library(dplyr) library(ggplot2)
aberr_module <- params$aberr_module parsed_title <- dplyr::case_when( aberr_module == "dicentrics" ~ "Dicentrics dose estimation report", aberr_module == "translocations" ~ "Translocation FISH dose estimation report", aberr_module == "micronuclei" ~ "Micronuclei dose estimation report" )
r parsed_title
pander::panderOptions("table.style", "grid") pander::panderOptions("table.split.table", Inf) pander::panderOptions( "table.alignment.default", function(df) { ifelse(sapply(df, is.numeric), "center", "left") } ) render_table <- function(x, ...) { pander::pander(x) # TODO: handle alignments via justify parameter }
# General parameters assessment <- params$est_results_list[["assessment"]] est_doses_whole <- params$est_results_list[["est_doses_whole"]] est_doses_partial <- params$est_results_list[["est_doses_partial"]] est_frac_partial <- params$est_results_list[["est_frac_partial"]] est_mixing_prop_hetero <- params$est_results_list[["est_mixing_prop_hetero"]] est_yields_hetero <- params$est_results_list[["est_yields_hetero"]] est_doses_hetero <- params$est_results_list[["est_doses_hetero"]] est_frac_hetero <- params$est_results_list[["est_frac_hetero"]] AIC_whole <- params$est_results_list[["AIC_whole"]] AIC_partial <- params$est_results_list[["AIC_partial"]] AIC_hetero <- params$est_results_list[["AIC_hetero"]] fit_coeffs <- params$est_results_list[["fit_coeffs"]] fit_formula_tex <- params$est_results_list[["fit_formula_tex"]] protraction <- params$est_results_list[["protraction"]] case_data <- params$est_results_list[["case_data"]] case_description <- params$est_results_list[["case_description"]] results_comments <- params$est_results_list[["results_comments"]] gg_curve <- params$est_results_list[["gg_curve"]] # Translocations genome_factor <- params$est_results_list[["genome_factor"]] chromosome_table <- params$est_results_list[["chromosome_table"]] trans_sex <- params$est_results_list[["trans_sex"]] confounders <- params$est_results_list[["confounders"]]
r paste0("$$", fit_formula_tex, if (protraction[1]) "G(x)", "$$")
r if (protraction[1]) {
paste(
"where $G(x)$ is a time-dependent correction that takes into account protracted and fractionated exposures:",
"$$", "G(x) = \\frac{2}{x^{2}} \\left(x - 1 - e^{-x} \\right), \\quad x = \\frac{t}{t_{0}}", "$$"
)
}
r if (protraction[1]) {
paste0(
"The protraction times used for the dose estimation were ",
paste0("$", "t =", protraction[2]), "\\, \\textrm{h}", "$",
" and ",
paste0("$", "t_{0} =", protraction[3]), "\\, \\textrm{h}", "$",
"."
)
}
r if (aberr_module == "translocations") {"## Full genome coefficients"} else {"## Coefficients"}
fit_coeffs %>% formatC(format = "e", digits = 3) %>% as.data.frame() %>% biodosetools:::fix_coeff_names(type = "rows", output = "kable") %>% render_table(align = "c")
r if (aberr_module == "translocations") {"# Case chromosome data"}
r if (aberr_module == "translocations") {
paste("The analysed blood sample comes from a", trans_sex, "individual.")
}
if (aberr_module == "translocations") { num_cols <- as.numeric(ncol(chromosome_table)) chromosome_table %>% dplyr::mutate( dplyr::across( .cols = dplyr::everything(), .fns = function(x) { x <- ifelse(is.na(x) | x == "FALSE", "", x) x <- ifelse(x == "TRUE", "$\\checkmark$", x) return(x) } ) ) %>% render_table(align = "c") }
r if (aberr_module == "translocations") {
if (num_cols == 1) {
"where each chromosome was stained using M-FISH."
}
}
r if (aberr_module == "translocations") {"## Genomic conversion factor"}
r if (aberr_module == "translocations") {
paste0("The genomic conversion factor to full genome is ", as.character(round(genome_factor, 3)), ".")
}
data <- case_data %>% as.matrix() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% dplyr::mutate( dplyr::across( .cols = c("N", "X", dplyr::starts_with("C")), .fns = as.integer ) ) %>% biodosetools:::fix_count_data_names(type = "case", output = "kable") data$`$u$` <- kableExtra::cell_spec( x = data$`$u$`, background = ifelse(data$`$u$` > 1.96, "#ffc0cb", "white") ) data %>% render_table(align = "c")
r if (aberr_module == "translocations") {"## Confounders"}
r if (aberr_module == "translocations") {
if (is.null(confounders)) {
paste("No confounders were selected.")
} else if (!is.null(confounders) & length(confounders) == 1) {
paste0("To account for confounders, the following translocation frequency per cell was manually input: ", confounders, ".")
} else if (!is.null(confounders) & length(confounders) > 1) {
paste("The following confounders were selected:")
}
}
if (aberr_module == "translocations") { if (!is.null(confounders) & length(confounders) > 1) { confounders %>% as.matrix() %>% t() %>% as.data.frame() %>% mutate( sex_bool = with(., case_when( (sex_bool == TRUE) ~ "Used", (sex_bool == FALSE) ~ "Not used" )), smoker_bool = with(., case_when( (smoker_bool == TRUE) ~ "Yes", (smoker_bool == FALSE) ~ "No" )), ethnicity_value = with(., case_when( (ethnicity_value == "white") ~ "White", (ethnicity_value == "asian") ~ "Asian", (ethnicity_value == "black") ~ "Black", (ethnicity_value == "other") ~ "Others", TRUE ~ "Not specified" )), region_value = with(., case_when( (region_value == "n-america") ~ "North America", (region_value == "w-europe") ~ "Western Europe", (region_value == "c-europe") ~ "Central Europe", (region_value == "e-europe") ~ "Eastern Europe", (region_value == "asia") ~ "Asia", TRUE ~ "Not specified" )) ) %>% `colnames<-`(c("Age", "Sex", "Smoker", "Ethnicity", "Lab region")) %>% render_table(align = "c") } }
r case_description
r if (TRUE) {"## Whole-body exposure results"}
r if (TRUE) {"### Whole-body exposure estimation {-}"}
if (TRUE) { est_doses_whole %>% dplyr::select(yield) %>% t() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Convert to hot and format table render_table(align = "c") }
if (TRUE) { est_doses_whole %>% dplyr::select(`dose (Gy)` = dose) %>% t() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Convert to hot and format table render_table(align = "c") }
r if (assessment == "partial-body") {"## Partial-body exposure results"}
r if (assessment == "partial-body") {"### Partial-body exposure estimation {-}"}
if (assessment == "partial-body") { est_doses_partial %>% dplyr::select(yield) %>% t() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Fix possible NA values dplyr::mutate_if(is.logical, as.double) %>% `colnames<-`(c("lower", "estimate", "upper")) %>% `row.names<-`("yield") %>% # Convert to hot and format table render_table(align = "c") }
if (assessment == "partial-body") { est_doses_partial %>% dplyr::select(dose) %>% t() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Fix possible NA values dplyr::mutate_if(is.logical, as.double) %>% `colnames<-`(c("lower", "estimate", "upper")) %>% `row.names<-`("dose (Gy)") %>% # Convert to hot and format table render_table(align = "c") }
r if (assessment == "partial-body") {"### Initial fraction of irradiated cells {-}"}
if (assessment == "partial-body") { est_frac_partial %>% t() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Fix possible NA values dplyr::mutate_if(is.logical, as.double) %>% `colnames<-`(c("lower", "estimate", "upper")) %>% `row.names<-`("fraction") %>% # Convert to hot and format table render_table(align = "c") }
r if (assessment == "hetero") {"## Heterogeneous exposure results"}
r if (assessment == "hetero") {"### Observed fraction of irradiated cells and its yield {-}"}
if (assessment == "hetero") { est_mixing_prop_hetero %>% as.matrix() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Fix possible NA values dplyr::mutate_if(is.logical, as.double) %>% `colnames<-`(c("yield", "yield.err", "frac", "frac.err")) %>% `row.names<-`(c("dose1", "dose2")) %>% # Convert to hot and format table render_table(align = "c") }
r if (assessment == "hetero") {"### Heterogeneous exposure estimation {-}"}
if (assessment == "hetero") { est_yields_hetero %>% t() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Fix possible NA values dplyr::mutate_if(is.logical, as.double) %>% `colnames<-`(c("lower", "estimate", "upper")) %>% `row.names<-`(c("yield1", "yield2")) %>% # Convert to hot and format table render_table(align = "c") }
if (assessment == "hetero") { est_doses_hetero %>% t() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% # Fix possible NA values dplyr::mutate_if(is.logical, as.double) %>% `colnames<-`(c("lower", "estimate", "upper")) %>% `row.names<-`(c("dose1 (Gy)", "dose2 (Gy)")) %>% # Convert to hot and format table render_table(align = "c") }
r if (assessment == "hetero") {"### Initial fraction of irradiated cells {-}"}
if (assessment == "hetero") { est_frac_hetero %>% # Fix possible NA values as.matrix() %>% formatC(format = "f", digits = 3) %>% as.data.frame() %>% dplyr::mutate_if(is.logical, as.double) %>% `colnames<-`(c("estimate", "std.err")) %>% `row.names<-`(c("dose1", "dose2")) %>% # Convert to hot and format table render_table(align = "c") }
gg_curve
r results_comments
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