From a9f24445cd73eb7ba1bb1b4ce41f886dde565418 Mon Sep 17 00:00:00 2001
From: aduvermy <arnaud.duvermy@ens-lyon.fr>
Date: Fri, 5 Apr 2024 09:35:16 +0200
Subject: [PATCH] normal_distrib = 'univariate' as default behavior

Former-commit-id: 702392f90154be2cfa1b9290a268c7043a2640ea
Former-commit-id: 6fc27d8a3326e5b470155c5f647fcbd1d6f40f83
Former-commit-id: 1e4392d3ae9eb809adbdd073ddd9afbd3c592a67
---
 R/mock_rnaseq.R                             |  6 +++---
 R/simulation.R                              |  6 +++---
 dev/flat_full.Rmd                           | 24 ++++++++++-----------
 man/getInput2simulation.Rd                  |  6 +++---
 man/mock_rnaseq.Rd                          |  6 +++---
 tests/testthat/test-actual_mainfixeffects.R |  8 +++----
 tests/testthat/test-evaluate_dispersion.R   |  2 +-
 tests/testthat/test-simulation.R            |  2 +-
 8 files changed, 30 insertions(+), 30 deletions(-)

diff --git a/R/mock_rnaseq.R b/R/mock_rnaseq.R
index f727fa5..8af2643 100644
--- a/R/mock_rnaseq.R
+++ b/R/mock_rnaseq.R
@@ -102,9 +102,9 @@ warning_too_low_mu_ij_row <- function(mu_ij_matrix, threshold = 1 ){
 #' @param sequencing_depth Sequencing depth
 #' @param basal_expression base expression gene
 #' @param dispersion User-provided dispersion vector (optional)
-#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' or 'multivariate' (default).
+#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' (default) or 'multivariate' .
 #' - 'univariate': Effects are drawn independently from univariate normal distributions. 
-#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. 
+#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. (not recommended)
 #' @return List containing the ground truth, counts, and metadata
 #' @export
 #' @examples
@@ -113,7 +113,7 @@ warning_too_low_mu_ij_row <- function(mu_ij_matrix, threshold = 1 ){
 #'               max_replicates = 4)
 mock_rnaseq <- function(list_var, n_genes, min_replicates, max_replicates, sequencing_depth = NULL,  
                         basal_expression = 0 , dispersion = stats::runif(n_genes, min = 0, max = 1000), 
-                        normal_distr = "multivariate") {
+                        normal_distr = "univariate") {
   
   ## -- get my effect
   df_inputSimulation <- getInput2simulation(list_var, n_genes, normal_distr )
diff --git a/R/simulation.R b/R/simulation.R
index 22f27ad..8c7bcc9 100644
--- a/R/simulation.R
+++ b/R/simulation.R
@@ -7,9 +7,9 @@
 #'
 #' @param list_var A list of variables (already initialized)
 #' @param n_genes Number of genes to simulate (default: 1)
-#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' or 'multivariate' (default).
+#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' (default) or 'multivariate' .
 #' - 'univariate': Effects are drawn independently from univariate normal distributions. 
-#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. 
+#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. (not recommended)
 #' @param input2mvrnorm Input to the \code{mvrnorm} function for simulating data from multivariate normal distribution (default: NULL)
 #' @return A data frame with input coefficients for simulation
 #' @export
@@ -17,7 +17,7 @@
 #' # Example usage
 #' list_var <- init_variable()
 #' getInput2simulation(list_var, n_genes = 10)
-getInput2simulation <- function(list_var, n_genes = 1, normal_distr = "multivariate",  input2mvrnorm = NULL) {
+getInput2simulation <- function(list_var, n_genes = 1, normal_distr = "univariate",  input2mvrnorm = NULL) {
   
   stopifnot( normal_distr %in% c("multivariate", "univariate") )
 
diff --git a/dev/flat_full.Rmd b/dev/flat_full.Rmd
index ab6be46..4b0e9e6 100644
--- a/dev/flat_full.Rmd
+++ b/dev/flat_full.Rmd
@@ -1529,9 +1529,9 @@ test_that("set_correlation sets the correlation between variables correctly", {
 #'
 #' @param list_var A list of variables (already initialized)
 #' @param n_genes Number of genes to simulate (default: 1)
-#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' or 'multivariate' (default).
+#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' (default) or 'multivariate' .
 #' - 'univariate': Effects are drawn independently from univariate normal distributions. 
-#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. 
+#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. (not recommended)
 #' @param input2mvrnorm Input to the \code{mvrnorm} function for simulating data from multivariate normal distribution (default: NULL)
 #' @return A data frame with input coefficients for simulation
 #' @export
@@ -1539,7 +1539,7 @@ test_that("set_correlation sets the correlation between variables correctly", {
 #' # Example usage
 #' list_var <- init_variable()
 #' getInput2simulation(list_var, n_genes = 10)
-getInput2simulation <- function(list_var, n_genes = 1, normal_distr = "multivariate",  input2mvrnorm = NULL) {
+getInput2simulation <- function(list_var, n_genes = 1, normal_distr = "univariate",  input2mvrnorm = NULL) {
   
   stopifnot( normal_distr %in% c("multivariate", "univariate") )
 
@@ -2020,7 +2020,7 @@ test_that("get_effects_from_rnorm generates correct effects", {
 test_that("getInput2simulation returns a data frame", {
   list_var <- init_variable()
   set.seed(101)
-  result <- getInput2simulation(list_var)
+  result <- getInput2simulation(list_var, normal_distr = 'multivariate')
   expect_is(result, "data.frame")
   expected <- data.frame(geneID = c("gene1", "gene1"), label_myVariable = as.factor(c("myVariable1", "myVariable2")), 
                          myVariable = c(-0.1414214,0.1414214))
@@ -2408,9 +2408,9 @@ warning_too_low_mu_ij_row <- function(mu_ij_matrix, threshold = 1 ){
 #' @param sequencing_depth Sequencing depth
 #' @param basal_expression base expression gene
 #' @param dispersion User-provided dispersion vector (optional)
-#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' or 'multivariate' (default).
+#' @param normal_distr Specifies the distribution type for generating effects. Choose between 'univariate' (default) or 'multivariate' .
 #' - 'univariate': Effects are drawn independently from univariate normal distributions. 
-#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. 
+#' - 'multivariate': Effects are drawn jointly from a multivariate normal distribution. (not recommended)
 #' @return List containing the ground truth, counts, and metadata
 #' @export
 #' @examples
@@ -2419,7 +2419,7 @@ warning_too_low_mu_ij_row <- function(mu_ij_matrix, threshold = 1 ){
 #'               max_replicates = 4)
 mock_rnaseq <- function(list_var, n_genes, min_replicates, max_replicates, sequencing_depth = NULL,  
                         basal_expression = 0 , dispersion = stats::runif(n_genes, min = 0, max = 1000), 
-                        normal_distr = "multivariate") {
+                        normal_distr = "univariate") {
   
   ## -- get my effect
   df_inputSimulation <- getInput2simulation(list_var, n_genes, normal_distr )
@@ -4992,7 +4992,7 @@ extract_tmbDispersion <- function(list_tmb) {
 
 
 test_that("extract_tmbDispersion function extracts dispersion correctly", {
-   N_GENES = 100
+   N_GENES = 50
   MAX_REPLICATES = 5
   MIN_REPLICATES = 5
   input_var_list <- init_variable(name = "varA", mu = 10, sd = 0.1, level = 3)
@@ -5656,7 +5656,7 @@ test_that("subsetByTermLabel with non-existent term label", {
 test_that("getActualMainFixEff", {
   input_var_list <- init_variable() 
   set.seed(101)
-  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2)
+  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2, normal_distr = "multivariate")
   data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL)
   inference <- fitModelParallel(kij ~ myVariable , 
                                   group_by = "geneID", data2fit, n.cores = 1)
@@ -5683,7 +5683,7 @@ test_that("getData2computeActualFixEffect return correct output",{
   # Prepare the test data
   input_var_list <- init_variable() 
   set.seed(101)
-  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2)
+  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2, normal_distr = "multivariate")
   data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL)
   inference <- fitModelParallel(kij ~ myVariable, group_by = "geneID", data2fit, n.cores = 1)
   tidy_inference <- tidy_tmb(inference)
@@ -5706,8 +5706,8 @@ test_that("generateActualForMainFixEff returns correct values for main fixed eff
   # Prepare the test data
   input_var_list <- init_variable() 
   set.seed(101)
-  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2)
-  data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL)
+  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2, normal_distr = "multivariate")
+  data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL )
   fixEff_dataActual <- getData2computeActualFixEffect(mock_data$groundTruth$effects)
   actual_intercept <- getActualIntercept(fixEff_dataActual)
   df_term <- generateActualForMainFixEff("myVariable2", actual_intercept, fixEff_dataActual$data, fixEff_dataActual$categorical_vars)
diff --git a/man/getInput2simulation.Rd b/man/getInput2simulation.Rd
index f50ce20..2e65d14 100644
--- a/man/getInput2simulation.Rd
+++ b/man/getInput2simulation.Rd
@@ -7,7 +7,7 @@
 getInput2simulation(
   list_var,
   n_genes = 1,
-  normal_distr = "multivariate",
+  normal_distr = "univariate",
   input2mvrnorm = NULL
 )
 }
@@ -16,10 +16,10 @@ getInput2simulation(
 
 \item{n_genes}{Number of genes to simulate (default: 1)}
 
-\item{normal_distr}{Specifies the distribution type for generating effects. Choose between 'univariate' or 'multivariate' (default).
+\item{normal_distr}{Specifies the distribution type for generating effects. Choose between 'univariate' (default) or 'multivariate' .
 \itemize{
 \item 'univariate': Effects are drawn independently from univariate normal distributions.
-\item 'multivariate': Effects are drawn jointly from a multivariate normal distribution.
+\item 'multivariate': Effects are drawn jointly from a multivariate normal distribution. (not recommended)
 }}
 
 \item{input2mvrnorm}{Input to the \code{mvrnorm} function for simulating data from multivariate normal distribution (default: NULL)}
diff --git a/man/mock_rnaseq.Rd b/man/mock_rnaseq.Rd
index c68b463..2c303e8 100644
--- a/man/mock_rnaseq.Rd
+++ b/man/mock_rnaseq.Rd
@@ -12,7 +12,7 @@ mock_rnaseq(
   sequencing_depth = NULL,
   basal_expression = 0,
   dispersion = stats::runif(n_genes, min = 0, max = 1000),
-  normal_distr = "multivariate"
+  normal_distr = "univariate"
 )
 }
 \arguments{
@@ -30,10 +30,10 @@ mock_rnaseq(
 
 \item{dispersion}{User-provided dispersion vector (optional)}
 
-\item{normal_distr}{Specifies the distribution type for generating effects. Choose between 'univariate' or 'multivariate' (default).
+\item{normal_distr}{Specifies the distribution type for generating effects. Choose between 'univariate' (default) or 'multivariate' .
 \itemize{
 \item 'univariate': Effects are drawn independently from univariate normal distributions.
-\item 'multivariate': Effects are drawn jointly from a multivariate normal distribution.
+\item 'multivariate': Effects are drawn jointly from a multivariate normal distribution. (not recommended)
 }}
 }
 \value{
diff --git a/tests/testthat/test-actual_mainfixeffects.R b/tests/testthat/test-actual_mainfixeffects.R
index c8ca602..68f8081 100644
--- a/tests/testthat/test-actual_mainfixeffects.R
+++ b/tests/testthat/test-actual_mainfixeffects.R
@@ -138,7 +138,7 @@ test_that("subsetByTermLabel with non-existent term label", {
 test_that("getActualMainFixEff", {
   input_var_list <- init_variable() 
   set.seed(101)
-  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2)
+  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2, normal_distr = "multivariate")
   data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL)
   inference <- fitModelParallel(kij ~ myVariable , 
                                   group_by = "geneID", data2fit, n.cores = 1)
@@ -165,7 +165,7 @@ test_that("getData2computeActualFixEffect return correct output",{
   # Prepare the test data
   input_var_list <- init_variable() 
   set.seed(101)
-  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2)
+  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2, normal_distr = "multivariate")
   data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL)
   inference <- fitModelParallel(kij ~ myVariable, group_by = "geneID", data2fit, n.cores = 1)
   tidy_inference <- tidy_tmb(inference)
@@ -188,8 +188,8 @@ test_that("generateActualForMainFixEff returns correct values for main fixed eff
   # Prepare the test data
   input_var_list <- init_variable() 
   set.seed(101)
-  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2)
-  data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL)
+  mock_data <- mock_rnaseq(input_var_list, 2, 2, 2, normal_distr = "multivariate")
+  data2fit <- prepareData2fit(mock_data$counts, mock_data$metadata, normalization = NULL )
   fixEff_dataActual <- getData2computeActualFixEffect(mock_data$groundTruth$effects)
   actual_intercept <- getActualIntercept(fixEff_dataActual)
   df_term <- generateActualForMainFixEff("myVariable2", actual_intercept, fixEff_dataActual$data, fixEff_dataActual$categorical_vars)
diff --git a/tests/testthat/test-evaluate_dispersion.R b/tests/testthat/test-evaluate_dispersion.R
index 5537b9c..60ba9bb 100644
--- a/tests/testthat/test-evaluate_dispersion.R
+++ b/tests/testthat/test-evaluate_dispersion.R
@@ -4,7 +4,7 @@
 
 
 test_that("extract_tmbDispersion function extracts dispersion correctly", {
-   N_GENES = 100
+   N_GENES = 50
   MAX_REPLICATES = 5
   MIN_REPLICATES = 5
   input_var_list <- init_variable(name = "varA", mu = 10, sd = 0.1, level = 3)
diff --git a/tests/testthat/test-simulation.R b/tests/testthat/test-simulation.R
index 6bd5328..39e9bd4 100644
--- a/tests/testthat/test-simulation.R
+++ b/tests/testthat/test-simulation.R
@@ -27,7 +27,7 @@ test_that("get_effects_from_rnorm generates correct effects", {
 test_that("getInput2simulation returns a data frame", {
   list_var <- init_variable()
   set.seed(101)
-  result <- getInput2simulation(list_var)
+  result <- getInput2simulation(list_var, normal_distr = 'multivariate')
   expect_is(result, "data.frame")
   expected <- data.frame(geneID = c("gene1", "gene1"), label_myVariable = as.factor(c("myVariable1", "myVariable2")), 
                          myVariable = c(-0.1414214,0.1414214))
-- 
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