#' Logistic mixture regression
#'
#' @import magrittr
#' @import stringr
#' @import flexmix
#' @import BiocParallel
#' @importFrom stats as.formula
#' @export
#'
#' @param df_samples_subset Data frame or tibble with proteins counts,
#' cell condition, and group information
#' @param protein_names A vector of column names of protein to use in the
#' analysis
#' @param condition The column name of the condition variable
#' @param group The column name of the group variable
#' @param cell_n_min Remove samples that are below this cell counts threshold
#' @param cell_n_subsample Subsample samples to have this maximum cell count
#' @param ks A vector of cluster sizes
#' @param num_cores Number of computing cores
#'
#' @return A list of class \code{cytoglm} containing
#' \item{flexmixfits}{list of \code{\link[flexmix]{flexmix}} objects}
#' \item{df_samples_subset}{possibly subsampled df_samples_subset table}
#' \item{protein_names}{input protein names}
#' \item{condition}{input condition variable}
#' \item{group}{input group names}
#' \item{cell_n_min}{input cell_n_min}
#' \item{cell_n_subsample}{input cell_n_subsample}
#' \item{ks}{input ks}
#' \item{num_cores}{input num_cores}
#'
#' @examples
#' set.seed(23)
#' df <- generate_data()
#' protein_names <- names(df)[3:12]
#' df <- dplyr::mutate_at(df, protein_names, function(x) asinh(x/5))
#' mix_fit <- CytoGLMM::cytoflexmix(df,
#' protein_names = protein_names,
#' condition = "condition",
#' group = "donor",
#' ks = 2)
#' mix_fit
cytoflexmix <- function(df_samples_subset,
protein_names,
condition,
group = "donor",
cell_n_min = Inf,
cell_n_subsample = 0,
ks = seq_len(10),
num_cores = 1) {
# some error checks
cyto_check(cell_n_subsample = cell_n_subsample,
cell_n_min = cell_n_min,
protein_names = protein_names)
# are the samples paired?
unpaired <- is_unpaired(df_samples_subset,
condition = condition,
group = group)
# remove donors with low cell count
df_samples_subset <- remove_samples(df_samples_subset,
condition = condition,
group = group,
unpaired = unpaired,
cell_n_min = cell_n_min)
# subsample cells
if(cell_n_subsample > 0) {
df_samples_subset %<>%
group_by_(group,condition) %>%
sample_n(size = cell_n_subsample) %>%
ungroup
}
# create formulas
df_samples_subset %<>% mutate_at(.vars = condition,.funs = as.factor)
df_samples_subset %<>% mutate(
xtreatment = ifelse(pull(df_samples_subset,condition) ==
levels(pull(df_samples_subset,condition))[1],
yes = 0,no = 1)
)
varying_formula <- paste0("cbind(xtreatment,1-xtreatment) ~ (",
paste(protein_names, collapse = " + "),
") | ",group)
# find best number of cluster
param <- MulticoreParam(workers = num_cores,
tasks = length(ks),
progressbar = FALSE)
flexmixfits <- bplapply(ks,
function(k) {
stepFlexmix(as.formula(varying_formula),
data = df_samples_subset,
model = FLXMRglm(family = "binomial"),
k = k,
nrep = 5)
},
BPPARAM = param)
# return cytoflexmix object
fit <- NULL
fit$flexmixfits <- flexmixfits
fit$df_samples_subset <- df_samples_subset
fit$protein_names <- protein_names
fit$condition <- condition
fit$group <- group
fit$cell_n_min <- cell_n_min
fit$cell_n_subsample <- cell_n_subsample
fit$ks <- ks
fit$num_cores <- num_cores
class(fit) <- "cytoflexmix"
fit
}
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