R/plate_summary.R

In cgrlab/TypeSeqHPV: TypeSeqHPV Analysis Functions and Workflows

#' plate_summary
#'

plate_summary <- function(split_deliverables){

    require(pander)

    controls_df = split_deliverables$control_matrix %>%
        group_by(PreExtraction_Plate_ID, Assay_Plate_Code, Control_type, control_result) %>%
        summarize(count = n()) %>%
        ungroup() %>%
        mutate(control_result = paste0(Control_type, "_", control_result)) %>%
        select(-Control_type) %>%
        bind_rows(
            data_frame(PreExtraction_Plate_ID = "temp",
                       control_result = c("pos_pass", "pos_fail",
                                          "neg_pass", "neg_fail"),
                       count = c(0,0,0,0))) %>%
        spread(control_result, count, fill = 0) %>%
        filter(PreExtraction_Plate_ID != "temp") %>%
        mutate(total_controls = neg_fail + neg_pass + pos_fail + pos_pass) %>%
        select(Assay_Plate_Code, pos_fail, neg_fail, total_controls)

    samples_and_controls_df = split_deliverables$samples_only_matrix %>%
        mutate(has_positive = ifelse(Num_Types_Pos == 0,0, 1)) %>%
        group_by(PreExtraction_Plate_ID, Assay_Plate_Code) %>%
        mutate(number_of_samples = n()) %>%
        mutate(plate_total_reads = sum(total_reads, na.rm = TRUE)) %>%
        mutate(plate_b2m_reads = sum(B2M, na.rm = TRUE)) %>%
        mutate(sample_status_for_count = ifelse(Human_Control == "failed_to_amplify", 1, 0)) %>%
        mutate(hpv_pos_rate = sum(has_positive) / number_of_samples) %>%
        mutate(num_samples_failed = sum(sample_status_for_count)) %>%
        select(PreExtraction_Plate_ID, Assay_Plate_Code, number_of_samples, plate_total_reads,
               plate_b2m_reads, hpv_pos_rate, num_samples_failed) %>%
        distinct() %>%
        mutate(hpv_pos_perc = scales::percent(hpv_pos_rate)) %>%
        mutate(perc_b2m_reads = scales::percent(plate_b2m_reads / plate_total_reads)) %>%
        mutate(plate_b2m_reads = scales::comma(plate_b2m_reads)) %>%
        mutate(plate_total_reads = scales::comma(plate_total_reads)) %>%
        select(-hpv_pos_rate) %>%
        left_join(controls_df, by = "Assay_Plate_Code") %>%
        arrange(PreExtraction_Plate_ID, Assay_Plate_Code)

    panderOptions("table.split.cells", 5)

    samples_and_controls_df %>%
        select(`PreExtraction plate ID` = PreExtraction_Plate_ID,
               `Assay Plate Code` = Assay_Plate_Code,
               `HPV % Positive` = hpv_pos_perc,
               `# neg controls failed` = neg_fail,
               `# pos controls failed` = pos_fail,
               `# samples failed` = num_samples_failed,
        ) %>%

        pandoc.table(style = "multiline",
                     caption = "Assay Plate Performance")

    samples_and_controls_df %>%
        select(`PreExtraction plate ID` = PreExtraction_Plate_ID,
               `Assay Plate Code` = Assay_Plate_Code,
               `total reads` = plate_total_reads,
               `total B2M reads` = plate_b2m_reads,
               `% B2M reads` = perc_b2m_reads,
               `# samples total` = number_of_samples,
               `# total controls` = total_controls
        ) %>%

        pandoc.table(style = "multiline")

}