Nothing
#' Internal csSAM functions
#'
#' These functions are not to be called by the user.
#'
#' @author Shai Shen-Orr, Rob Tibshirani, Narasimhan Balasubramanian, David Wang
#' @keywords internal
#' @name csSAM-internal
NULL
#' Cell-specific Differential Expression (csSAM)
#'
#' SAM for Cell-specific Differential Expression SAM.
#'
#' \tabular{ll}{ Package: \tab csSAM\cr Type: \tab Package\cr Version: \tab
#' 1.2\cr Date: \tab 2011-10-08\cr License: \tab LGPL\cr LazyLoad: \tab yes\cr
#'
#' Tissues are often made up of multiple cell-types. Each with its own
#' functional attributes and molecular signature. Yet, the proportions of any
#' given cell-type in a sample can vary markedly. This results in a significant
#' loss of sensitivity in gene expression studies and great difficulty in
#' identifying the cellular source of any perturbations. Here we present a
#' statistical methodology (cell-type specific Significance Analysis of
#' Microarrays or csSAM) which, given microarray data from two groups of
#' biological samples and the relative cell-type frequencies of each sample,
#' estimates in a virtual manner the gene expression data for each cell-type at
#' a group level, and uses these to identify differentially expressed genes at a
#' cell-type specific level between groups.
#'
#' The lower limit for the number of samples needed for deconvolving the
#' cell-specific expression of N cell-types is N+1. For a singe color array -
#' the result could be interperted as the avg. expression level of a given gene
#' in a cell-type of that group. Multiplied by the frequecy of a given cell-type
#' in an individual in the group, it is the amount contributed by that cell type
#' to the overall measured expression on the array. \cr Key functions for this
#' package:\cr csSamWrapper - Single wrapper function performs all
#' functionality. csfit: For deconvolving the average cell-type specific
#' expression for each cell-type in a given group.\cr csSAM: For calculating the
#' constrast between every pair of cells being compared between the two
#' groups.\cr fdrCsSAM: Estimate the false discovery rate for each cell-type
#' specific comparison.\cr findSigGenes:Identifies the list of differentially
#' expressed genes in a given cell-type at a given FDR cutoff.\cr
#' plotCsSAM:Plots a fdr plot of ther results.\cr } Additional functions exists
#' (runSAM and fdrSAM to contrast csSAM with the tissue heterogeneity ignorant
#' SAM).
#'
#' @name csSAM-package
#' @docType package
#' @author Shai Shen-Orr, Rob Tibshirani, Narasimhan Balasubramanian, David Wang
#'
#' Maintainer: Shai Shen-Orr <shenorr@@stanford.edu>
#' @bibliography ~/Documents/articles/library.bib
#' @cite Shen-Orr2010
#' @exportPattern ^[^\\.]
#' @examples
#'
#' library("csSAM")
#' ##
#' ## Generate random dataset
#' ##
#' set.seed(143)
#' k <- 5 # number of cell types
#' ng <- 500 # number of genes
#' p <- 20 # number of samples
#' ndiff <- 100 # number of genes differentially expressed
#'
#' # true cell-specific signatures
#' H1 <- matrix(rnorm(5*ng), ncol=ng)
#' H2 <- H1
#' # create differential expression for 3rd cell type
#' H2[3,1:ndiff] <- H2[3,1:ndiff] + 5
#'
#' # cell frequency matrix per sample
#' cc <- matrix(runif(p*k), ncol=k)
#' cc <- t(scale(t(cc), center=FALSE, scale=rowSums(cc)))
#' colnames(cc) <- paste('cellType', 1:ncol(cc), sep="")
#'
#' # global expression matrix
#' G <- rbind(cc[1:10, ] %*% H1, cc[11:p, ] %*%H2 ) + matrix(rnorm(p*ng), ncol=ng)
#' # sample classes (2 groups)
#' y <- gl(2, p/2)
#'
#' fileName = "Example File.pdf";
#' \dontshow{ on.exit(unlink(filename)) }
#'
#' # Now run, either using the wrapper
#' # NB: more permutations would be needed for real data
#' deconvResults = csSamWrapper(G, cc, y, nperms = 50, alternative = "two.sided", standardize = TRUE, medianCenter = TRUE,fileName = fileName)
#'
#' # Or by calling each function independently
#' # (e.g. useful if you want to perform only cell-specific expression without differential expression).
#' \dontrun{
#' numset = nlevels(y)
#' n <- summary(y, maxsum=Inf) # number of samples in each class
#' numgene = ncol(G)
#' numcell = ncol(cc)
#' geneID = colnames(G)
#' cellID = colnames(cc)
#'
#' deconv <- list()
#' # run analysis
#' for (curset in levels(y))
#' deconv[[curset]]= csfit(cc[y==curset,], G[y==curset,])
#'
#' rhat <- array(dim = c(numcell,numgene))
#' rhat[, ] <- csSAM(deconv[[1]]$ghat, deconv[[1]]$se,
#' n[1], deconv[[2]]$ghat, deconv[[2]]$se, n[2],
#' standardize=TRUE, medianCenter=TRUE, nonNeg=TRUE)
#'
#' tt.sam <- runSAM(G, y)
#' falseDiscovR <- fdrCsSAM(G,cc,y,n,numcell,numgene, rhat,
#' nperms = 200,standardize=TRUE,alternative='two.sided',
#' medianCenter=TRUE, nonNeg=TRUE)
#' falseDiscovRSAM <- fdrSAM(G, y, nperms=200, alternative = 'two.sided',tt.sam)
#' sigGene <- findSigGene(G, cc, y, rhat, falseDiscovR)
#'
#' plotCsSAM(falseDiscovR, falseDiscovRSAM,alternative='two.sided',cellID,numcell, fileName)
#' print (falseDiscovR$fdr.g[ , ] )
#' }
#'
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.