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Added multithreaded sensitivity analysis #322

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68 changes: 48 additions & 20 deletions R/ancombc2.R
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Original file line number Diff line number Diff line change
Expand Up @@ -156,8 +156,10 @@
#' @param neg_lb logical. Whether to classify a taxon as a structural zero using
#' its asymptotic lower bound. Default is FALSE.
#' @param alpha numeric. Level of significance. Default is 0.05.
#' @param n_cl numeric. The number of nodes to be forked. For details, see
#' \code{?parallel::makeCluster}. Default is 1 (no parallel computing).
#' @param n_cl numeric. The number of nodes to be forked for parallel processing.
#' This parameter controls parallelization for both the main analysis and
#' sensitivity analysis. For details, see \code{?parallel::makeCluster}.
#' Default is 1 (no parallel computing).
#' @param verbose logical. Whether to generate verbose output during the
#' ANCOM-BC2 fitting process. Default is FALSE.
#' @param global logical. Whether to perform the global test. Default is FALSE.
Expand Down Expand Up @@ -489,24 +491,50 @@ ancombc2 = function(data, taxa_are_rows = TRUE,
if (trend) global = TRUE
iter_control$verbose = FALSE

ss_list = lapply(pseudo_list, function(pseudo_count) {
res_pseudo = .ancombc2_core(data = O1, aggregate_data = O2,
meta_data = meta_data,
fix_formula = fix_formula,
rand_formula = rand_formula,
p_adj_method = p_adj_method,
pseudo = pseudo_count,
s0_perc = s0_perc,
group = group, alpha = alpha,
verbose = FALSE,
global = global, pairwise = pairwise,
dunnet = dunnet, trend = FALSE,
iter_control = iter_control,
em_control = em_control,
lme_control = lme_control,
mdfdr_control = mdfdr_control)
return(res_pseudo)
})
# Use parallel processing for sensitivity analysis if n_cl > 1
if (n_cl > 1) {
# Use the existing cluster setup for sensitivity analysis

ss_list = foreach(pseudo_count = pseudo_list,
.export = c(".ancombc2_core")) %dopar% {
res_pseudo = .ancombc2_core(data = O1, aggregate_data = O2,
meta_data = meta_data,
fix_formula = fix_formula,
rand_formula = rand_formula,
p_adj_method = p_adj_method,
pseudo = pseudo_count,
s0_perc = s0_perc,
group = group, alpha = alpha,
verbose = FALSE,
global = global, pairwise = pairwise,
dunnet = dunnet, trend = FALSE,
iter_control = iter_control,
em_control = em_control,
lme_control = lme_control,
mdfdr_control = mdfdr_control)
return(res_pseudo)
}
} else {
# Fall back to sequential processing
ss_list = lapply(pseudo_list, function(pseudo_count) {
res_pseudo = .ancombc2_core(data = O1, aggregate_data = O2,
meta_data = meta_data,
fix_formula = fix_formula,
rand_formula = rand_formula,
p_adj_method = p_adj_method,
pseudo = pseudo_count,
s0_perc = s0_perc,
group = group, alpha = alpha,
verbose = FALSE,
global = global, pairwise = pairwise,
dunnet = dunnet, trend = FALSE,
iter_control = iter_control,
em_control = em_control,
lme_control = lme_control,
mdfdr_control = mdfdr_control)
return(res_pseudo)
})
}

# Combine main results with sensitivity analysis results
ss_list = c(list(res_main), ss_list)
Expand Down