#' progressively remove max IQR sample and recalculate
#'
#' @param sample_set the metadata
#' @param logged_norm_counts counts on log2 scale and normalized
#'
#'@export
removeOneRedoIqr = function(sample_set, logged_norm_counts){
#TODO re-write this with new rle functions
removed_sample_list = list()
# recalculated_iqr_list = list()
# i = nrow(sample_set)
#
# while (i > 3){
# max_iqr = max(sample_set$INTERQUARTILE_RANGE)
# max_iqr_boolean_vector = sample_set$INTERQUARTILE_RANGE == max_iqr
# sample_number = as.character(sample_set[max_iqr_boolean_vector, "FASTQFILENUMBER"])
#
# removed_sample_list[[sample_number]] = max_iqr
#
# sample_set = sample_set[!max_iqr_boolean_vector, ]
#
# sample_columns = sample_set$FASTQFILENAME
# max_iqr_removed_log_norm_counts = logged_norm_counts[,sample_columns]
#
# #rle_stats_df = rleSummary(createFullRLETable(max_iqr_removed_log_norm_counts, logged=TRUE))
# recalculated_iqr_list[[sample_number]] = rle_stats_df
#
# i = i-1
# }
# return (list("removed_samples" = removed_sample_list, "df_list" = recalculated_iqr_list))
## a recursive version -- each step works, and if you put a print statement after the df calculation then the output is correct, but the lists aren't being returned
# if(nrow(sample_set) > 4){
# max_iqr = max(sample_set$INTERQUARTILE_RANGE)
# max_iqr_boolean_vector = sample_set$INTERQUARTILE_RANGE == max_iqr
# sample_number = as.character(sample_set[max_iqr_boolean_vector, "FASTQFILENUMBER"])
#
# x[[sample_number]] = max_iqr
#
# sample_set = sample_set[!max_iqr_boolean_vector, ]
#
# sample_columns = sample_set$FASTQFILENAME
# max_iqr_removed_log_norm_counts = logged_norm_counts[,sample_columns]
#
# rle_stats_df = rleSummary(createFullRLETable(max_iqr_removed_log_norm_counts, logged=TRUE))
# y[[sample_number]] = rle_stats_df
#
# removeOneRedoIqr(sample_set, logged_norm_counts, x, y)
# } # end if
}
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