R/posterior.R
In linnykos/eSVD2: eSVD2 for performing the eSVD-DE for cohort-wide differential expression analysis
Defines functions
.format_param_posterior
compute_posterior.default
compute_posterior.eSVD
compute_posterior
Documented in
compute_posterior
compute_posterior.default
compute_posterior.eSVD
#' Compute posterior according to Gamma-Poisson model
#'
#' Generic function interface
#'
#' @param input_obj Main object
#' @param ... Additional parameters
#'
#' @return Output dependent on class of \code{input_obj}
#' @export
compute_posterior <- function(input_obj, ...) {UseMethod("compute_posterior")}
#' Compute posterior according to Gamma-Poisson model for eSVD object
#'
#' The posterior is computed based on whatever \code{input_obj$latest_Fit} is set to.
#'
#' @param input_obj \code{eSVD} object outputed from \code{opt_esvd.eSVD}.
#' @param alpha_max Maximum value of numerator when computing posterior, default is \code{1e3}.
#' @param bool_adjust_covariates Boolean to adjust the numerator in the posterior by the donor covariates, default is \code{FALSE}.
#' This parameter is experimental, and we have not yet encountered a scenario where it is useful to be set to be \code{TRUE}.
#' @param bool_covariates_as_library Boolean to include the donor covariates effects in the adjusted library size, default is \code{TRUE}
#' @param bool_return_components Boolean to return the numerator and denominator of the posterior terms as well (which will themselves by matrices that are cell-by-gene matrices), default is \code{FALSE}
#' @param bool_stabilize_underdispersion Boolean to stabilize the over-dispersion parameter, specifically to rescale all the over-dispersions the global mean over-disperion is less than 1, default is \code{TRUE}
#' @param library_min All covariate-adjusted library size smaller than this value are set to this value, default is 0.1.
#' @param nuisance_lower_quantile All the nuisance values that are smaller than this quantile
#' are set to this quantile, default is 0.01
#' @param pseudocount The additional count that is added to the count matrix, default is 0.
#' @param ... Additional parameters.
#'
#' @return \code{eSVD} object with \code{posterior_mean_mat}
#' and \code{posterior_var_mat} appended to the list in
#' \code{input_obj[[input_obj[["latest_Fit"]]]]}.
#' @export
compute_posterior.eSVD <- function(input_obj,
alpha_max = 1e3,
bool_adjust_covariates = F,
bool_covariates_as_library = T,
bool_return_components = F,
bool_stabilize_underdispersion = T,
library_min = 0.1,
nuisance_lower_quantile = 0.01,
pseudocount = 0,
...){
stopifnot(inherits(input_obj, "eSVD"), "latest_Fit" %in% names(input_obj),
input_obj[["latest_Fit"]] %in% names(input_obj),
inherits(input_obj[[input_obj[["latest_Fit"]]]], "eSVD_Fit"))
dat <- .get_object(eSVD_obj = input_obj, what_obj = "dat", which_fit = NULL)
covariates <- .get_object(eSVD_obj = input_obj, what_obj = "covariates", which_fit = NULL)
latest_Fit <- .get_object(eSVD_obj = input_obj, what_obj = "latest_Fit", which_fit = NULL)
esvd_res <- .get_object(eSVD_obj = input_obj, what_obj = NULL, which_fit = latest_Fit)
nuisance_vec <- .get_object(eSVD_obj = input_obj, what_obj = "nuisance", which_fit = latest_Fit)
stopifnot(all(nuisance_vec >= 0))
param <- .format_param_posterior(alpha_max = alpha_max,
bool_adjust_covariates = bool_adjust_covariates,
bool_covariates_as_library = bool_covariates_as_library,
bool_return_components = bool_return_components,
bool_stabilize_underdispersion = bool_stabilize_underdispersion,
library_min = library_min,
nuisance_lower_quantile = nuisance_lower_quantile,
pseudocount = pseudocount)
input_obj$param <- .combine_two_named_lists(input_obj$param, param)
case_control_variable <- .get_object(eSVD_obj = input_obj, which_fit = "param", what_obj = "init_case_control_variable")
library_size_variable <- .get_object(eSVD_obj = input_obj, which_fit = "param", what_obj = "init_library_size_variable")
bool_library_includes_interept <- .get_object(eSVD_obj = input_obj, which_fit = "param", what_obj = "nuisance_bool_library_includes_interept")
res <- compute_posterior.default(
input_obj = dat,
case_control_variable = case_control_variable,
covariates = covariates,
esvd_res = esvd_res,
library_size_variable = library_size_variable,
nuisance_vec = nuisance_vec,
alpha_max = alpha_max,
bool_adjust_covariates = bool_adjust_covariates,
bool_covariates_as_library = bool_covariates_as_library,
bool_library_includes_interept = bool_library_includes_interept,
bool_return_components = bool_return_components,
bool_stabilize_underdispersion = bool_stabilize_underdispersion,
library_min = library_min,
nuisance_lower_quantile = nuisance_lower_quantile,
pseudocount = pseudocount
)
input_obj[[latest_Fit]]$posterior_mean_mat <- res$posterior_mean_mat
input_obj[[latest_Fit]]$posterior_var_mat <- res$posterior_var_mat
if(bool_return_components){
input_obj[[latest_Fit]]$numerator_mat <- res$numerator_mat
input_obj[[latest_Fit]]$denominator_mat <- res$denominator_mat
}
input_obj
}
#' Compute posterior according to Gamma-Poisson model for matrices and sparse matrices.
#'
#' @param input_obj Dataset (either \code{matrix} or \code{dgCMatrix}) where the \eqn{n} rows represent cells
#' and \eqn{p} columns represent genes.
#' The rows and columns of the matrix should be named.
#' @param case_control_variable A string of the column name of \code{covariates} which depicts the case-control
#' status of each cell. Notably, this should be a binary variable where a \code{1}
#' is hard-coded to describe case, and a \code{0} to describe control.
#' @param covariates \code{matrix} object with \eqn{n} rows with the same rownames as \code{dat} where the columns
#' represent the different covariates.
#' Notably, this should contain only numerical columns (i.e., all categorical
#' variables should have already been split into numerous indicator variables).
#' @param esvd_res Output of \code{opt_esvd.default}, notably with elements
#' \code{x_mat}, \code{y_mat} and \code{z_mat}
#' @param library_size_variable A string of the variable name (which must be in \code{covariates}) of which variable denotes the sequenced (i.e., observed) library size.
#' @param nuisance_vec Vector of non-negative numerics of length \code{ncol(input_obj)}, such as
#' the output of \code{estimate_nuisance.default}.
#' @param alpha_max Maximum value of numerator when computing posterior, default is \code{1e3}.
#' @param bool_adjust_covariates Boolean to adjust the numerator in the posterior by the donor covariates, default is \code{FALSE}.
#' This parameter is experimental, and we have not yet encountered a scenario where it is useful to be set to be \code{TRUE}.
#' @param bool_covariates_as_library Boolean to include the donor covariates effects in the adjusted library size, default is \code{TRUE}.
#' @param bool_library_includes_interept Boolean if the intercept term from the eSVD matrix factorization should be included in the calculation for the covariate-adjusted library size, default is \code{TRUE}.
#' @param bool_return_components Boolean to return the numerator and denominator of the posterior terms as well (which will themselves by matrices that are cell-by-gene matrices), default is \code{FALSE}.
#' @param bool_stabilize_underdispersion Boolean to stabilize the over-dispersion parameter, specifically to rescale all the over-dispersions the global mean over-disperion is less than 1, default is \code{TRUE}.
#' @param library_min All covariate-adjusted library size smaller than this value are set to this value, default is 0.1.
#' @param nuisance_lower_quantile All the nuisance values that are smaller than this quantile
#' are set to this quantile.
#' @param pseudocount The additional count that is added to the count matrix, default is 0.
#' @param ... Additional parameters.
#'
#' @return A \code{list} of elements \code{posterior_mean_mat}
#' and \code{posterior_var_mat}
#' @export
compute_posterior.default <- function(input_obj,
case_control_variable,
covariates,
esvd_res,
library_size_variable,
nuisance_vec,
alpha_max = 1e3,
bool_adjust_covariates = F,
bool_covariates_as_library = T,
bool_library_includes_interept = T,
bool_return_components = F,
bool_stabilize_underdispersion = T,
library_min = 0.1,
nuisance_lower_quantile = 0.01,
pseudocount = 0,
...){
stopifnot(inherits(input_obj, c("matrix", "dgCMatrix")),
is.list(esvd_res),
all(c("x_mat", "y_mat", "z_mat") %in% names(esvd_res)),
case_control_variable %in% colnames(covariates),
library_size_variable %in% colnames(covariates),
all(colnames(covariates) == colnames(esvd_res$z_mat)),
nrow(esvd_res$x_mat) == nrow(covariates),
nrow(esvd_res$y_mat) == nrow(esvd_res$z_mat),
ncol(esvd_res$x_mat) == ncol(esvd_res$y_mat),
!bool_adjust_covariates | !bool_covariates_as_library)
if(inherits(input_obj, "dgCMatrix")) input_obj <- as.matrix(input_obj)
if(!is.null(pseudocount) && pseudocount > 0) input_obj <- input_obj + pseudocount
if(is.null(case_control_variable)) case_control_variable <- numeric(0)
case_control_idx <- which(colnames(covariates) == case_control_variable)
library_size_variables <- library_size_variable
if(bool_covariates_as_library) library_size_variables <- unique(c(library_size_variables,
setdiff(colnames(covariates),
c("Intercept", case_control_variable))))
if(bool_library_includes_interept) library_size_variables <- unique(c("Intercept", library_size_variables))
library_idx <- which(colnames(covariates) %in% library_size_variables)
idx_vec <- c(case_control_idx, library_idx)
nat_mat1 <- tcrossprod(esvd_res$x_mat, esvd_res$y_mat)
nat_mat2 <- tcrossprod(covariates[,-library_idx], esvd_res$z_mat[,-library_idx])
nat_mat_nolib <- nat_mat1 + nat_mat2
mean_mat_nolib <- exp(nat_mat_nolib)
library_mat <- exp(tcrossprod(
covariates[,library_idx], esvd_res$z_mat[,library_idx]
))
if(!is.null(library_min)) library_mat <- pmax(library_mat, library_min)
nuisance_vec <- pmax(nuisance_vec,
stats::quantile(nuisance_vec, probs = nuisance_lower_quantile))
if(bool_stabilize_underdispersion & mean(log10(nuisance_vec)) > 0) {
nuisance_vec <- 10^(scale(log10(nuisance_vec), center = T, scale = F))
}
if(!is.null(alpha_max)) mean_mat_nolib <- pmin(mean_mat_nolib, alpha_max)
Alpha <- sweep(mean_mat_nolib, MARGIN = 2,
STATS = nuisance_vec, FUN = "*")
AplusAlpha <- input_obj + Alpha
# adjust the Alpha's based on the confounding covariates
if(bool_adjust_covariates){
tmp <- log(AplusAlpha)
nat_mat_confounder <- tcrossprod(covariates[,-idx_vec,drop = F],
esvd_res$z_mat[,-idx_vec,drop = F])
AplusAlpha <- exp(tmp - nat_mat_confounder)
}
SplusBeta <- sweep(library_mat, MARGIN = 2,
STATS = nuisance_vec, FUN = "+")
posterior_mean_mat <- AplusAlpha/SplusBeta
posterior_var_mat <- AplusAlpha/SplusBeta^2
rownames(posterior_mean_mat) <- rownames(input_obj)
rownames(posterior_var_mat) <- rownames(input_obj)
colnames(posterior_mean_mat) <- colnames(input_obj)
colnames(posterior_var_mat) <- colnames(input_obj)
if(!bool_return_components){
list(posterior_mean_mat = posterior_mean_mat,
posterior_var_mat = posterior_var_mat)
} else {
list(posterior_mean_mat = posterior_mean_mat,
posterior_var_mat = posterior_var_mat,
numerator_mat = AplusAlpha,
denominator_mat = SplusBeta)
}
}
.format_param_posterior <- function(alpha_max,
bool_adjust_covariates,
bool_covariates_as_library,
bool_return_components,
bool_stabilize_underdispersion,
library_min,
nuisance_lower_quantile,
pseudocount) {
list(posterior_alpha_max = alpha_max,
posterior_bool_adjust_covariates = bool_adjust_covariates,
posterior_bool_covariates_as_library = bool_covariates_as_library,
posterior_bool_return_components = bool_return_components,
posterior_bool_stabilize_underdispersion = bool_stabilize_underdispersion,
posterior_library_min = library_min,
posterior_nuisance_lower_quantile = nuisance_lower_quantile,
posterior_pseudocount = pseudocount)
}
linnykos/eSVD2 documentation built on July 17, 2024, 12:01 a.m.
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Defines functions .format_param_posterior compute_posterior.default compute_posterior.eSVD compute_posterior
Documented in compute_posterior compute_posterior.default compute_posterior.eSVD
#' Compute posterior according to Gamma-Poisson model
#'
#' Generic function interface
#'
#' @param input_obj Main object
#' @param ... Additional parameters
#'
#' @return Output dependent on class of \code{input_obj}
#' @export
compute_posterior <- function(input_obj, ...) {UseMethod("compute_posterior")}
#' Compute posterior according to Gamma-Poisson model for eSVD object
#'
#' The posterior is computed based on whatever \code{input_obj$latest_Fit} is set to.
#'
#' @param input_obj \code{eSVD} object outputed from \code{opt_esvd.eSVD}.
#' @param alpha_max Maximum value of numerator when computing posterior, default is \code{1e3}.
#' @param bool_adjust_covariates Boolean to adjust the numerator in the posterior by the donor covariates, default is \code{FALSE}.
#' This parameter is experimental, and we have not yet encountered a scenario where it is useful to be set to be \code{TRUE}.
#' @param bool_covariates_as_library Boolean to include the donor covariates effects in the adjusted library size, default is \code{TRUE}
#' @param bool_return_components Boolean to return the numerator and denominator of the posterior terms as well (which will themselves by matrices that are cell-by-gene matrices), default is \code{FALSE}
#' @param bool_stabilize_underdispersion Boolean to stabilize the over-dispersion parameter, specifically to rescale all the over-dispersions the global mean over-disperion is less than 1, default is \code{TRUE}
#' @param library_min All covariate-adjusted library size smaller than this value are set to this value, default is 0.1.
#' @param nuisance_lower_quantile All the nuisance values that are smaller than this quantile
#' are set to this quantile, default is 0.01
#' @param pseudocount The additional count that is added to the count matrix, default is 0.
#' @param ... Additional parameters.
#'
#' @return \code{eSVD} object with \code{posterior_mean_mat}
#' and \code{posterior_var_mat} appended to the list in
#' \code{input_obj[[input_obj[["latest_Fit"]]]]}.
#' @export
compute_posterior.eSVD <- function(input_obj,
alpha_max = 1e3,
bool_adjust_covariates = F,
bool_covariates_as_library = T,
bool_return_components = F,
bool_stabilize_underdispersion = T,
library_min = 0.1,
nuisance_lower_quantile = 0.01,
pseudocount = 0,
...){
stopifnot(inherits(input_obj, "eSVD"), "latest_Fit" %in% names(input_obj),
input_obj[["latest_Fit"]] %in% names(input_obj),
inherits(input_obj[[input_obj[["latest_Fit"]]]], "eSVD_Fit"))
dat <- .get_object(eSVD_obj = input_obj, what_obj = "dat", which_fit = NULL)
covariates <- .get_object(eSVD_obj = input_obj, what_obj = "covariates", which_fit = NULL)
latest_Fit <- .get_object(eSVD_obj = input_obj, what_obj = "latest_Fit", which_fit = NULL)
esvd_res <- .get_object(eSVD_obj = input_obj, what_obj = NULL, which_fit = latest_Fit)
nuisance_vec <- .get_object(eSVD_obj = input_obj, what_obj = "nuisance", which_fit = latest_Fit)
stopifnot(all(nuisance_vec >= 0))
param <- .format_param_posterior(alpha_max = alpha_max,
bool_adjust_covariates = bool_adjust_covariates,
bool_covariates_as_library = bool_covariates_as_library,
bool_return_components = bool_return_components,
bool_stabilize_underdispersion = bool_stabilize_underdispersion,
library_min = library_min,
nuisance_lower_quantile = nuisance_lower_quantile,
pseudocount = pseudocount)
input_obj$param <- .combine_two_named_lists(input_obj$param, param)
case_control_variable <- .get_object(eSVD_obj = input_obj, which_fit = "param", what_obj = "init_case_control_variable")
library_size_variable <- .get_object(eSVD_obj = input_obj, which_fit = "param", what_obj = "init_library_size_variable")
bool_library_includes_interept <- .get_object(eSVD_obj = input_obj, which_fit = "param", what_obj = "nuisance_bool_library_includes_interept")
res <- compute_posterior.default(
input_obj = dat,
case_control_variable = case_control_variable,
covariates = covariates,
esvd_res = esvd_res,
library_size_variable = library_size_variable,
nuisance_vec = nuisance_vec,
alpha_max = alpha_max,
bool_adjust_covariates = bool_adjust_covariates,
bool_covariates_as_library = bool_covariates_as_library,
bool_library_includes_interept = bool_library_includes_interept,
bool_return_components = bool_return_components,
bool_stabilize_underdispersion = bool_stabilize_underdispersion,
library_min = library_min,
nuisance_lower_quantile = nuisance_lower_quantile,
pseudocount = pseudocount
)
input_obj[[latest_Fit]]$posterior_mean_mat <- res$posterior_mean_mat
input_obj[[latest_Fit]]$posterior_var_mat <- res$posterior_var_mat
if(bool_return_components){
input_obj[[latest_Fit]]$numerator_mat <- res$numerator_mat
input_obj[[latest_Fit]]$denominator_mat <- res$denominator_mat
}
input_obj
}
#' Compute posterior according to Gamma-Poisson model for matrices and sparse matrices.
#'
#' @param input_obj Dataset (either \code{matrix} or \code{dgCMatrix}) where the \eqn{n} rows represent cells
#' and \eqn{p} columns represent genes.
#' The rows and columns of the matrix should be named.
#' @param case_control_variable A string of the column name of \code{covariates} which depicts the case-control
#' status of each cell. Notably, this should be a binary variable where a \code{1}
#' is hard-coded to describe case, and a \code{0} to describe control.
#' @param covariates \code{matrix} object with \eqn{n} rows with the same rownames as \code{dat} where the columns
#' represent the different covariates.
#' Notably, this should contain only numerical columns (i.e., all categorical
#' variables should have already been split into numerous indicator variables).
#' @param esvd_res Output of \code{opt_esvd.default}, notably with elements
#' \code{x_mat}, \code{y_mat} and \code{z_mat}
#' @param library_size_variable A string of the variable name (which must be in \code{covariates}) of which variable denotes the sequenced (i.e., observed) library size.
#' @param nuisance_vec Vector of non-negative numerics of length \code{ncol(input_obj)}, such as
#' the output of \code{estimate_nuisance.default}.
#' @param alpha_max Maximum value of numerator when computing posterior, default is \code{1e3}.
#' @param bool_adjust_covariates Boolean to adjust the numerator in the posterior by the donor covariates, default is \code{FALSE}.
#' This parameter is experimental, and we have not yet encountered a scenario where it is useful to be set to be \code{TRUE}.
#' @param bool_covariates_as_library Boolean to include the donor covariates effects in the adjusted library size, default is \code{TRUE}.
#' @param bool_library_includes_interept Boolean if the intercept term from the eSVD matrix factorization should be included in the calculation for the covariate-adjusted library size, default is \code{TRUE}.
#' @param bool_return_components Boolean to return the numerator and denominator of the posterior terms as well (which will themselves by matrices that are cell-by-gene matrices), default is \code{FALSE}.
#' @param bool_stabilize_underdispersion Boolean to stabilize the over-dispersion parameter, specifically to rescale all the over-dispersions the global mean over-disperion is less than 1, default is \code{TRUE}.
#' @param library_min All covariate-adjusted library size smaller than this value are set to this value, default is 0.1.
#' @param nuisance_lower_quantile All the nuisance values that are smaller than this quantile
#' are set to this quantile.
#' @param pseudocount The additional count that is added to the count matrix, default is 0.
#' @param ... Additional parameters.
#'
#' @return A \code{list} of elements \code{posterior_mean_mat}
#' and \code{posterior_var_mat}
#' @export
compute_posterior.default <- function(input_obj,
case_control_variable,
covariates,
esvd_res,
library_size_variable,
nuisance_vec,
alpha_max = 1e3,
bool_adjust_covariates = F,
bool_covariates_as_library = T,
bool_library_includes_interept = T,
bool_return_components = F,
bool_stabilize_underdispersion = T,
library_min = 0.1,
nuisance_lower_quantile = 0.01,
pseudocount = 0,
...){
stopifnot(inherits(input_obj, c("matrix", "dgCMatrix")),
is.list(esvd_res),
all(c("x_mat", "y_mat", "z_mat") %in% names(esvd_res)),
case_control_variable %in% colnames(covariates),
library_size_variable %in% colnames(covariates),
all(colnames(covariates) == colnames(esvd_res$z_mat)),
nrow(esvd_res$x_mat) == nrow(covariates),
nrow(esvd_res$y_mat) == nrow(esvd_res$z_mat),
ncol(esvd_res$x_mat) == ncol(esvd_res$y_mat),
!bool_adjust_covariates | !bool_covariates_as_library)
if(inherits(input_obj, "dgCMatrix")) input_obj <- as.matrix(input_obj)
if(!is.null(pseudocount) && pseudocount > 0) input_obj <- input_obj + pseudocount
if(is.null(case_control_variable)) case_control_variable <- numeric(0)
case_control_idx <- which(colnames(covariates) == case_control_variable)
library_size_variables <- library_size_variable
if(bool_covariates_as_library) library_size_variables <- unique(c(library_size_variables,
setdiff(colnames(covariates),
c("Intercept", case_control_variable))))
if(bool_library_includes_interept) library_size_variables <- unique(c("Intercept", library_size_variables))
library_idx <- which(colnames(covariates) %in% library_size_variables)
idx_vec <- c(case_control_idx, library_idx)
nat_mat1 <- tcrossprod(esvd_res$x_mat, esvd_res$y_mat)
nat_mat2 <- tcrossprod(covariates[,-library_idx], esvd_res$z_mat[,-library_idx])
nat_mat_nolib <- nat_mat1 + nat_mat2
mean_mat_nolib <- exp(nat_mat_nolib)
library_mat <- exp(tcrossprod(
covariates[,library_idx], esvd_res$z_mat[,library_idx]
))
if(!is.null(library_min)) library_mat <- pmax(library_mat, library_min)
nuisance_vec <- pmax(nuisance_vec,
stats::quantile(nuisance_vec, probs = nuisance_lower_quantile))
if(bool_stabilize_underdispersion & mean(log10(nuisance_vec)) > 0) {
nuisance_vec <- 10^(scale(log10(nuisance_vec), center = T, scale = F))
}
if(!is.null(alpha_max)) mean_mat_nolib <- pmin(mean_mat_nolib, alpha_max)
Alpha <- sweep(mean_mat_nolib, MARGIN = 2,
STATS = nuisance_vec, FUN = "*")
AplusAlpha <- input_obj + Alpha
# adjust the Alpha's based on the confounding covariates
if(bool_adjust_covariates){
tmp <- log(AplusAlpha)
nat_mat_confounder <- tcrossprod(covariates[,-idx_vec,drop = F],
esvd_res$z_mat[,-idx_vec,drop = F])
AplusAlpha <- exp(tmp - nat_mat_confounder)
}
SplusBeta <- sweep(library_mat, MARGIN = 2,
STATS = nuisance_vec, FUN = "+")
posterior_mean_mat <- AplusAlpha/SplusBeta
posterior_var_mat <- AplusAlpha/SplusBeta^2
rownames(posterior_mean_mat) <- rownames(input_obj)
rownames(posterior_var_mat) <- rownames(input_obj)
colnames(posterior_mean_mat) <- colnames(input_obj)
colnames(posterior_var_mat) <- colnames(input_obj)
if(!bool_return_components){
list(posterior_mean_mat = posterior_mean_mat,
posterior_var_mat = posterior_var_mat)
} else {
list(posterior_mean_mat = posterior_mean_mat,
posterior_var_mat = posterior_var_mat,
numerator_mat = AplusAlpha,
denominator_mat = SplusBeta)
}
}
.format_param_posterior <- function(alpha_max,
bool_adjust_covariates,
bool_covariates_as_library,
bool_return_components,
bool_stabilize_underdispersion,
library_min,
nuisance_lower_quantile,
pseudocount) {
list(posterior_alpha_max = alpha_max,
posterior_bool_adjust_covariates = bool_adjust_covariates,
posterior_bool_covariates_as_library = bool_covariates_as_library,
posterior_bool_return_components = bool_return_components,
posterior_bool_stabilize_underdispersion = bool_stabilize_underdispersion,
posterior_library_min = library_min,
posterior_nuisance_lower_quantile = nuisance_lower_quantile,
posterior_pseudocount = pseudocount)
}
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