R/mr_clust_em_jnk_optional.R

In cnfoley/mrclust: Identifying Clustered Heterogeneity in Mendelian Randomization Analyses

#' MR-Clust mixture model fitting
#'
#' Assessment of clustered heterogeneity in Mendelian randomization analyses
#' using expectation-maximisation (EM) based model fitting of the MR-Clust
#' mixture model. Function output includes both data-tables and a visualisation
#' of the assingment of variants to clusters.
#'
#' @param theta numeric vector of length the number of variants,
#' the i-th element is a ratio-estimate for the i-th genetic variant.
#' @param theta_se numeric vector of length the number of variants,
#' the i-th element is the standard error of the ratio-estimate
#' for the i-th genetic variant.
#' @param bx numeric vector of length the number of variants,
#' the i-th element is the estimated regression coefficient -
#' i.e. beta-x value - relating the i-th genetic variant to the risk-factor.
#' @param by numeric vector of length the number of variants, the i-th element
#' is the estimated regression coefficient - i.e. beta-y value - relating the
#' i-th genetic variant to the outcome.
#' @param bxse numeric vector of length the number of variants, the i-th element
#'  is the standard error of the estimated regression coefficient relating the
#'  i-th genetic variant to the risk-factor.
#' @param byse numeric vector of length the number of variants, the i-th element
#'  is the standard error of the estimated regression coefficient relating the
#'  i-th genetic variant to the outcome.
#' @param obs_names character vector of length the number of variants,
#' the i-th element is the name of the i-th genetic variants - e.g. the rsID.
#' @param max_iter numeric integer denoting the maximum number of iterations to
#' take before stopping the EM-algorithm's search for a maxima in the
#' log-likelihood.
#' @param tol numeric scalar denoting the maximum absolute difference between
#' two computations of the log-likelihood with which we accept that a maxima in
#' the log-likelihood has been computed.
#' @param junk_mixture switch junk mixture on (TRUE) or off (FALSE).
#' @param junk_sd numeric scalar denoting the scale parameter in the generalised
#'  t-distribution
#' @param junk_mean numeric scalar denoting the mean of the generalised
#' t-distribution. By default mean is set to zero.
#' @param null_mixture switch null mixture on (TRUE) or off (FALSE).
#' @param min_clust_search numeric integer which denotes the minimum number of
#' clusters searched for in the data - default computes evidence supporting up
#' to K=10 clusters which might explain any clustered heterogeneity in the data.
#' @param stop_bic_iter numeric integer I, for computational efficiency -
#' particularly when analysing large numbers of variants - we can stop the
#' EM-algorithm if the BIC is monotonic increasing over the previous I increases
#'  in the number of clusters K. By default evidence supporting at least 10
#'  clusters in the data is computed and so, for example, if the BIC from models
#'   which assume 6 clusters; 7 clusters; ... or; 10 clusters is monotonic
#'   increasing - in the number of clusters K -then the EM-algorithm is stopped
#'   and the model whose K minimises the BIC is returned.
#' @param results_list character list allowing users to choose whether to return
#'  a table with the variants assigned to: "all" of the clusters; a single
#'  "best" cluster or; both. By default we return both, i.e. results_list =
#'  list("all", "best").
#' @param cluster_membership numeric list which allows users to output a list
#' which, for each cluster, returns the variants assigned to the cluster by
#' stratified by the probability of belonging to the cluster. By default,
#' cluster_membership = list(by_prob = 0.1, bound = 0); so that MRClust
#' returns a list, which for each cluster, outputs the variants assigned
#' to the cluster with probability between (0.9,1); (0.8,0.9);... and finally;
#' (0.1,0), i.e. by probability increments 0.1 from 1 to a lower bound of 0.
#' @param plot_results numeric list which allows users to plot the output of
#' MRClust. By default, plot_results = list("best", min_pr = 0.5); so that the
#' best clustering is plotted with variants assigned to a cluster with
#' probability above 0.5.
#' @param trait_search logical, for each of the non-null and non-junk clusters
#' search phenoscanner for traits associated with the variants.
#' @param trait_pvalue numeric scalar for use with trait_search, representing
#' the maximum p-value with with at least one variant in the cluster must be
#' associated with a trait for it to be returned in the phenoscanner search.
#' Default value is GWA significance, i.e. 5*10^-8.
#' @param proxy_r2 numeric scalar for use with trait search, allowing variants
#' whose r2>=proxy_r2 to be included in the trait search. Default r2=0.8.
#' @param catalogue character, for use with trait search. From Phenoscanner
#' (http://www.phenoscanner.medschl.cam.ac.uk/information/) "the catalogue to be
#'  searched (options: None, GWAS, eQTL, pQTl, mQTL, methQTL)". Default setting
#'  is catalogue = "GWAS".
#' @param proxies character, for use with trait search. From Phenoscanner
#' (http://www.phenoscanner.medschl.cam.ac.uk/information/) "the proxies
#' database to be searched (options: None, AFR, AMR, EAS, EUR, SAS)".
#' Default setting is proxies = "None"
#' @param build integer, for use with trait search. From Phenoscanner
#' (http://www.phenoscanner.medschl.cam.ac.uk/information/) "Human genome
#' build numbers (options: 37, 38; default: 37)". Default setting is build = 37.
#' @return Returned are: estimates of the putative number of clusters in the
#' sample, complete with allocation probabilities and summaries of the
#' association estimates for each variant; plots which visualise the allocation
#' of variants to clusters and; several summaries of the fitting process, i.e.
#' the BIC and likelihood estimates.
#' @export
mr_clust_em_jnk_optional <- function(theta, theta_se, bx, by, bxse, byse,
                        obs_names = NULL, max_iter = 5e3, tol = 1e-5,
                        junk_mixture = TRUE,
                        junk_sd = NULL, junk_mean = 0,
                        null_mixture = TRUE,
                        stop_bic_iter = 5, min_clust_search = 10,
                        results_list = list("all", "best"),
                        cluster_membership = list(by_prob = 0.1, bound = 0),
                        plot_results = list("best", min_pr = 0.5),
                        trait_search = FALSE, trait_pvalue = 1e-5,
                        proxy_r2 = 0.8, catalogue = "GWAS", proxies = "None",
                        build = 37) {
  
  # previous user defined options now hard-coded
  cluster_sizes <- NULL
  init_clust_means <- NULL
  init_clust_probs <- NULL
  #junk_mixture <- TRUE
  df <- 4
  junk_prob <- NULL
  junk_null_aware_bic <- TRUE
  fix_junk_prob <- FALSE
  #null_mixture <- TRUE
  null_sd <- NULL
  null_prob <- NULL
  fix_null_prob <- FALSE
  scale_grid_search <- FALSE
  grid_increment <- 0.1
  grid_max <- 2
  clust_size_prior <- FALSE
  bic_prior <- NULL
  rand_num <- 5
  rand_sample <- seq(0.05, 0.4, by = 0.05)
  
  # initial parameter values
  
  m <- length(theta)
  # AM: if removed as always true due to hard-coded options
  cluster_sizes <- 0:m
  if (is.null(obs_names)) {
    obs_names <- paste0("snp_", 1:m)
  }
  
  num_clust <- length(cluster_sizes)
  # AM: if removed as always true due to hard-coded options
  num_clust <- num_clust * rand_num
  
  bic_clust <- bic_clust_mx <- vector()
  clust_mn <- vector("list", num_clust)
  clust_pi <- vector("list", num_clust)
  log_like <- vector("list", num_clust)
  
  
  count0 <- count_k <- 1
  
  for (i in cluster_sizes) {
    for (itr in 1:(rand_num + 1)) {
      if (is.null(init_clust_means) | is.null(init_clust_probs)) {
        if (i > 0 & i != m) {
          init_conds <- stats::kmeans(x = theta, centers = i, iter.max = 5e3)
          clust_means <- as.numeric(init_conds$centers)
          clust_probs <- table(init_conds$cluster) / m
          init_conds
          clust_means
          clust_probs
        } else if (i == m) {
          clust_means <- theta
          clust_probs <- rep(1, m) / m
        } else if (i == 0) {
          clust_means <- clust_probs <- NULL
        }
      }
      
      # junk distribution parameters
      
      if (junk_mixture & is.null(junk_sd)) {
        rng_thet <- range(theta)
        max_disp <- which.max(abs(theta) + 2 * theta_se)
        sig <- (rng_thet[2] - rng_thet[1] + theta_se[max_disp])
      } else if (junk_mixture & !is.null(junk_sd)) {
        sig <- junk_sd
      } else {
        sig <- NULL
      }
      if (junk_mixture & is.null(junk_mean)) {
        mu <- 0
      } else if (junk_mixture & !is.null(junk_mean)) {
        mu <- junk_mean
      } else {
        mu <- NULL
      }
      # null distribution parameters
      null_obs <- abs(theta / theta_se) < 1.96
      if (null_mixture & is.null(null_sd)) {
        sig_null <- theta_se
        mu_null <- 0
      } else if (null_mixture & !is.null(null_sd)) {
        sig_null <- null_sd
        mu_null <- 0
      } else {
        sig_null <- mu_null <- NULL
      }
      
      ####
      if (itr == 1) {
        if (junk_mixture | null_mixture) {
          ### junk_mixture parameters
          sum_pr <- 0
          junk_obs <- sum(2 * (1 - stats::pnorm(theta - stats::median(theta),
                                                0, theta_se)) < 0.05)
          if (junk_mixture & !fix_junk_prob) {
            if (sum(junk_obs) == 0) {
              jk_pr <- 1 / m
              sum_pr <- jk_pr
            } else {
              jk_pr <- sum(junk_obs) / m
              sum_pr <- jk_pr
            }
          } else if (junk_mixture & fix_junk_prob) {
            jk_pr <- junk_prob
            sum_pr <- jk_pr
          } else {
            jk_pr <- NULL
          }
          
          #### null_mixture parameters
          if (null_mixture & !fix_null_prob) {
            if (is.null(jk_pr)) {
              tmp_jk_pr <- 0
            } else {
              tmp_jk_pr <- jk_pr
            }
            if (sum(null_obs) == 0) {
              null_pr <- 1 / m
              sum_pr <- sum_pr + null_pr
            } else {
              null_pr <- sum(null_obs) / m
              if (null_pr + tmp_jk_pr > (1 - 1 / m)) {
                tmp_sum <- null_pr + tmp_jk_pr
                if (is.null(jk_pr)) {
                  null_pr <- null_pr / tmp_sum - 1 / m
                } else {
                  null_pr <- null_pr / tmp_sum - 1 / (2 * m)
                  tmp_jk_pr <- tmp_jk_pr / tmp_sum - 1 / (2 * m)
                  jk_pr <- tmp_jk_pr
                  sum_pr <- tmp_jk_pr
                }
              }
              sum_pr <- sum_pr + null_pr
            }
          } else if (null_mixture & fix_null_prob) {
            null_pr <- null_prob
            sum_pr <- sum_pr + null_pr
          } else {
            null_pr <- NULL
          }
          
          clust_means <- c(clust_means, mu_null, mu)
          clust_probs <- c(clust_probs * (1 - sum_pr), null_pr, jk_pr)
          k_clust <- cluster_sizes[count_k] + sum(junk_mixture) +
            sum(null_mixture)
        } else {
          k_clust <- cluster_sizes[count_k]
          sig <- mu <- NULL
          sig_null <- mu_null <- NULL
          junk_null_aware_bic <- FALSE
        }
      } else {
        if (null_mixture) {
          null_pr <- sample(rand_sample, 1)
          sum_pr <- null_pr
        } else {
          null_pr <- NULL
          sum_pr <- 0
        }
        if (junk_mixture) {
          jk_pr <- sample(rand_sample, 1)
          sum_pr <- sum_pr + jk_pr
        } else {
          jk_pr <- NULL
        }
        
        clust_means <- c(clust_means, mu_null, mu)
        clust_probs <- c(clust_probs * (1 - sum_pr), null_pr, jk_pr)
        k_clust <- cluster_sizes[count_k] + sum(junk_mixture) +
          sum(null_mixture)
      }
      ####
      
      if (scale_grid_search & junk_mixture) {
        sigs <- sig * seq(1, grid_max, by = grid_increment)
        pi_tmp <- vector("list", length(sigs))
        theta_tmp <- vector("list", length(sigs))
        loglik_tmp <- vector("numeric", length(sigs))
        count2 <- 1
        
        for (inc in sigs) {
          sig_tmp <- inc
          pi_clust <- clust_probs # initialise cluster class probabilities
          theta_clust <- clust_means # initialise cluster centroids; these can
          #be the same as the std error is assumed to vary between the
          #observations
          count <- 1 # start iteration count
          loglik_diff <- 1
          loglik_iter <- NULL
          while (loglik_diff > tol & count < max_iter) {
            tmp_loglik0 <- loglik(
              m, pi_clust, theta, theta_se, theta_clust, junk_mixture, df, mu,
              sig_tmp, null_mixture, mu_null, sig_null
            )
            tmp_clust <- theta_updt(
              1:k_clust, m, theta, theta_se, theta_clust, pi_clust,
              junk_mixture, df, mu, sig_tmp, null_mixture, mu_null, sig_null
            )
            tmp_pi <- pi_updt(
              1:k_clust, m, pi_clust, theta, theta_se, theta_clust,
              junk_mixture, df, mu, sig_tmp, junk_prob, fix_junk_prob,
              null_mixture, mu_null, sig_null, null_prob, fix_null_prob
            )
            theta_clust <- tmp_clust
            pi_clust <- tmp_pi
            tmp_loglik <- loglik(
              m, pi_clust, theta, theta_se, theta_clust, junk_mixture, df, mu,
              sig_tmp, null_mixture, mu_null, sig_null
            )
            loglik_diff <- abs(tmp_loglik - tmp_loglik0)
            loglik_iter <- c(loglik_iter, tmp_loglik0)
            count <- count + 1
          }
          pi_tmp[[count2]] <- pi_clust
          theta_tmp[[count2]] <- theta_clust
          loglik_tmp[count2] <- tmp_loglik
          count2 <- count2 + 1
        }
        mx <- which.max(loglik_tmp)
        theta_clust <- theta_tmp[[mx]]
        pi_clust <- pi_tmp[[mx]]
        tmp_loglik <- loglik_tmp[mx]
        sig <- sigs[mx]
      } else {
        pi_clust <- clust_probs # initialise cluster class probabilities
        theta_clust <- clust_means # initialise cluster centroids; these can be
        #the same as the std error is assumed to vary between the observations
        count <- 1 # start iteration count
        loglik_diff <- 1
        loglik_iter <- NULL
        
        while (loglik_diff > tol & count < max_iter) {
          tmp_loglik0 <- loglik(m, pi_clust, theta, theta_se, theta_clust,
                                junk_mixture, df, mu, sig, null_mixture,
                                mu_null, sig_null)
          tmp_clust <- theta_updt(
            1:k_clust, m, theta, theta_se, theta_clust, pi_clust, junk_mixture,
            df, mu, sig, null_mixture, mu_null, sig_null
          )
          tmp_pi <- pi_updt(
            1:k_clust, m, pi_clust, theta, theta_se, theta_clust,
            junk_mixture, df, mu, sig, junk_prob, fix_junk_prob,
            null_mixture, mu_null, sig_null, null_prob, fix_null_prob
          )
          theta_clust <- tmp_clust
          pi_clust <- tmp_pi
          tmp_loglik <- loglik(m, pi_clust, theta, theta_se, theta_clust,
                               junk_mixture, df, mu, sig, null_mixture, mu_null,
                               sig_null)
          loglik_diff <- abs(tmp_loglik - tmp_loglik0)
          loglik_iter <- c(loglik_iter, tmp_loglik0)
          count <- count + 1
        }
      }
      
      clust_mn[[count0]] <- theta_clust
      clust_pi[[count0]] <- pi_clust
      log_like[[count0]] <- loglik_iter
      bic_clust[count0] <- if (!junk_null_aware_bic) {
        -2 * tmp_loglik + (2 * k_clust - 1) * log(m)
      } else {
        -2 * tmp_loglik + (2 * k_clust - 1 - sum(junk_mixture)
                           - sum(null_mixture)) * log(m)
      }
      count0 <- count0 + 1
    }
    bic_clust_mx[count_k] <- max(bic_clust[(count0 -
                                              (rand_num + 2)):(count0 - 1)])
    
    if ((count_k > stop_bic_iter) & (i >= min_clust_search)) {
      tmp_cond <- TRUE
      for (j in 1:stop_bic_iter) {
        tmp_cond <- (tmp_cond & (bic_clust_mx[count_k - j + 1] >
                                   bic_clust_mx[count_k - j]))
      }
      if (tmp_cond) {
        break
      }
    }
    count_k <- count_k + 1
  }
  
  results <- clust_prob(bic_clust, clust_mn, clust_pi,
                        theta = theta, theta_sd = theta_se, junk_mixture = junk_mixture, df = df,
                        junk_mean = mu, junk_sd = sig, null_mixture = null_mixture,
                        null_mean = mu_null, null_sd = sig_null, obs_names = obs_names,
                        clust_size_prior = clust_size_prior, prior = bic_prior
  )
  results_all <- results[order(results$cluster), ]
  results_best <- best_clust(results)
  
  if (!is.null(cluster_membership)) {
    variant_clusters <- clust_inc_list(results,
                                       by_prob = cluster_membership$by_prob,
                                       bound = cluster_membership$bound)
  }
  if (!is.null(plot_results)) {
    if (plot_results[[1]] == "best") {
      plot_1 <- junk_clust_plot(results_best, sig = sig, mu = mu,
                                mu_null = mu_null)
      plot_2 <- two_stage_plot(results_best, bx, by, bxse, byse, obs_names)
    } else {
      tmp <- plot_results[[2]]
      tmp_res <- pr_clust(results, prob = tmp)
      plot_1 <- junk_clust_plot(tmp_res, sig = sig, mu = mu, mu_null = mu_null,
                                junk_mixture = junk_mixture,
                                null_mixture = null_mixture)
      plot_2 <- two_stage_plot(tmp_res, bx, by, bxse, byse, obs_names)
    }
    res <- list(
      results = list(all = results_all, best = results_best),
      cluster_membership = variant_clusters, plots = list(two_stage = plot_2,
                                                          split_plot = plot_1),
      log_likelihood = log_like, bic = bic_clust
    )
  } else {
    res <- list(
      results = list(all = results_all, best = results_best),
      cluster_membership = variant_clusters,
      log_likelihood = log_like, bic = bic_clust
    )
  }
  if (trait_search) {
    trt_search <- pheno_search(variant_clusters = variant_clusters,
                               p_value = trait_pvalue, r2 = proxy_r2,
                               catalogue = catalogue, proxies = proxies,
                               build = build)
    res <- list(
      results = list(all = results_all, best = results_best),
      cluster_membership = variant_clusters, trait_search = trt_search,
      log_likelihood = log_like, bic = bic_clust
    )
  }
  return(res)
}