#' Filter and save markers
#'
#' Filters marker genes based on padj and k and saves them as a csv file
#' @param sce_object A single cell experiment object
#' @param k number of clusters
#' @param padj Adjusted p value
#' @param auroc Area Under Receiving Operating Curve
#' @param folder Folder where to export the file to
#' @param filename Name for exported csv file
#' @return csv file
#' @export
export_markers <- function(sce_object, k, padj = 0.01, auroc = 0.85, folder, filename) {
#require(c("SC3", "lazyeval", "dplyr", "rlang"))
k_clusts <- paste0("sc3_", k, "_markers_clusts", sep = "")
k_auroc <- paste0("sc3_", k, "_markers_auroc", sep = "")
k_padj <- paste0("sc3_", k, "_markers_padj", sep = "")
#sc3_plot_markers(sce_object, k = k, show_pdata = TRUE)
sce_object %>%
rowData() %>%
as_tibble() %>%
#group_by_(k_clusts) %>%
filter_(interp(~ a < x & b > y,
a = as.name(k_padj),
b = as.name(k_auroc),
x = padj,
y = auroc)) %>%
select_("mgi_symbol", "ensembl_gene_id", "log10_mean_counts", k_auroc, k_clusts, k_padj) %>%
arrange_(.dots = c(k_clusts, k_padj)) %>%
write.csv(paste0(folder, "/", filename, ".csv"))
}
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