# Import ToxPi GUI Files

This vignette will show how to load a data file that was saved using the ToxPi Java GUI, which can be downloaded from here. The ToxPi Java GUI will save data files using file format “C” described in the ToxPi User Manual. This vignette will use the “format_C.csv” file to demonstrate how to import GUI data.

    library(toxpiR)

    ## Create a tempfile and download 'format_C.csv'
    fmtc <- tempfile()
    ghuc <- "https://raw.githubusercontent.com"
    fmtcUrl <- file.path(ghuc, "ToxPi", "ToxPi-example-files", "main", "format_C.csv")
    download.file(url = fmtcUrl, destfile = fmtc, quiet = TRUE)

The “format_C.csv” model specification reuses metrics across different slices. In general, we do not recommend duplicating inputs across slices, so the user gets a warning when creating a model with duplicate inputs.

    ## Import file into R
    gui <- txpImportGui(fmtc)
    #> Warning in method(object): The following 'input' columns are duplicated in the model:
    #>     metric3, metric2, metric3, metric1, metric2

The resulting `list` object contains: `$model`, a `TxpModel` object with the model specifications; `$input`, a `data.frame` containing the data for calculating ToxPi scores; and `$fills`, an array of slice colors for plotting.

    gui$model
    #> TxpModel with 4 slices.
    #>   txpSlices(4): Slice1 Slice2 Slice3 Slice4
    #>   txpWeights(4): 4 4 4 5
    #>   txpTransFuncs(4): NULL NULL NULL NULL
    gui$input
    #>    Row   Source       CASRN   Name metric1 metric2 metric3 metric4
    #> 1    1 source01 11-111-1111 chem01      25      91      NA      NA
    #> 2    2 source02 22-222-2222 chem02      NA      46      51      48
    #> 3    3 source03 33-333-3333 chem03      44      NA       9      34
    #> 4    4 source04 44-444-4444 chem04      26      64      27       9
    #> 5    5 source05 55-555-5555 chem05      33      36      69      88
    #> 6    6 source06 66-666-6666 chem06      94      46      NA      54
    #> 7    7 source07 77-777-7777 chem07      37      31      NA       7
    #> 8    8 source08 88-888-8888 chem08      58      29       9      46
    #> 9    9 source09 99-999-9999 chem09      95      24      78      46
    #> 10  10 source10 11-222-3333 chem10      68      54      43      25
    gui$fills
    #> [1] "#FF69B4" "#6959CD" "#CDC1C5" "#FF6347"

We calculate ToxPi scores using the `txpCalculateScores` function, which takes a model and input `data.frame`. Note that by default the ToxPi GUI does not accept negative values. However, the package keeps them by default. To replicate the GUI functionailty, we set `negative.value.handling = "missing"`.

    ## Calculate ToxPi scores
    res <- txpCalculateScores(model = gui$model, input = gui$input, id.var = "Name",negative.value.handling = "missing")

    ## Overall ToxPi scores
    txpScores(res)
    #>         1         2         3         4         5         6         7         8
    #> 0.1679363 0.4085540 0.5230658 0.6485548 0.4709411 0.3709372 0.5082931 0.8065481
    #>         9        10
    #> 0.4328831 0.5015004

    ## Slice scores
    txpSliceScores(res, adjusted = FALSE)
    #>        Slice1     Slice2     Slice3      Slice4
    #> 1  0.00000000 0.00000000 0.71372906 0.000000000
    #> 2  0.82792881 0.19675216 0.70869704 0.002381066
    #> 3  0.18129307 1.00000000 1.00000000 0.033389421
    #> 4  0.85188671 0.49126173 0.22527793 0.950345105
    #> 5  0.82344996 0.05677389 0.12681807 0.795566352
    #> 6  0.05345762 0.00000000 0.60857479 0.731560510
    #> 7  0.08438215 0.00000000 0.82586349 1.000000000
    #> 8  1.00000000 1.00000000 0.40340351 0.819540604
    #> 9  0.84561468 0.00000000 0.00000000 0.795310761
    #> 10 0.82873456 0.27576432 0.01947655 0.805920923

A results output similar to that given by the Java GUI can be obtained by combining score components.

    out <- as.data.frame(res, adjusted = FALSE)
    out <- out[order(out$score, decreasing = TRUE), ]
    out
    #>        id     score rank     Slice1     Slice2     Slice3      Slice4
    #> 8  chem08 0.8065481    1 1.00000000 1.00000000 0.40340351 0.819540604
    #> 4  chem04 0.6485548    2 0.85188671 0.49126173 0.22527793 0.950345105
    #> 3  chem03 0.5230658    3 0.18129307 1.00000000 1.00000000 0.033389421
    #> 7  chem07 0.5082931    4 0.08438215 0.00000000 0.82586349 1.000000000
    #> 10 chem10 0.5015004    5 0.82873456 0.27576432 0.01947655 0.805920923
    #> 5  chem05 0.4709411    6 0.82344996 0.05677389 0.12681807 0.795566352
    #> 9  chem09 0.4328831    7 0.84561468 0.00000000 0.00000000 0.795310761
    #> 2  chem02 0.4085540    8 0.82792881 0.19675216 0.70869704 0.002381066
    #> 6  chem06 0.3709372    9 0.05345762 0.00000000 0.60857479 0.731560510
    #> 1  chem01 0.1679363   10 0.00000000 0.00000000 0.71372906 0.000000000

ToxPi images and overall score rank plot can also be produced.

    plot(sort(res), fills = gui$fills)

    plot(res, txpRanks(res))

    plot(res, txpRanks(res), labels = 1:10, pch = 16, size = grid::unit(0.75, "char"))

The basic clustering methods offered in the Java GUI can also be recreated.

    ## Hierarchical Clustering
    hc <- hclust(dist(txpSliceScores(res)), method = 'complete')
    plot(hc, hang = -1, labels = txpIDs(res), xlab = 'Name', sub = '')

    ## K-Means Clustering, plotted using principal components
    nClusters <- 3
    km <- kmeans(txpSliceScores(res), nClusters)
    pc <- prcomp(txpSliceScores(res))
    coord <- predict(pc) * -sum(txpWeights(res))
    plot(coord[,1], coord[,2], col = km$cluster,
         xlab = 'PC1', ylab = 'PC2', pch = 16)