wide
#' @name yeast_long
#' @docType data
#' @usage data(yeast_long)
#' @return A data frame 
#' @note The values in the data frame have been normalised using the rma procedure. 
#' To reduce the memory footprint, only the first 500 probes are included in this data set. The columns in data_wide are
#' \describe{
#' \item{ID.}{Probe ID}
#' \item{value.}{Normalised log expression level}
#' \item{type.}{Mutant or wild-type}
#' \item{rep.}{Replication number: 1, 2, 3}
#' \item{tps.}{Time point: 0, 60, 120, 180, 240 minutes}
#' }
#' @references Gillespie, C. S., et al, 2010. Analysing yeast time 
#'course microarray data using BioConductor: a case study using yeast2 Affymetrix arrays. BMC Research Notes, 3:81.
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