R/permutationSimpleLmMatrix.R

In GIGSEA: Genotype Imputed Gene Set Enrichment Analysis

#' permutationSimpleLmMatrix
#'
#' permutationSimpleLmMatrix is a permutation test to calculate the empirical p 
#' values for the weighted simple linear regression model based on the weighted 
#' Pearson correlation.
#'
#' @param fc a vector of numeric values representing the gene expression fold 
#' change
#' @param net a matrix of numeric values in the size of gene number x gene set 
#' number, representing the connectivity betwen genes and gene sets
#' @param weights a vector of numeric values representing the weights of 
#' permuted genes
#' @param num an integer value representing the number of permutations
#' @param step an integer value representing the number of permutations in 
#' each step 
#' @param verbose an boolean value indicating whether or not to print output to 
#' the screen 
#'
#' @return a data frame comprising following columns:
#' \itemize{
#' \item {term} a vector of character values incidating the name of gene set.
#' \item {usedGenes} a vector of numeric values indicating the number of gene 
#' used in the model.
#' \item {observedCorr} a vector of numeric values indicating the observed 
#' weighted Pearson correlation coefficients.
#' \item {empiricalPval} a vector of numeric values [0,1] indicating the 
#' permutation-based empirical p values. 
#' \item {BayesFactor} a vector of numeric values indicating the Bayes Factor 
#' for the multiple test correction.
#' }
#'
#' @export
#'
#' @importFrom stats lm pt qnorm
#' @importFrom utils read.table write.table
#' @importFrom Matrix sparseMatrix
#' @importFrom locfdr locfdr
#' @import MASS
#' 
#' @examples
#'
#' # load data
#' data(heart.metaXcan)
#' gene <- heart.metaXcan$gene_name
#'
#' # extract the imputed Z-score of gene differential expression, which follows 
#' # the normal distribution
#' fc <- heart.metaXcan$zscore
#'
#' # use as weights the prediction R^2 and the fraction of imputation-used SNPs 
#' usedFrac <- heart.metaXcan$n_snps_used / heart.metaXcan$n_snps_in_cov
#' r2 <- heart.metaXcan$pred_perf_r2
#' weights <- usedFrac*r2
#'
#' # build a new data frame for the following weighted linear regression-based 
#' # enrichment analysis
#' data <- data.frame(gene,fc,weights)
#' head(data)
#'
#' net <- MSigDB.KEGG.Pathway$net
#'
#' # intersect the permuted genes with the gene sets of interest
#' data2 <- orderedIntersect( x = data , by.x = data$gene , 
#' by.y = rownames(net)  )
#' net2 <- orderedIntersect( x = net , by.x = rownames(net) , 
#' by.y = data$gene  )
#' all( rownames(net2) == as.character(data2$gene) )
#'
#' # the SGSEA.res1 uses the weighted simple linear regression model, 
#' # while SGSEA.res2 used the weighted Pearson correlation. The latter one 
#' # takes substantially less time.
#' # system.time(SGSEA.res1<-permutationSimpleLm(fc=data2$fc, net=net2, 
#' # weights=data2$weights, num=1000))
#' system.time(SGSEA.res2<-permutationSimpleLmMatrix(fc=data2$fc, net=net2, 
#' weights=data2$weights, num=1000))
#' head(SGSEA.res2)
#'
#' @author Shijia Zhu, \email{shijia.zhu@@mssm.edu}
#'
#' @seealso \code{\link{orderedIntersect}}; \code{\link{permutationSimpleLm}};
#'
permutationSimpleLmMatrix <- function( fc, net, weights=rep(1,nrow(net)), 
                                      num=100, step=1000, verbose=TRUE )
{
    fc[is.na(fc)] <- 0
    weights[is.na(weights)] <- 0
    net <- as.matrix(net)
    net <- net[,colSums(net)>0]
    observedCorr <- weightedPearsonCorr( x=net, y=fc, w=weights )[,1]
    observedPval <- matrixPval( observedCorr, df=sum(weights>0, na.rm=TRUE)-2 )
    #observedPval2 <- separateLm( fc , net , weights )
    #max(abs(observedPval-observedPval2))
    
    empiricalSum <- rep(0,ncol(net))
    if( num%%step==0 )
    {
        steps <- rep(step,num%/%step)
    } else {
        steps <- c( rep(step,num%/%step) , num%%step )
    }
    cs_steps <- c( 1, cumsum(steps) )
    
    for( i in seq_along(steps) )
    {
        stepi <- steps[i]
        shuffledFC <- vapply( seq_len(stepi) , 
                             function(s) sample(fc) , numeric(length(fc)) )
        shuffledCorr <-  weightedPearsonCorr( x=net, y=shuffledFC, w=weights )
        
        for( j in seq_len(nrow(shuffledCorr)) )
        {
            if(observedCorr[j]>0) 
            {
                empiricalSum[j] <- empiricalSum[j] + 
                  sum(shuffledCorr > observedCorr[j])
            } else {
                empiricalSum[j] <- empiricalSum[j] + 
                  sum(shuffledCorr < observedCorr[j])
        }
        }
        
        for(j in cs_steps[i]:cs_steps[i+1]  ) 
        {
            if(verbose) reportProgress(j,num,10)
        }
    }
    
    term <- colnames(net)
    usedGenes <- apply(as.matrix(net), 2, function(x) sum(x!=0,na.rm=TRUE) )
    empiricalPval <- (empiricalSum+0.1)/(num*ncol(net))
    #lc <- locfdr( -1 * sign(observedCorr)*qnorm(empiricalPval) , plot=0 )
    #lc <- locfdr( observedCorr , plot=0 )
    zscore <- qnorm(empiricalPval)
    lc <- locfdr( c(zscore,-zscore) , plot=0 )
    localFdr <- lc$fdr[seq_along(zscore)]
    
    p0 <- lc$fp0[3,3]
    # p0 <- lc$fp0[3,3] - 1.96*lc$fp0[4,3]
    
    BayesFactor <- (1-localFdr)/localFdr *  p0/abs(1-p0)
    
    pval <- data.frame(term, usedGenes, observedCorr, empiricalPval, 
                       BayesFactor)
    rownames(pval) <- NULL
    #pval <- pval[order(pval$BayesFactor,decreasing=TRUE),]
    pval <- pval[order(pval$empiricalPval),]
    pval
    
}