# Translocations dose-effect fitting

## Calculate genomic conversion factor

To be able to perform fit the equivalent full genome dose-effect curve, we need to calculate the genomic conversion factor.

To do this, in the “Stain color options” box we select the sex of the individual, and the list of chromosomes and stains used for the translocation assay. Clicking on “Generate table” will show a table in the “Chromosome data” box in which we select the chromosome-stain pairs. Clicking on the “Calculate fraction” will calculate the genomic conversion factor.

To calculate the genomic conversion factor in R we call the calculate_genome_factor() function:

genome_factor <- calculate_genome_factor(
dna_table = dna_content_fractions_morton,
chromosome = c(1, 4, 11),
color = rep("Red", 3),
sex = "female"
)
genome_factor
#> [1] 0.3147797

## Input count data

Once the genomic conversion factor has been calculated, we can input the count data. On the app, we can select to either load the count data from a file (supported formats are .csv, .dat, and .txt) or to input the data manually. Once the table is generated and filled, the “Calculate parameters” button will calculate the number of cells ($$N$$), number of aberrations ($$X$$), as well as mean ($$\bar{y}$$), variance ($$\hat{\sigma}^{2}$$), dispersion index ($$\hat{\sigma}^{2}/\bar{y}$$), and $$u$$-value.

This step is accomplished in R by calling the calculate_aberr_table() function:

count_data <- system.file("extdata", "count-data-rodriguez-2004.csv",
package = "biodosetools") %>%
calculate_aberr_table(type = "count")
count_data
#> # A tibble: 11 × 13
#>        D     N     X    C0    C1    C2    C3    C4    C5    mean     var    DI
#>    <dbl> <int> <dbl> <int> <int> <int> <int> <int> <int>   <dbl>   <dbl> <dbl>
#>  1  0     4356     6  4350     6     0     0     0     0 0.00138 0.00138 0.999
#>  2  0.1   3324    15  3309    15     0     0     0     0 0.00451 0.00449 0.996
#>  3  0.25  3069    18  3051    18     0     0     0     0 0.00587 0.00583 0.994
#>  4  0.5   3072    33  3039    33     0     0     0     0 0.0107  0.0106  0.990
#>  5  0.75  2111    40  2072    38     1     0     0     0 0.0189  0.0195  1.03
#>  6  1     2124    50  2075    48     1     0     0     0 0.0235  0.0239  1.02
#>  7  1.5   1043    56   990    50     3     0     0     0 0.0537  0.0566  1.05
#>  8  2      718    71   650    65     3     0     0     0 0.0989  0.0976  0.987
#>  9  3      781   157   649   108    23     1     0     0 0.201   0.227   1.13
#> 10  4      397   147   265   117    15     0     0     0 0.370   0.310   0.836
#> 11  5      395   180   246   122    23     4     0     0 0.456   0.426   0.936
#> # … with 1 more variable: u <dbl>

## Perform fitting

To perform the fitting the user needs select the appropriate fitting options to click the “Calculate fitting” button on the “Data input” box. The fit can be done either using the full genome translocations, or those measured by FISH. This will not impact any future dose estimation, as the results internally use the full genome translocations.

The fitting results and summary statistics are shown in the “Results” tabbed box, and the dose-effect curve is displayed in the “Curve plot” box.

The “Export results” box shows two buttons: (a) “Save fitting data”, and (b) “Download report”. The “Save fitting data” will generate an .rds file that contains all information about the count data, irradiation conditions, and options selected when performing the fitting. This file can be then loaded in the dose estimation module to load the dose-effect curve coefficients.

Similarly, the “Download report” will generate a .pdf or a .docx report containing all inputs and fitting results.

To perform the fitting in R we call the fit() function:

fit_results <- fit(
count_data = count_data %>%
dplyr::mutate(N = N * genome_factor),
model_family = "automatic",
aberr_module = "translocations"
)

The fit_results object is a list that contains all necessary information about the count data as well as options selected when performing the fitting. This is a vital step to ensure traceability and reproducibility. Below we can see its elements:

names(fit_results)
#>  [1] "fit_raw_data"         "fit_formula_raw"      "fit_formula_tex"
#>  [4] "fit_coeffs"           "fit_cor_mat"          "fit_var_cov_mat"
#>  [7] "fit_dispersion"       "fit_model_statistics" "fit_algorithm"
#> [10] "fit_model_summary"

In particular, we can see how fit_coeffs matches the results obtained in the UI:

fit_results\$fit_coeffs
#>                estimate   std.error statistic      p.value
#> coeff_C     0.006560406 0.002052834  3.195780 2.538519e-02
#> coeff_alpha 0.027197296 0.009922177  2.741061 5.081502e-02
#> coeff_beta  0.057982322 0.004630926 12.520675 3.100113e-06

To visualise the dose-effect curve, we call the plot_fit_dose_curve() function:

plot_fit_dose_curve(
fit_results,
aberr_name = "Translocations"
)