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Add cache_stan_model parameter to relevant functions and update docum… #222

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16fbb8b
Add cache_stan_model parameter to relevant functions and update docum…
stemangiola Jul 19, 2025
49574b4
version UP
stemangiola Jul 19, 2025
2ba0c5c
fixed documentation
stemangiola Jul 21, 2025
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1 change: 1 addition & 0 deletions NAMESPACE
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Expand Up @@ -28,6 +28,7 @@ export(sccomp_remove_unwanted_variation)
export(sccomp_replicate)
export(sccomp_test)
export(sccomp_theme)
export(sccomp_stan_models_cache_dir)
export(simulate_data)
import(dplyr)
import(ggplot2)
Expand Down
11 changes: 9 additions & 2 deletions R/model_fitting.R
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Expand Up @@ -5,6 +5,7 @@ fit_model = function(
seed , pars = c("beta", "alpha", "prec_coeff","prec_sd"), output_samples = NULL, chains=NULL, max_sampling_iterations = 20000,
output_directory = "sccomp_draws_files",
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...
)
{
Expand Down Expand Up @@ -82,7 +83,7 @@ fit_model = function(
dir.create(output_directory, showWarnings = FALSE)

# Fit
mod = load_model(model_name, threads = cores)
mod = load_model(model_name, threads = cores, cache_dir = cache_stan_model)

# Avoid 0 proportions
if(data_for_model$is_proportion && min(data_for_model$y_proportion)==0){
Expand Down Expand Up @@ -144,6 +145,7 @@ fit_model = function(
psis_resample = FALSE,
verbose = verbose,
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
show_exceptions = FALSE,
...
)
Expand Down Expand Up @@ -203,6 +205,10 @@ load_model <- function(name, cache_dir = sccomp_stan_models_cache_dir, force=FAL
# }, error = function(e) {
# Try to load the model from cache

# Handle cache directory - always add version to ensure version isolation
sccomp_version <- as.character(packageVersion("sccomp"))
cache_dir <- file.path(cache_dir, sccomp_version)

# RDS compiled model
cache_dir |> dir.create(showWarnings = FALSE, recursive = TRUE)
cache_file <- file.path(cache_dir, paste0(name, ".rds"))
Expand Down Expand Up @@ -335,6 +341,7 @@ vb_iterative = function(model,
verbose = TRUE,
psis_resample = FALSE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...) {
res = NULL
i = 0
Expand Down Expand Up @@ -384,7 +391,7 @@ vb_iterative = function(model,
error = function(e) {
if(e$message |> str_detect("The Stan file used to create the `CmdStanModel` object does not exist\\.")) {
clear_stan_model_cache()
model <<- load_model(model_name, force=TRUE, threads = cores)
model <<- load_model(model_name, force=TRUE, threads = cores, cache_dir = cache_stan_model)
}
else writeLines(sprintf("Further attempt with Variational Bayes: %s", e))

Expand Down
23 changes: 20 additions & 3 deletions R/sccomp_estimate.R
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Expand Up @@ -48,6 +48,9 @@
#' @param max_sampling_iterations Integer to limit the maximum number of iterations for large datasets.
#' @param pass_fit Logical, whether to include the Stan fit as an attribute in the output.
#' @param sig_figs Number of significant figures to use for Stan model output. Default is 9.
#' @param cache_stan_model A character string specifying the cache directory for compiled Stan models.
#' The sccomp version will be automatically appended to ensure version isolation.
#' Default is `sccomp_stan_models_cache_dir` which points to `~/.sccomp_models`.
#' @param .count **DEPRECATED**. Use `abundance` instead.
#' @param approximate_posterior_inference **DEPRECATED**. Use `inference_method` instead.
#' @param variational_inference **DEPRECATED**. Use `inference_method` instead.
Expand Down Expand Up @@ -157,6 +160,7 @@ sccomp_estimate <- function(.data,
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...,

# DEPRECATED
Expand Down Expand Up @@ -248,6 +252,7 @@ sccomp_estimate.Seurat <- function(.data,
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...,

# DEPRECATED
Expand Down Expand Up @@ -300,6 +305,7 @@ sccomp_estimate.Seurat <- function(.data,
max_sampling_iterations = max_sampling_iterations,
pass_fit = pass_fit,
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
...,
.count = !!.count,
approximate_posterior_inference = approximate_posterior_inference,
Expand Down Expand Up @@ -341,6 +347,7 @@ sccomp_estimate.SingleCellExperiment <- function(.data,
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...,

# DEPRECATED
Expand Down Expand Up @@ -394,6 +401,7 @@ sccomp_estimate.SingleCellExperiment <- function(.data,
max_sampling_iterations = max_sampling_iterations,
pass_fit = pass_fit,
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
...,
.count = !!.count,
approximate_posterior_inference = approximate_posterior_inference,
Expand Down Expand Up @@ -435,6 +443,7 @@ sccomp_estimate.DFrame <- function(.data,
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...,

# DEPRECATED
Expand Down Expand Up @@ -477,7 +486,8 @@ sccomp_estimate.DFrame <- function(.data,
use_data = use_data,
mcmc_seed = mcmc_seed,
max_sampling_iterations = max_sampling_iterations,
pass_fit = pass_fit,
pass_fit = pass_fit,
cache_stan_model = cache_stan_model,
...,
.count = !!.count,
approximate_posterior_inference = approximate_posterior_inference,
Expand Down Expand Up @@ -522,6 +532,7 @@ sccomp_estimate.data.frame <- function(.data,
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...,

# DEPRECATED
Expand Down Expand Up @@ -642,6 +653,7 @@ sccomp_estimate.data.frame <- function(.data,
max_sampling_iterations = max_sampling_iterations,
pass_fit = pass_fit,
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
...
)

Expand Down Expand Up @@ -672,6 +684,7 @@ sccomp_estimate.data.frame <- function(.data,
max_sampling_iterations = max_sampling_iterations,
pass_fit = pass_fit,
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
...
)

Expand Down Expand Up @@ -718,6 +731,7 @@ sccomp_glm_data_frame_raw = function(.data,
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...) {

# See https://community.rstudio.com/t/how-to-make-complete-nesting-work-with-quosures-and-tidyeval/16473
Expand Down Expand Up @@ -794,6 +808,7 @@ sccomp_glm_data_frame_raw = function(.data,
max_sampling_iterations = max_sampling_iterations,
pass_fit = pass_fit,
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
...
)
}
Expand Down Expand Up @@ -827,8 +842,9 @@ sccomp_glm_data_frame_counts = function(.data,
cores = 4,
mcmc_seed = sample_seed(),
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
...) {

# Prepare column same enquo
Expand Down Expand Up @@ -1004,6 +1020,7 @@ sccomp_glm_data_frame_counts = function(.data,
"log_lik"
),
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
...
)

Expand Down
9 changes: 8 additions & 1 deletion R/sccomp_remove_outliers.R
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Expand Up @@ -23,6 +23,9 @@
#' @param max_sampling_iterations Integer, limits the maximum number of iterations in case a large dataset is used, to limit computation time.
#' @param enable_loo Logical, whether to enable model comparison using the R package LOO. This is useful for comparing fits between models, similar to ANOVA.
#' @param sig_figs Number of significant figures to use for Stan model output. Default is 9.
#' @param cache_stan_model A character string specifying the cache directory for compiled Stan models.
#' The sccomp version will be automatically appended to ensure version isolation.
#' Default is `sccomp_stan_models_cache_dir` which points to `~/.sccomp_models`.
#' @param approximate_posterior_inference DEPRECATED, use the `variational_inference` argument.
#' @param variational_inference DEPRECATED Logical, whether to use variational Bayes for posterior inference. It is faster and convenient. Setting this argument to `FALSE` runs full Bayesian (Hamiltonian Monte Carlo) inference, which is slower but the gold standard.
#' @param ... Additional arguments passed to the `cmdstanr::sample` function.
Expand Down Expand Up @@ -94,6 +97,7 @@ sccomp_remove_outliers <- function(.estimate,
max_sampling_iterations = 20000,
enable_loo = FALSE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,

# DEPRECATED
approximate_posterior_inference = NULL,
Expand Down Expand Up @@ -125,6 +129,7 @@ sccomp_remove_outliers.sccomp_tbl = function(.estimate,
max_sampling_iterations = 20000,
enable_loo = FALSE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,

# DEPRECATED
approximate_posterior_inference = NULL,
Expand Down Expand Up @@ -180,7 +185,7 @@ sccomp_remove_outliers.sccomp_tbl = function(.estimate,
random_effect_elements = .estimate |> attr("formula_composition") |> parse_formula_random_effect()

# Load model
mod_rng = load_model("glm_multi_beta_binomial_generate_data", threads = cores)
mod_rng = load_model("glm_multi_beta_binomial_generate_data", threads = cores, cache_dir = cache_stan_model)

rng = mod_rng |> sample_safe(
generate_quantities_fx,
Expand Down Expand Up @@ -323,6 +328,7 @@ sccomp_remove_outliers.sccomp_tbl = function(.estimate,
max_sampling_iterations = max_sampling_iterations,
pars = c("beta", "alpha", "prec_coeff", "prec_sd", "alpha_normalised", "random_effect", "random_effect_2"),
sig_figs = sig_figs,
cache_stan_model = cache_stan_model,
...
)

Expand Down Expand Up @@ -453,6 +459,7 @@ sccomp_remove_outliers.sccomp_tbl = function(.estimate,
seed = mcmc_seed,
max_sampling_iterations = max_sampling_iterations,
pars = c("beta", "alpha", "prec_coeff","prec_sd", "alpha_normalised", "random_effect", "random_effect_2", "log_lik"),
cache_stan_model = cache_stan_model,
...
)

Expand Down
17 changes: 12 additions & 5 deletions R/sccomp_replicate.R
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Expand Up @@ -7,6 +7,9 @@
#' @param formula_variability A formula. The formula describing the model for differential variability, for example ~treatment. In most cases, if differentially variability is of interest, the formula should only include the factor of interest as a large anount of data is needed to define variability depending to each factors. This formula can be a sub-formula of your estimated model; in this case all other factor will be factored out.
#' @param number_of_draws An integer. How may copies of the data you want to draw from the model joint posterior distribution.
#' @param mcmc_seed An integer. Used for Markov-chain Monte Carlo reproducibility. By default a random number is sampled from 1 to 999999. This itself can be controlled by set.seed()
#' @param cache_stan_model A character string specifying the cache directory for compiled Stan models.
#' The sccomp version will be automatically appended to ensure version isolation.
#' Default is `sccomp_stan_models_cache_dir` which points to `~/.sccomp_models`.
#'
#' @return A tibble `tbl` with cell_group-wise statistics
#'
Expand Down Expand Up @@ -48,7 +51,8 @@ sccomp_replicate <- function(fit,
formula_composition = NULL,
formula_variability = NULL,
number_of_draws = 1,
mcmc_seed = sample_seed()) {
mcmc_seed = sample_seed(),
cache_stan_model = sccomp_stan_models_cache_dir) {

# Run the function
check_and_install_cmdstanr()
Expand All @@ -62,7 +66,8 @@ sccomp_replicate.sccomp_tbl = function(fit,
formula_composition = NULL,
formula_variability = NULL,
number_of_draws = 1,
mcmc_seed = sample_seed()){
mcmc_seed = sample_seed(),
cache_stan_model = sccomp_stan_models_cache_dir){

.sample = attr(fit, ".sample")
.cell_group = attr(fit, ".cell_group")
Expand All @@ -79,7 +84,8 @@ sccomp_replicate.sccomp_tbl = function(fit,
formula_composition = formula_composition,
formula_variability = formula_variability,
number_of_draws = number_of_draws,
mcmc_seed = mcmc_seed
mcmc_seed = mcmc_seed,
cache_stan_model = cache_stan_model
)

model_input = attr(fit, "model_input")
Expand Down Expand Up @@ -453,7 +459,8 @@ replicate_data = function(.data,
new_data = NULL,
number_of_draws = 1,
mcmc_seed = sample(1e5, 1),
cores = detectCores()){
cores = detectCores(),
cache_stan_model = sccomp_stan_models_cache_dir){

# Extract required components from .data
.sample = attr(.data, ".sample")
Expand Down Expand Up @@ -529,7 +536,7 @@ replicate_data = function(.data,
number_of_draws = min(number_of_draws, number_of_draws_in_the_fit)

# Load model
mod_rng = load_model("glm_multi_beta_binomial_generate_data", threads = cores)
mod_rng = load_model("glm_multi_beta_binomial_generate_data", threads = cores, cache_dir = cache_stan_model)

draws_matrix <- attr(.data, "fit")$draws(format = "matrix")

Expand Down
11 changes: 8 additions & 3 deletions R/simulate_data.R
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Expand Up @@ -23,6 +23,9 @@
#' @param mcmc_seed An integer. Used for Markov-chain Monte Carlo reproducibility. By default a random number is sampled from 1 to 999999. This itself can be controlled by set.seed()#' @param cores Integer, the number of cores to be used for parallel calculations.
#' @param cores Integer, the number of cores to be used for parallel calculations.
#' @param sig_figs Number of significant figures to use for Stan model output. Default is 9.
#' @param cache_stan_model A character string specifying the cache directory for compiled Stan models.
#' The sccomp version will be automatically appended to ensure version isolation.
#' Default is `sccomp_stan_models_cache_dir` which points to `~/.sccomp_models`.
#'
#' @return A tibble (`tbl`) with the following columns:
#' \itemize{
Expand Down Expand Up @@ -79,7 +82,8 @@ simulate_data <- function(.data,
number_of_draws = 1,
mcmc_seed = sample_seed(),
cores = detectCores(),
sig_figs = 9) {
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir) {

# Run the function
check_and_install_cmdstanr()
Expand Down Expand Up @@ -107,7 +111,8 @@ simulate_data.tbl = function(.data,
number_of_draws = 1,
mcmc_seed = sample_seed(),
cores = detectCores(),
sig_figs = 9) {
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir) {


.sample = enquo(.sample)
Expand Down Expand Up @@ -144,7 +149,7 @@ simulate_data.tbl = function(.data,
# [1] 5.6260004 -0.6940178
# prec_sd = 0.816423129

mod_rng = load_model("glm_multi_beta_binomial_simulate_data", threads = cores)
mod_rng = load_model("glm_multi_beta_binomial_simulate_data", threads = cores, cache_dir = cache_stan_model)

fit = mod_rng |> sample_safe(
generate_quantities_fx,
Expand Down
4 changes: 2 additions & 2 deletions R/utilities.R
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Expand Up @@ -7,8 +7,8 @@ get_sccomp_cache_dir <- function() {
file.path(path.expand("~"), ".sccomp_models", packageVersion("sccomp"))
}

# Define global variable
sccomp_stan_models_cache_dir = get_sccomp_cache_dir()
# Define global variable (without version - version will be added in load_model when needed)
sccomp_stan_models_cache_dir = file.path(path.expand("~"), ".sccomp_models")

#' Add attribute to abject
#'
Expand Down
5 changes: 5 additions & 0 deletions man/sccomp_estimate.Rd
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5 changes: 5 additions & 0 deletions man/sccomp_remove_outliers.Rd
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