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#' iGC: an integrated analysis package of gene expression and copy number
#' alteration
#'
#' The iGC package is used to identify CNA-driven differentially expressed
#' genes. The iGC package provides four categories of important functions:
#' `create_sample_desc`, `create_gene_ex`, `create_gene_cna` and
#' `find_cna_drive_gene`.
#'
#' @section create_sample_desc: The create_sample_desc function is provided for
#' creating a sample description table containing all required inputs.
#'
#'
#' @section create_gene_exp function: The create_gene_exp function is used to
#' rearrange the input gene expression files into a gene expression list of
#' entire samples.
#'
#' @section create_gene_cna function: The create_gene_cna function maps CNA data
#' to human genes and then defines the mapped human genes as CN gain or loss
#' based on the CN threshold, whose default values are set as 2.5 for gain and
#' 1.5 for loss. These mapped genes will be assigned values in +1, -1 or 0,
#' where +1 stands for CNA-gain, -1 stands for CNA-loss and 0 stands for
#' neutral.
#'
#' @section find_cna_driven_gene function: The find_cna_driven_gene function
#' identifies CNA-driven differentially expressed genes. The input mapped
#' genes remain for further analyses if its ratio of the number of CN changed
#' samples, CNA-gain (G) or CNA-loss (L), to the number of total samples is
#' larger than a given threshold. Here the default setting is that only genes
#' showing CNAs in at least 20% of the samples will be analyzed further. Then,
#' statistical tests, T-test and Wilcoxon rank sum test, are performed in the
#' GE level by classifying the samples as G and L plus Nertral (N) groups or L
#' and G plus N groups, depending on the CN of the interested gene increases
#' or decreases.
#'
#' @docType package
#' @name iGC
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