Improve simulation

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: sccomp
 Type: Package
 Title: Differential Composition and Variability Analysis for Single-Cell Data
-Version: 2.1.23
+Version: 2.1.24
 Date: 2024-01-15
 Authors@R: c(person("Stefano", "Mangiola", email = "stefano.mangiola@unimelb.edu.au", role = c("aut", "cre")), person("Alexandra J.", "Roth-Schulze", role = "aut"), person("Marie", "Trussart", role = "aut"), person("Enrique", "Zozaya-Valdés", role = "aut"), person("Mengyao", "Ma", role = "aut"), person("Zijie", "Gao", role = "aut"), person("Alan F.", "Rubin", role = "aut"), person("Terence P.", "Speed", role = "aut"), person("Heejung", "Shim", role = "aut"), person("Anthony T.", "Papenfuss", role = "aut"))
 Description: Comprehensive R package for differential composition and variability analysis in single-cell RNA sequencing, CyTOF, and microbiome data. Provides robust Bayesian modeling with outlier detection, random effects, and advanced statistical methods for cell type proportion analysis. Features include probabilistic outlier identification, mixed-effect modeling, differential variability testing, and comprehensive visualization tools. Perfect for cancer research, immunology, developmental biology, and single-cell genomics applications.

NAMESPACE

@@ -12,8 +12,8 @@ S3method(sccomp_proportional_fold_change,sccomp_tbl)
 S3method(sccomp_remove_outliers,sccomp_tbl)
 S3method(sccomp_remove_unwanted_effects,sccomp_tbl)
 S3method(sccomp_replicate,sccomp_tbl)
+S3method(sccomp_simulate,sccomp_tbl)
 S3method(sccomp_test,sccomp_tbl)
-S3method(simulate_data,tbl)
 export(clear_draw_files)
 export(clear_stan_model_cache)
 export(plot_1D_intervals)
@@ -27,6 +27,7 @@ export(sccomp_remove_outliers)
 export(sccomp_remove_unwanted_effects)
 export(sccomp_remove_unwanted_variation)
 export(sccomp_replicate)
+export(sccomp_simulate)
 export(sccomp_stan_models_cache_dir)
 export(sccomp_test)
 export(sccomp_theme)
@@ -119,6 +120,7 @@ importFrom(rlang,quo_is_symbolic)
 importFrom(rlang,quo_name)
 importFrom(rlang,quo_squash)
 importFrom(rlang,set_names)
+importFrom(rlang,sym)
 importFrom(scales,trans_new)
 importFrom(stats,C)
 importFrom(stats,as.formula)

R/utilities.R

@@ -664,7 +664,62 @@ calculate_na_fraction_contribution = function(my_design_matrix, na_cols, design_
     bind_rows()
 }
 
-
+#' Normalize sum_to_zero_vector parameters in posterior draws
+#'
+#' @description
+#' This function normalizes sum_to_zero_vector parameters in Stan posterior draws to ensure
+#' they sum to exactly zero. This fixes floating-point precision issues when using
+#' generate_quantities with sum_to_zero_vector types.
+#'
+#' @details
+#' Stan's sum_to_zero_vector type has a strict constraint that the sum must be exactly zero.
+#' When posterior draws are saved and reloaded, floating-point precision can cause the sum
+#' to deviate slightly from zero, causing generate_quantities to fail. This function fixes
+#' this by setting the last element of each vector to exactly the negative sum of all others,
+#' guaranteeing a zero sum.
+#'
+#' @param draws A matrix of posterior draws from a Stan model
+#' @param param_pattern A regular expression pattern matching the parameter names to normalize
+#'   (e.g., "^beta_raw\\[" for beta_raw parameters)
+#'
+#' @return A matrix with normalized parameters that sum to exactly zero
+#'
+#' @keywords internal
+#' @noRd
+#'
+normalize_sum_to_zero_params = function(draws, param_pattern) {
+  param_cols = grep(param_pattern, colnames(draws))
+  if(length(param_cols) == 0) return(draws)
+
+  # Parse parameter names to extract indices
+  param_names = colnames(draws)[param_cols]
+  # Extract first index (grouping dimension) and second index (M dimension)
+  first_indices = gsub(paste0("^", param_pattern, "\\[(\\d+),.*"), "\\1", param_names)
+  second_indices = gsub(paste0("^", param_pattern, "\\[\\d+,(\\d+)\\]"), "\\1", param_names)
+
+  # Group by first index and normalize each group
+  unique_groups = unique(first_indices)
+  for(group_idx in unique_groups) {
+    group_cols = param_cols[first_indices == group_idx]
+    if(length(group_cols) > 1) {
+      # Get the M indices for this group to find the last element
+      m_indices = as.integer(second_indices[first_indices == group_idx])
+      last_m_idx = max(m_indices)
+      last_col_idx = group_cols[m_indices == last_m_idx]
+
+      # Normalize each row's vector to sum to exactly zero
+      for(i in seq_len(nrow(draws))) {
+        vec = as.numeric(draws[i, group_cols])
+        # Calculate sum of all but the last element
+        sum_others = sum(vec[-which(group_cols == last_col_idx)])
+        # Set last element to exactly negative sum of others (guarantees zero sum)
+        vec[which(group_cols == last_col_idx)] = -sum_others
+        draws[i, group_cols] = vec
+      }
+    }
+  }
+  return(draws)
+}
 
 #' @importFrom purrr when
 #' @importFrom stats model.matrix