This vignette will show how to save a toxpiR model that will be compatible with the ToxPi Java GUI, which can be downloaded from here. The toxpiR package and ToxPi Java GUI are not directly compatible and there are several key differences to keep in mind.
Key differences between Java GUI and toxpiR
Slice weights
The Java GUI only allows weights that are either integers or a ratio
of integers whereas the toxpiR package has no restrictions. The
txpExportGui() function requires all weights to be
integers, so the user may need to change the model weights to acceptable
approximations prior to calling the export function.
Transformation/scaling functions
Currently the Java GUI only allows specific scaling functions and applies them independently to every input within a slice. The toxpiR package allows user-defined transformation functions at the input-level and slice-level. To account for these differences, all input-level transformation functions are applied before the data is exported. If slice-level transformations are applied, then the export function will create a data file that has the final slice scores rather than input-level data.
The Java GUI does not allow negative input values and will treat them a missing data. This causes a problem if negative values exist after applying any user-defined transformations. If negative values occur within a slice, then all values of that slice will be shifted up by a constant so that no negative values remain. If a slice has both negative transformed values and missing values, then missing values are replaced with the added constant. In this last case, the toxpi and slice scores will be computed correctly, however, the Java GUI should not be used to compute bootstrapped confidence intervals because replacing missing data during the export process will cause the resampling step to be incorrect.
Example use
First create a toxpiR model with accompanying data. Here we’ll load
the “Format C” data example using the txpImportGui()
function.
library(toxpiR)
# Load example model from "Import ToxPi GUI Files" vignette
data_format_C <- tempfile()
download.file(
url = "https://raw.githubusercontent.com/ToxPi/ToxPi-example-files/main/format_C.csv",
destfile = data_format_C,
quiet = TRUE
)
gui1 <- txpImportGui(data_format_C)
#> Warning in method(object): The following 'input' columns are duplicated in the model:
#> metric3, metric2, metric3, metric1, metric2Now we can use to export function to create a new data file. Notice the warnings for negative and missing values.
# Export back into GUI format
data_exported <- tempfile()
txpExportGui(
fileName = data_exported,
input = gui1$input,
model = gui1$model,
id.var = 'Name',
fills = gui1$fills
)
#> Warning in txpExportGui(fileName = data_exported, input = gui1$input, model =
#> gui1$model, : Slice "Slice2" contains negative values after applying
#> transformations so all values were increased by x = 5.
#> Warning in txpExportGui(fileName = data_exported, input = gui1$input, model =
#> gui1$model, : Slice "Slice3" contains both missing and negative values after
#> applying transformations so missing values were replaced with 0 and then all
#> values were increased by x = 5.Compare the data files
Take a moment to observe differences between the original data file
(data_format_C) and the exported version
(data_exported).
data_format_C
| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 |
|---|---|---|---|---|---|---|---|
| # Slice1!4!0xFF69B4!-log10(x)+6 | x | x | |||||
| # Slice2!4!0x6959CD!-ln(x) | x | ||||||
| # Slice3!4!0xCDC1C5!-ln(x) | x | x | x | ||||
| # Slice4!5!0xFF6347!-log10(x)+6 | x | x | x | ||||
| Row | Source | CASRN | Name | metric1 | metric2 | metric3 | metric4 |
| 1 | source01 | 11-111-1111 | chem01 | 25 | 91 | NA | NA |
| 2 | source02 | 22-222-2222 | chem02 | NA | 46 | 51 | 48 |
| 3 | source03 | 33-333-3333 | chem03 | 44 | NA | 9 | 34 |
| 4 | source04 | 44-444-4444 | chem04 | 26 | 64 | 27 | 9 |
| 5 | source05 | 55-555-5555 | chem05 | 33 | 36 | 69 | 88 |
| 6 | source06 | 66-666-6666 | chem06 | 94 | 46 | NA | 54 |
| 7 | source07 | 77-777-7777 | chem07 | 37 | 31 | NA | 7 |
| 8 | source08 | 88-888-8888 | chem08 | 58 | 29 | 9 | 46 |
| 9 | source09 | 99-999-9999 | chem09 | 95 | 24 | 78 | 46 |
| 10 | source10 | 11-222-3333 | chem10 | 68 | 54 | 43 | 25 |
data_exported
| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | V10 |
|---|---|---|---|---|---|---|---|---|---|
| # Slice1!4!0xFF69B4!linear(x) | x | x | |||||||
| # Slice2!4!0x6959CD!linear(x) | x | ||||||||
| # Slice3!4!0xCDC1C5!linear(x) | x | x | x | ||||||
| # Slice4!5!0xFF6347!linear(x) | x | x | x | ||||||
| metric2_slice1 | metric3_slice1 | metric3_slice2 | metric1_slice3 | metric2_slice3 | metric3_slice3 | metric1_slice4 | metric2_slice4 | metric4 | |
| chem01 | 4.0410 | NA | NA | 1.7811 | 0.4891 | 5.0000 | 4.6021 | 4.0410 | NA |
| chem02 | 4.3372 | 4.2924 | 1.0682 | 5.0000 | 1.1714 | 1.0682 | NA | 4.3372 | 4.3188 |
| chem03 | NA | 5.0458 | 2.8028 | 1.2158 | 5.0000 | 2.8028 | 4.3565 | NA | 4.4685 |
| chem04 | 4.1938 | 4.5686 | 1.7042 | 1.7419 | 0.8411 | 1.7042 | 4.5850 | 4.1938 | 5.0458 |
| chem05 | 4.4437 | 4.1612 | 0.7659 | 1.5035 | 1.4165 | 0.7659 | 4.4815 | 4.4437 | 4.0555 |
| chem06 | 4.3372 | NA | NA | 0.4567 | 1.1714 | 5.0000 | 4.0269 | 4.3372 | 4.2676 |
| chem07 | 4.5086 | NA | NA | 1.3891 | 1.5660 | 5.0000 | 4.4318 | 4.5086 | 5.1549 |
| chem08 | 4.5376 | 5.0458 | 2.8028 | 0.9396 | 1.6327 | 2.8028 | 4.2366 | 4.5376 | 4.3372 |
| chem09 | 4.6198 | 4.1079 | 0.6433 | 0.4461 | 1.8219 | 0.6433 | 4.0223 | 4.6198 | 4.3372 |
| chem10 | 4.2676 | 4.3665 | 1.2388 | 0.7805 | 1.0110 | 1.2388 | 4.1675 | 4.2676 | 4.6021 |
Compare results
Although the data files are visually different, they will result in the same toxpi and slice scores.
gui2 <- txpImportGui(data_exported)
res1 <- txpCalculateScores(gui1$model, gui1$input)
res2 <- txpCalculateScores(gui2$model, gui2$input)
all.equal(
txpScores(res1),
txpScores(res2)
)
#> [1] TRUE
all.equal(
txpSliceScores(res1),
txpSliceScores(res2)
)
#> [1] TRUE