R/nci60.R

In robustHD: Robust Methods for High-Dimensional Data

# --------------------------------------
# Author: Andreas Alfons
#         Erasmus Universiteit Rotterdam
# --------------------------------------

#' @name nci60
#' @aliases protein
#' @aliases gene
#' @aliases proteinInfo
#' @aliases geneInfo
#' @aliases cellLineInfo
#'
#' @docType data
#'
#' @title NCI-60 cancer cell panel
#'
#' @description
#' The data set is a pre-processed version of the NCI-60 cancer cell panel as
#' used in Alfons, Croux & Gelper (2013).  One observation was removed since
#' all values in the gene expression data were missing.
#'
#' @usage
#' data("nci60")
#'
#' @format
#' Protein and gene expression data on 59 observations are stored in two
#' separate matrices:
#' \describe{
#'   \item{\code{protein}}{a matrix containing protein expressions based on
#'   antibodies (162 columns), acquired via reverse-phase protein lysate
#'   arrays and log2 transformed.}
#'   \item{\code{gene}}{a matrix containing gene expression data (22283
#'   columns), obtained with an Affymetrix HG-U133A chip and normalized
#'   with the GCRMA method.}
#' }
#' In addition, meta information on the proteins, genes, and cancer cell lines
#' is stored in three separate data frames:
#' \describe{
#'   \item{\code{proteinInfo}}{a data frame with 162 rows and the following 4
#'   columns: \code{Experiment} (the name of the experiment for collecting
#'   the data), \code{Probe} (the name of the individual probe), \code{Symbol}
#'   (the symbol of the protein in Human Genome Organisation (HUGO)
#'   nomenclature), and \code{ID} (identifier of the protein per the National
#'   Center for Biotechnology Information (NCBI) Entrez database).  The rows of
#'   this data frame correspond to the columns of the matrix \code{protein}.}
#'   \item{\code{geneInfo}}{a data frame with 22283 rows and the following 4
#'   columns: \code{Experiment} (the name of the experiment for collecting
#'   the data), \code{Probe} (the name of the individual probe), \code{Symbol}
#'   (the symbol of the gene in Human Genome Organisation (HUGO) nomenclature),
#'   and \code{ID} (identifier of the gene per the National Center for
#'   Biotechnology Information (NCBI) Entrez database).  The rows of this
#'   data frame correspond to the columns of the matrix \code{gene}.}
#'   \item{\code{cellLineInfo}}{a data frame with 59 rows and 15 columns
#'   containing various information on the cancer cell lines, such as tissue of
#'   origin and histology, or age and sex of the patient. The rows of this data
#'   frame correspond to the rows of the matrices \code{protein} and
#'   \code{gene}.}
#' }
#'
#' @source
#' The original data were downloaded from
#' \url{https://discover.nci.nih.gov/cellminer/} on 2012-01-27.
#'
#' The exact version of the data used in Alfons, Croux & Gelper (2013) can be
#' obtained from \url{https://github.com/aalfons/nci60}, together with the
#' script for pre-processing.  The data in package \pkg{robustHD} differ in
#' that the matrix of the gene expressions is called \code{gene} and that they
#' include the three data frames with meta information on proteins, genes, and
#' cancer cell lines.
#'
#' @references
#' Reinhold, W.C., Sunshine, M., Liu, H., Varma, S., Kohn, K.W., Morris, J.,
#' Doroshow, J. and Pommier, Y. (2012) CellMiner: A Web-Based Suite of Genomic
#' and Pharmacologic Tools to Explore Transcript and Drug Patterns in the
#' NCI-60 Cell Line Set. \emph{Cancer Research}, \bold{72}(14), 3499--3511.
#' \doi{10.1158/0008-5472.CAN-12-1370}
#'
#' Alfons, A., Croux, C. and Gelper, S. (2013) Sparse least trimmed squares
#' regression for analyzing high-dimensional large data sets. \emph{The Annals
#' of Applied Statistics}, \bold{7}(1), 226--248. \doi{10.1214/12-AOAS575}
#'
#' @examples
#' \donttest{
#' # load data
#' data("nci60")
#' # define response variable
#' y <- protein[, 92]
#' # screen most correlated predictor variables
#' correlations <- apply(gene, 2, corHuber, y)
#' keep <- partialOrder(abs(correlations), 100, decreasing = TRUE)
#' X <- gene[, keep]
#' }

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