R/plot_FMOs.R

In aef1004/cytotypr: Pipeline to Identify Cell Types from Cytometry Data

#' Plot all of the FMOs and their cutoffs
#'
#' @param df1 the dataframe that contains the filtered FMO values (only the FMO file that matches up with it's marker)
#' @param df2 the dataframe that contains the quantile values
#'
#' @return plots that shows data for each of the FMOs and where the cutoff is placed
#' @export
#'
#' @examples plot_FMOs(FMO_filtered_data, add_quantile)
#'
#' @importFrom rlang .data
#'
plot_FMOs <- function(df1, df2) {

  right_quantile <- dplyr::full_join(df1, df2, by = "filename")

  ggplot2::ggplot(right_quantile, ggplot2::aes(x = .data$MFI, y = .data$`SSC-A`)) +
    ggplot2::geom_hex(bins = 300, na.rm = TRUE) +
    ggplot2::scale_fill_viridis_c() +
    ggplot2::facet_wrap(~ .data$marker, nrow = 3) +
    ggplot2::ylab("") +
    ggplot2::geom_vline(mapping = ggplot2::aes(xintercept = .data$quantile_99)) +
    ggplot2::theme_gray() +
    ggplot2::theme(axis.text = ggplot2::element_text(size =12),
          axis.title = ggplot2::element_text(size = 20),
          strip.text = ggplot2::element_text(size = 12))+
    ggplot2::scale_x_continuous(labels = scales::scientific, limits = c(-10000, 20000), breaks = c(0, 50000))

}