To be able to 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:

```
<- calculate_genome_factor(
genome_factor dna_table = dna_content_fractions_morton,
chromosome = c(1, 4, 11),
color = rep("Red", 3),
sex = "female"
)
```

```
genome_factor#> [1] 0.3147797
```

Once the genomic conversion factor has been calculated, we can input
the count data. On the {shiny} 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 total number of cells (\(N\)), total number of aberrations (\(X\)), as well as mean (\(\bar{y}\)), variance (\(\sigma^{2}\)), dispersion index (\(\sigma^{2}/\bar{y}\)), and \(u\)-value.

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

function:

```
<- system.file("extdata", "count-data-rodriguez-2004.csv",
count_data package = "biodosetools") %>%
::read.csv() %>%
utilscalculate_aberr_table(type = "count") %>%
::mutate(N = N * genome_factor) dplyr
```

```
count_data#> # A tibble: 11 × 13
#> D N X C0 C1 C2 C3 C4 C5 mean var DI
#> <dbl> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 0 1371. 6 4350 6 0 0 0 0 0.00138 0.00138 0.999
#> 2 0.1 1046. 15 3309 15 0 0 0 0 0.00451 0.00449 0.996
#> 3 0.25 966. 18 3051 18 0 0 0 0 0.00587 0.00583 0.994
#> 4 0.5 967. 33 3039 33 0 0 0 0 0.0107 0.0106 0.990
#> 5 0.75 664. 40 2072 38 1 0 0 0 0.0189 0.0195 1.03
#> 6 1 669. 50 2075 48 1 0 0 0 0.0235 0.0239 1.02
#> 7 1.5 328. 56 990 50 3 0 0 0 0.0537 0.0566 1.05
#> 8 2 226. 71 650 65 3 0 0 0 0.0989 0.0976 0.987
#> 9 3 246. 157 649 108 23 1 0 0 0.201 0.227 1.13
#> 10 4 125. 147 265 117 15 0 0 0 0.370 0.310 0.836
#> 11 5 124. 180 246 122 23 4 0 0 0.456 0.426 0.936
#> # … with 1 more variable: u <dbl>
```

To perform the fitting the user needs to 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(
fit_results count_data = count_data,
model_formula = "lin-quad",
model_family = "automatic",
fit_link = "identity",
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_coeffs
fit_results#> 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"
)
```