R: Visualisation of distributions using histograms

vis_hist {immunarch} | R Documentation

## Visualisation of distributions using histograms

### Description

Visualisation of distributions using ggplot2-based histograms.

### Usage

    vis_hist(
      .data,
      .by = NA,
      .meta = NA,
      .title = "Gene usage",
      .ncol = NA,
      .points = TRUE,
      .test = TRUE,
      .coord.flip = FALSE,
      .grid = FALSE,
      .labs = c("Gene", NA),
      .melt = TRUE,
      .legend = NA,
      .add.layer = NULL,
      ...
    )

### Arguments

`.data` | Input matrix or data frame.
`.by` | Pass NA if you want to plot samples without grouping. You can pass a character vector with one or several column names from ".meta" to group your data before plotting. In this case you should provide ".meta". You can pass a character vector that exactly matches the number of samples in your data, each value should correspond to a sample's property. It will be used to group data based on the values provided. Note that in this case you should pass NA to ".meta".
`.meta` | A metadata object. An R dataframe with sample names and their properties, such as age, serostatus or hla.
`.title` | The text for the title of the plot.
`.ncol` | A number of columns to display. Provide NA (by default) if you want the function to automatically detect the optimal number of columns.
`.points` | A logical value defining whether points will be visualised or not.
`.test` | A logical vector whether statistical tests should be applied. See "Details" for more information.
`.coord.flip` | If TRUE then swap x- and y-axes.
`.grid` | If TRUE then plot separate visualisations for each sample.
`.labs` | A character vector of length two with names for x-axis and y-axis, respectively.
`.melt` | If TRUE then apply melt to the ".data" before plotting. In this case ".data" is supposed to be a data frame with the first character column reserved for names of genes and other numeric columns reserved to counts or frequencies of genes. Each numeric column should be associated with a specific repertoire sample.
`.legend` | If TRUE then plots the legend. If FALSE removes the legend from the plot. If NA automatically detects the best way to display legend.
`.add.layer` | Addditional ggplot2 layers, that added to each plot in the output plot or grid of plots.
`...` | Is not used here.

### Details

If data is grouped, then statistical tests for comparing means of groups will be performed, unless `.test = FALSE` is supplied. In case there are only two groups, the Wilcoxon rank sum test (https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test) is performed (R function `wilcox.test` with an argument `exact = FALSE`) for testing if there is a difference in mean rank values between two groups. In case there more than two groups, the Kruskal-Wallis test (https://en.wikipedia.org/wiki/Kruskal A significant Kruskal-Wallis test indicates that at least one sample stochastically dominates one other sample. Adjusted for multiple comparisons P-values are plotted on the top of groups. P-value adjusting is done using the Holm method (https://en.wikipedia.org/wiki/Holm You can execute the command `?p.adjust` in the R console to see more.

### Value

A ggplot2 object.

### See Also

vis.immunr_gene_usage, geneUsage

### Examples

    data(immdata)
    imm_gu <- geneUsage(immdata$data[[1]])
    vis(imm_gu,
      .plot = "hist", .add.layer =
        theme(axis.text.x = element_text(angle = 75, vjust = 1))
    )
    imm_gu <- geneUsage(immdata$data[1:4])
    vis(imm_gu,
      .plot = "hist", .grid = TRUE, .add.layer =
        theme(axis.text.x = element_text(angle = 75, vjust = 1))
    )

[Package immunarch version 0.6.7 Index]