get_results_for_dyes: Extract dye results from a data frame with detector results.

In flowQB: Automated Quadratic Characterization of Flow Cytometer Instrument Sensitivity: Q, B and CV instrinsic calculations

Description

This function takes a data frame where columns are named based on detectors and extracts a subset of the data frame it by selecting only specified detectors. In addition, the columns will be renamed based on the specified dyes argument.

Usage

    get_results_for_dyes(dyes, detectors, results)

Arguments

dyes	A vector of n dye names which shall correspond to the dyes specified in the dyes argument. These will be the column names of the resulting data frame. The detector-dye mapping is done based on the order of values in the two vectors, i.e., the first dye shall correspond to the first detector, etc.
detectors	A vector of n detector names which shall correspond to the dyes specified in the dyes argument. These shall correspond to the column names in the input data frame. The detector-dye mapping is done based on the order of values in the two vectors, i.e., the first dye shall correspond to the first detector, etc.
results	An input data frame that shall contain columns corresponding to all the different values specified by the detectors vector.

Details

This function is used to select a subset of columns from a data frame by specifying the columns of interest (detectors). In addition, the columns will be renamed to dyes corresponding to those detectors.

Value

A data frame with n columns, column names corresponding to the specified dyes and rows/values extracted from the input data frame.

Author(s)

Wayne Moore, Faysal El Khettabi, Josef Spidlen

See Also

calc_mean_sd_duke

Examples

    library(flowCore)
    library(xlsx)
    library(flowQBData)

    inst_xlsx_path <- system.file("extdata", 
        "140126_InstEval_Stanford_LSRIIA2.xlsx", package="flowQBData")
    xlsx <- read.xlsx(inst_xlsx_path, 1, headers=FALSE, stringsAsFactors=FALSE)
    
    ignore_channels_row <- 9
    ignore_channels <- vector()
    i <- 1
    while(!is.na(xlsx[[i+4]][[ignore_channels_row]])) {
        ignore_channels[[i]] <- xlsx[[i+4]][[ignore_channels_row]]
        i <- i + 1
    }
    
    instrument_folder_row <- 9
    instrument_folder_col <- 2
    instrument_folder <- xlsx[[instrument_folder_col]][[instrument_folder_row]]

    test_column <- 13
    test_row <- 14
    folder <- xlsx[[test_column]][[test_row]]
    beads_file_name <- xlsx[[test_column]][[test_row+1]]
    scatter_channels <- c(
        xlsx[[test_column]][[test_row+2]], 
        xlsx[[test_column]][[test_row+3]])

    fcs_path <- system.file("extdata",
        instrument_folder, folder, beads_file_name, package="flowQBData")

    results <- calc_mean_sd_duke(fcs_path, scatter_channels, ignore_channels)
    
    channel_cols <- 3:12
    dye_row <- 11
    detector_row <- 13
    dyes <- as.character(xlsx[dye_row,channel_cols])
    detectors <- as.character(xlsx[detector_row,channel_cols])
    dye_results <- get_results_for_dyes(dyes, detectors, results)

flowQB documentation built on May 6, 2019, 3:05 a.m.