R/pnpp_experiment-reclassify.R

Defines functions reclassify_droplets_single.pnpp_experiment reclassify_droplets_single reclassify_droplets.pnpp_experiment reclassify_droplets

Documented in reclassify_droplets reclassify_droplets.pnpp_experiment reclassify_droplets_single reclassify_droplets_single.pnpp_experiment

## ddpcr - R package for analysis of droplet digital PCR data
## Copyright (C) 2015 Dean Attali

#' Analysis step: Reclassify droplets
#' 
#' After classifying droplets into clusters in each well individually, it may
#' be useful to attempt to reclassify droplets in wells where the gates are
#' not very clearly defined. This function uses information taken from wells
#' with a high negative frquency (where the gate is easily defined) to adjust
#' the gates in the other wells.\cr\cr
#' \href{https://github.com/daattali/ddpcr#advanced-topic-2-algorithms-used-in-each-step}{See the README} for
#' more information about the algorithm used.
#' 
#' This function is recommended to be run as part of an analysis pipeline (ie.
#' within the \code{\link[ddpcr]{analyze}} function) rather than being called
#' directly.
#' @seealso \code{\link[ddpcr]{analyze}}\cr
#' \code{\link[ddpcr]{reclassify_droplets_single}}\cr
#' \code{\link[ddpcr]{mark_clusters}}
#' 
#' @note This is an S3 generic, which means that different ddPCR plate types can
#' implement this function differently. 
#' \href{https://github.com/daattali/ddpcr#advanced-topic-3-creating-new-plate-types}{See the README} for
#' more information on how to implement custom ddPCR plate types.
#' @export
#' @keywords internal
reclassify_droplets <- function(plate) {
  UseMethod("reclassify_droplets")
}

#' Analysis step: Reclassify droplets
#' @inheritParams reclassify_droplets
#' @export
#' @keywords internal
reclassify_droplets.pnpp_experiment <- function(plate) {
  CURRENT_STEP <- plate %>% step('RECLASSIFY')
  plate %>% check_step(CURRENT_STEP)
  
  if (length(wells_success(plate)) > 0) {
    
    # if there are not enough wells with high MT freq to use as prior info or if
    # there are no wells with low MT freq to reclassify, do nothing
    min_wells <- params(plate, 'RECLASSIFY', 'MIN_WELLS_NEGATIVE_CLUSTER')
    if (plate %>% wells_negative %>% length < min_wells ||
        plate %>% wells_positive %>% length == 0) {
      msg(paste0("Reclassifying droplets... skipped (not enough",
                     " wells with significant ",
                     negative_name(plate),
                     " clusters)"))
      status(plate) <- CURRENT_STEP
      return(plate)
    }
    
    step_begin("Reclassify droplets based on info in all wells")
    
    data <- plate_data(plate)
    CLUSTER_NEGATIVE <- plate %>% cluster('NEGATIVE')
    CLUSTER_POSITIVE <- plate %>% cluster('POSITIVE')
    
    # calculate the ratio of the highest MT drop over the median WT drop
    variable_var <- variable_dim_var(plate)
    consensus_border_ratio <-
      vapply(plate %>% wells_negative,
             function(x) {
               negative_max <- 
                 data %>%
                 dplyr::filter_(~ well == x,
                                ~ cluster == CLUSTER_NEGATIVE) %>%
                 .[[variable_var]] %>%
                 max
               positive_median <- 
                 data %>%
                 dplyr::filter_(~ well == x,
                                ~ cluster == CLUSTER_POSITIVE) %>%
                 .[[variable_var]] %>%
                 stats::median()               
               ratio <- negative_max / positive_median
               ratio
             },
             numeric(1)
      ) %>%
      stats::quantile(params(plate, 'RECLASSIFY', 'BORDER_RATIO_QUANTILE')) %>%
      as.numeric()
    
    wells_to_reclassify <- plate %>% wells_positive
    
    well_clusters_info <-
      vapply(wells_to_reclassify,
             function(x) reclassify_droplets_single(plate, x, consensus_border_ratio = consensus_border_ratio),
             integer(1)) %>%
      named_vec_to_df(meta_var_name(plate, "negative_border"))
    
    # add metadata to each well
    plate_meta(plate) %<>%
      merge_dfs_overwrite_col(well_clusters_info)  
    
    plate %<>%
      mark_clusters(wells_to_reclassify) %>%
      calculate_negative_freqs
  }
  
  # ---
  
  status(plate) <- CURRENT_STEP
  step_end()
  
  plate
}

#' Reclassify droplets in a well 
#' @keywords internal
#' @export
reclassify_droplets_single <- function(plate, well_id, ...) {
  UseMethod("reclassify_droplets_single")
}

#' Reclassify droplets in a well 
#' 
#' Reclassify droplets in a well given the ratio of where to place the MT border
#' over the median WT drops
#' @keywords internal
#' @export
reclassify_droplets_single.pnpp_experiment <- function(plate, well_id, ..., consensus_border_ratio) {
  
  well_data <- get_single_well(plate, well_id, clusters = TRUE)
  variable_var <- variable_dim_var(plate)
  
  # find the median WT drops
  CLUSTER_POSITIVE <- plate %>% cluster('POSITIVE')
  positive_median <- 
    well_data %>%
    dplyr::filter_(~ cluster == CLUSTER_POSITIVE) %>%
    .[[variable_var]] %>%
    stats::median()
  
  # determine the negative border based on the median WT drops and the given ratio
  negative_border <- (consensus_border_ratio * positive_median) %>% as.integer
  
  negative_border
}

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ddpcr documentation built on June 24, 2017, 9:01 a.m.