R/update_y_pb.R

In gemini: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens

#' update_y_pb
#' @description Update values of y using data from \code{Input} 
#' and current values of other parameters.
#'
#' @param Model a Model object of class gemini.model
#' @param mean_y numeric indicating prior mean of y
#' @param sd_y numeric indicating prior sd of y
#' @param verbose default FALSE
#'
#' @return An object of class gemini.model
#'
#' @importFrom pbmcapply pbmclapply
#' @importFrom magrittr set_names
#' @importFrom pbmcapply progressBar
#' @importFrom utils getTxtProgressBar
#' @importFrom utils setTxtProgressBar
#'
#' @examples
#' data("Model", package = "gemini")
#' Model %<>% update_y_pb()
#'
#' @export
update_y_pb <- function(Model,
                        mean_y = 0,
                        sd_y = 10,
                        verbose = FALSE) {
    if (verbose) {
        message("Updating y...")
        message("\tUsing 1 core (serial).")
        tstart = Sys.time()
        pb <-
            pbmcapply::progressBar(min = 1, max = length(rownames(Model$y)))
    }
    
    Input <- Model$Input
    LFC <- Input[[Model$LFC.name]]
    guide2gene <- Input[[Model$guide.pair.annot]]
    
    # genes corresponding to x_seq1
    x_seq_genes = Model$hashes_x$gene_hash
    
    # mean of gamma distribution
    tau = Model$alpha / Model$beta
    
    # update y
    for (g in rownames(Model$y)) {
        gi_seq = names(x_seq_genes)[x_seq_genes == g]
        gi_seq_hj = lapply(gi_seq, function(x)
            unlist(Model$hashes_x$hash[x])) %>% magrittr::set_names(gi_seq)
        
        # calculating updates for each x_seq
        numerator = denominator = numeric(ncol(LFC)) %>% 
            magrittr::set_names(colnames(LFC))
        
        for (i in gi_seq) {
            # identify which position the partner gene (h) is in
            h.col <- vapply(gi_seq_hj[[i]], function(s) {
                ss = strsplit(s,
                              split = Model$pattern_split,
                              fixed = TRUE)[[1]]
                return(which(ss != i))
            }, numeric(1))
            
            # identify cases in which the partner gene is nc_gene
            ind_nc = guide2gene[match(gi_seq_hj[[i]], guide2gene[, 1]), 3]
            ind_nc[h.col == 1] = guide2gene[match(gi_seq_hj[[i]], guide2gene[, 1]), 2][h.col ==
                                                                                           1]
            ind_nc = ind_nc %in% Model$nc_gene
            
            # identify partner guides, filtering out nc_gene guides
            hj = Model$hashes_x$paired_guide[gi_seq_hj[[i]], 2]
            hj[h.col == 1] = Model$hashes_x$paired_guide[gi_seq_hj[[i]], 1][h.col ==
                                                                                1]
            hj = hj[!ind_nc]
            
            # gene g paired with genes h
            h = x_seq_genes[hj]
            gh = apply(cbind(rep(g, length(h)), h), 1, function(x)
                paste(sort(x), collapse = Model$pattern_join))
            
            # numerator for each guide i
            numerator_i = LFC[gi_seq_hj[[i]][!ind_nc],] - Model$x[hj] *
                Model$y[h,] - Model$xx[gi_seq_hj[[i]][!ind_nc]] * Model$s[gh,]
            numerator_i = (Model$x[i] * tau[gi_seq_hj[[i]][!ind_nc],]) *
                numerator_i
            if (sum(!ind_nc) > 1 | sum(!ind_nc) == 0) {
                numerator_i = colSums(as.matrix(numerator_i), na.rm = TRUE)
            } else{
                numerator_i = colSums(as.matrix(t(numerator_i)), na.rm = TRUE)
            }
            numerator_nc_i = (Model$x[i] * tau[gi_seq_hj[[i]][ind_nc],]) *
                LFC[gi_seq_hj[[i]][ind_nc],]
            if (sum(ind_nc) > 1 | sum(ind_nc) == 0) {
                numerator_nc_i = colSums(as.matrix(numerator_nc_i), na.rm = TRUE)
            } else{
                numerator_nc_i = colSums(as.matrix(t(numerator_nc_i)), na.rm = TRUE)
            }
            numerator_i = numerator_i + numerator_nc_i
            numerator = numerator_i + numerator
            
            # denominator for each guide i
            if (length(gi_seq_hj[[i]]) > 1) {
                denominator_i = colSums(as.matrix(Model$x2[i] * tau[gi_seq_hj[[i]],]), na.rm = TRUE)
            } else{
                denominator_i = colSums(as.matrix(t(Model$x2[i] * tau[gi_seq_hj[[i]],])), na.rm = TRUE)
            }
            #denominator_i = colSums(as.matrix(Model$x2[i] * tau[gi_seq_hj[[i]], ]), na.rm = TRUE)
            denominator = denominator_i + denominator
        }
        
        # update progress bar
        if (verbose)
            utils::setTxtProgressBar(pb = pb,
                                     value = utils::getTxtProgressBar(pb) + 1)
        
        # adding prior values to the numerator and denominator
        numerator = mean_y / (sd_y ^ 2) + numerator
        denominator = 1 / (sd_y ^ 2) + denominator
        
        # updating y and y2
        Model$y[g,] = numerator / denominator
        Model$y2[g,] = Model$y[g,] ^ 2 + 1 / denominator
    }
    
    
    # output
    if (verbose) {
        close(pb)
        tend = Sys.time()
        tdiff = difftime(tend, tstart)
        message("\tCompleted update of y.")
        message("\tTime to completion: ",
                round(tdiff, digits = 3),
                ' ',
                units(tdiff))
    }
    return(Model)
}