dWilcox {DepecheR} | R Documentation

## Wilcoxon rank-sum or signed rank test comparison of subject groups in a dClust result

### Description

This function is used to compare groups of individuals from whom comparable cytometry or other complex data has been generated.

### Usage

    dWilcox(
      xYData,
      idsVector,
      groupVector,
      clusterVector,
      displayVector,
      paired = FALSE,
      multipleCorrMethod = "BH",
      densContour = TRUE,
      plotName = "default",
      groupName1 = unique(groupVector)[1],
      groupName2 = unique(groupVector)[2],
      title = FALSE,
      lowestPlottedP = 0.05,
      plotDir = ".",
      bandColor = "black",
      dotSize = 500/sqrt(nrow(xYData)),
      createOutput = TRUE
    )

### Arguments

`xYData` | A dataframe or matrix with two columns. Each row contains information about the x and y positition in the field for that observation.
`idsVector` | Vector with the same length as xYData containing information about the id of each observation.
`groupVector` | Vector with the same length as xYData containing information about the group identity of each observation.
`clusterVector` | Vector with the same length as xYData containing information about the cluster identity of each observation.
`displayVector` | Optionally, if the dataset is very large and the SNE calculation hence becomes impossible to perform for the full dataset, this vector can be included. It should contain the set of rows from the data used for statistics, that has been used to generate the xYData.
`paired` | Defaults to FALSE, i.e. no assumption of pairing is made and Wilcoxon rank sum-test is performed. If true, the software will by default pair the first id in the first group with the firs id in hte second group and so forth.
`multipleCorrMethod` | Which method that should be used for adjustment of multiple comparisons. Defaults to Benjamini-Hochberg, but all other methods available in `p.adjust` can be used.
`densContour` | If density contours should be created for the plot(s) or not. Defaults to TRUE. a
`plotName` | The main name for the graph and the analysis.
`groupName1` | The name for the first group
`groupName2` | The name for the second group
`title` | If there should be a title displayed on the plotting field. As the plotting field is saved as a png, this title cannot be removed as an object afterwards, as it is saved as coloured pixels. To simplify usage for publication, the default is FALSE, as the files are still named, eventhough no title appears on the plot.
`lowestPlottedP` | If multiple plots should be compared, it might be useful to define a similar color scale for all plots, so that the same color always means the same statistical value. A p-value that determines this can be added here. Default is a p-value of 0.05. In cases where no datapoints have any lower p-values than this, a Wilcoxon-statistic corresponding as closely as possible to 0.05 will be identified with iterations of datasets with the same size as indicated by hte group vector. If one value is lowerthan 0.05, the wilcoxon statistic from this comparison is used instead.
`plotDir` | If different from the current directory. If specified and non-existent, the function creates it. If "." is specified, the plots will be saved at the current directory.
`bandColor` | The color of the contour bands. Defaults to black.
`dotSize` | Simply the size of the dots. The default makes the dots smaller the more observations that are included.
`createOutput` | For testing purposes. Defaults to TRUE. If FALSE, no plots are generated.

### Value

This function always returns a dataframe showing the Wilcoxon statistic and the p-value for each cluster, with an included adjustment for multiple comparisons (see above). It also returns a sne based plot showing which events that belong to a cluster dominated by the first or the second group.

### See Also

`dColorPlot`, `dDensityPlot`, `dResidualPlot`

### Examples

    # Load some data
    data(testData)
    ## Not run:
    # Load or create the dimensions that you want to plot the result over.
    # uwot::umap recommended due to speed, but tSNE or other method would
    # work as fine.
    data(testDataSNE)

    # Run the clustering function. For more rapid example execution,
    # a depeche clustering of the data is inluded
    # testDataDepeche <- depeche(testData[,2:15])
    data(testDataDepeche)

    # Run the function
    dWilcoxResult <- dWilcox(
        xYData = testDataSNE$Y, idsVector = testData$ids,
        groupVector = testData$label, clusterVector =
        testDataDepeche$clusterVector
    )

    # Here is an example of how the display vector can be used.
    subsetVector <- sample(1:nrow(testData), size = 10000)

    # Now, the SNE for this displayVector could be created
    # testDataSubset <- testData[subsetVector, 2:15]
    # testDataSNESubset <- Rtsne(testDataDisplay, pca=FALSE)$Y
    # But we will just subset the testDataSNE immediately
    testDataSNESubset <- testDataSNE$Y[subsetVector, ]

    # And now, this new SNE can be used for display, although all
    # the data is used for the Wilcoxon calculations
    dWilcoxResult <- dWilcox(
        xYData = testDataSNESubset, idsVector = testData$ids,
        groupVector = testData$label, clusterVector =
            testDataDepeche$clusterVector, displayVector = subsetVector
    )

    ## End(Not run)

[Package DepecheR version 1.8.0 Index]