#' Group-specific fixed effects model
#'
#' @import magrittr
#' @importFrom stats binomial
#' @importFrom caret class2ind
#' @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
#'
#' @return A list of class \code{cytoglm} containing
#' \item{groupfit}{\code{\link[glm]{glm}} object}
#' \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}
#'
#' @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))
#' group_fit <- CytoGLMM::cytogroup(df,
#' protein_names = protein_names,
#' condition = "condition",
#' group = "donor")
#' group_fit
cytogroup <- function(df_samples_subset,
protein_names,
condition,
group = "donor",
cell_n_min = Inf,
cell_n_subsample = 0) {
# 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 formula
df_samples_subset %<>% mutate_at(.vars = group,.funs = as.factor)
donor_dummy <- class2ind(pull(df_samples_subset,group))
colnames(donor_dummy) <- paste0("X",colnames(donor_dummy))
df_samples_subset %<>% bind_cols(as_tibble(donor_dummy))
pnames <- paste(protein_names, collapse = " + ")
dnames <- paste(colnames(donor_dummy), collapse = " + ")
formula_str <- paste0(condition," ~ (",pnames,") * (",dnames,")")
# logistic regression
groupfit <- glm(formula = formula_str,
family = binomial(),
data = df_samples_subset)
# return cytoglmm object
fit <- NULL
fit$groupfit <- groupfit
fit$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
class(fit) <- "cytogroup"
fit
}
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