Export ToxPi GUI Files

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.

Metrics in multiple slices

The Java GUI does not allow multiple columns to have the same name, unless the data in those columns matches exactly. If a toxpiR model includes an input column in multiple slices, then the name will be appended with the slice index for each occurrence.

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, metric2

Now 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