dittoDimPlot {dittoSeq} | R Documentation

## Shows data overlayed on a tsne, pca, or similar type of plot

### Description

Shows data overlayed on a tsne, pca, or similar type of plot

### Usage

    dittoDimPlot(
      object,
      var,
      reduction.use = .default_reduction(object),
      size = 1,
      opacity = 1,
      dim.1 = 1,
      dim.2 = 2,
      cells.use = NULL,
      shape.by = NULL,
      split.by = NULL,
      extra.vars = NULL,
      split.nrow = NULL,
      split.ncol = NULL,
      assay = .default_assay(object),
      slot = .default_slot(object),
      adjustment = NULL,
      color.panel = dittoColors(),
      colors = seq_along(color.panel),
      shape.panel = c(16, 15, 17, 23, 25, 8),
      show.others = TRUE,
      show.axes.numbers = TRUE,
      show.grid.lines = !grepl("umap|tsne", tolower(reduction.use)),
      main = "make",
      sub = NULL,
      xlab = "make",
      ylab = "make",
      theme = theme_bw(),
      legend.show = TRUE,
      legend.size = 5,
      legend.title = "make",
      shape.legend.size = 5,
      shape.legend.title = shape.by,
      do.ellipse = FALSE,
      do.label = FALSE,
      labels.size = 5,
      labels.highlight = TRUE,
      labels.repel = TRUE,
      rename.var.groups = NULL,
      rename.shape.groups = NULL,
      min.color = "#F0E442",
      max.color = "#0072B2",
      min = NULL,
      max = NULL,
      legend.breaks = waiver(),
      legend.breaks.labels = waiver(),
      do.letter = FALSE,
      do.hover = FALSE,
      hover.data = var,
      hover.assay = .default_assay(object),
      hover.slot = .default_slot(object),
      hover.adjustment = NULL,
      add.trajectory.lineages = NULL,
      add.trajectory.curves = NULL,
      trajectory.cluster.meta,
      trajectory.arrow.size = 0.15,
      data.out = FALSE
    )

### Arguments

`object` | A Seurat or SingleCellExperiment object to work with
`var` | String name of a "gene" or "metadata" (or "ident" for a Seurat `object`) to use for coloring the plots. This is the data that will be displayed for each cell/sample. Discrete or continuous data both work. Alternatively, can be a vector of same length as there are cells/samples in the `object`.
`reduction.use` | String, such as "pca", "tsne", "umap", or "PCA", etc, which is the name of a dimensionality reduction slot within the object, and which sets what dimensionality reduction space within the object to use. Default = the first dimensionality reduction slot inside the object named "umap", "tsne", or "pca", or the first dimensionality reduction slot if none of those exist.
`size` | Number which sets the size of data points. Default = 1.
`opacity` | Number between 0 and 1. Great for when you have MANY overlapping points, this sets how solid the points should be: 1 = not see-through at all. 0 = invisible. Default = 1. (In terms of typical ggplot variables, = alpha)
`dim.1` | The component number to use on the x-axis. Default = 1
`dim.2` | The component number to use on the y-axis. Default = 2
`cells.use` | String vector of cells'/samples' names which should be included. Alternatively, a Logical vector, the same length as the number of cells in the object, which sets which cells to include. For the typically easier logical method, provide `USE` in `colnames(object)[USE]` OR `object@cell.names[USE]`.
`shape.by` | Variable for setting the shape of cells/samples in the plot. Note: must be discrete. Can be the name of a gene or meta-data. Alternatively, can be "ident" for clusters of a Seurat object. Alternatively, can be a numeric of length equal to the total number of cells/samples in object. Note: shapes can be harder to see, and to process mentally, than colors. Even as a color blind person myself writing this code, I recommend use of colors for variables with many discrete values.
`split.by` | 1 or 2 strings naming discrete metadata to use for splitting the cells/samples into multiple plots with ggplot faceting. When 2 metadatas are named, c(row,col), the first is used as rows and the second is used for columns of the resulting grid. When 1 metadata is named, shape control can be achieved with `split.nrow` and `split.ncol`
`extra.vars` | String vector providing names of any extra metadata to be stashed in the dataframe supplied to `ggplot(data)`. Useful for making custom spliting/faceting or other additional alterations after dittoSeq plot generation.
`split.nrow, split.ncol` | Integers which set the dimensions of faceting/splitting when a single metadata is given to `split.by`.
`assay, slot` | single strings or integer that set which data to use when plotting gene expression. See `gene` for more information.
`adjustment` | When plotting gene expression (or antibody, or other forms of counts data), should that data be used directly (default) or should it be adjusted to be "z-score": scaled with the scale() function to produce a relative-to-mean z-score representation "relative.to.max": divided by the maximum expression value to give percent of max values between [0,1]
`color.panel` | String vector which sets the colors to draw from. `dittoColors()` by default, see `dittoColors` for contents.
`colors` | Integer vector, the indexes / order, of colors from color.panel to actually use. Useful for quickly swapping the colors of nearby clusters.
`shape.panel` | Vector of integers corresponding to ggplot shapes which sets what shapes to use. When discrete groupings are supplied by `shape.by`, this sets the panel of shapes. When nothing is supplied to `shape.by`, only the first value is used. Default is a set of 6, `c(16,15,17,23,25,8)`, the first being a simple, solid, circle. Note: Unfortunately, shapes can be hard to see when points are on top of each other & they are more slowly processed by the brain. For these reasons, even as a color blind person myself writing this code, I recommend use of colors for variables with many discrete values.
`show.others` | Logical. Whether other cells should be shown in the background in light gray. Default = TRUE.
`show.axes.numbers` | Logical which controls whether the axes values should be displayed.
`show.grid.lines` | Logical which sets whether gridlines of the plot should be shown. They are removed when set to FALSE. Default = TRUE for umap and tsne `reduction.use`, FALSE otherwise.
`main` | String, sets the plot title. Default title is automatically generated if not given a specific value. To remove, set to `NULL`.
`sub` | String, sets the plot subtitle
`xlab, ylab` | Strings which set the labels for the axes. Default labels are generated if you do not give this a specific value. To remove, set to `NULL`.
`theme` | A ggplot theme which will be applied before dittoSeq adjustments. Default = `theme_bw()`. See https://ggplot2.tidyverse.org/reference/ggtheme.html for other options and ideas.
`legend.show` | Logical. Whether the legend should be displayed. Default = `TRUE`.
`legend.size` | Number representing the size at which color legend shapes should be plotted (for discrete variable plotting) in the color legend. Default = 5. *Enlarging the colors legend is incredibly helpful for making colors more distinguishable by color blind individuals.
`legend.title` | String which sets the title for the color legend. Default = `NULL` normally, but `var` when a shape legend will also be shown.
`shape.legend.size` | Number representing the size at which shapes should be plotted in the shape legend.
`shape.legend.title` | String which sets the title of the shapes legend. Default is `shape.by`
`do.ellipse` | Logical. Whether the groups should be surrounded by median-centered ellipses.
`do.label` | Logical. Whether to add text labels near the center (median) of clusters for grouping vars.
`labels.size` | Size of the the labels text
`labels.highlight` | Logical. Whether the labels should have a box behind them
`labels.repel` | Logical, that sets whether the labels' placements will be adjusted with ggrepel to avoid intersections between labels and plot bounds. TRUE by default.
`rename.var.groups` | String vector which sets new names for the identities of `var` groups.
`rename.shape.groups` | String vector which sets new names for the identities of `shape.by` groups.
`min.color` | color for lowest values of `var`/`min`. Default = yellow
`max.color` | color for highest values of `var`/`max`. Default = blue
`min` | Number which sets the value associated with the minimum color.
`max` | Number which sets the value associated with the maximum color.
`legend.breaks` | Numeric vector which sets the discrete values to show in the color-scale legend for continuous data.
`legend.breaks.labels` | String vector, with same length as `legend.breaks`, which renames what's displayed next to the tick marks of the color-scale.
`do.letter` | Logical which sets whether letters should be added on top of the colored dots. For extended colorblindness compatibility. NOTE: `do.letter` is ignored if `do.hover = TRUE` or `shape.by` is provided a metadata because lettering is incompatible with plotly and with changing the dots' to be different shapes.
`do.hover` | Logical which controls whether the output will be converted to a plotly object so that data about individual points will be displayed when you hover your cursor over them. `hover.data` argument is used to determine what data to use.
`hover.data` | String vector of gene and metadata names, example: `c("meta1","gene1","meta2")` which determines what data to show on hover when `do.hover` is set to `TRUE`.
`hover.assay, hover.slot, hover.adjustment` | Similar to the non-hover versions of these inputs, when showing expression data upon hover, these set what data will be shown.
`add.trajectory.lineages` | List of vectors representing trajectory paths, each from start-cluster to end-cluster, where vector contents are the names of clusters provided in the `trajectory.cluster.meta` input. If the `slingshot` package was used for trajectory analysis, you can use `add.trajectory.lineages = SlingshotDataSet(SCE_with_slingshot)$lineages`. In future versions, I might build such retrieval in by default for SCEs.
`add.trajectory.curves` | List of matrices, each representing coordinates for a trajectory path, from start to end, where matrix columns represent x (`dim.1`) and y (`dim.2`) coordinates of the paths. Alternatively, a list of lists(/princurve objects) can be provided. Thus, if the `slingshot` package was used for trajectory analysis, you can provide `add.trajectory.curves = SlingshotDataSet(SCE_with_slingshot)$curves`
`trajectory.cluster.meta` | String name of metadata containing the clusters that were used for generating trajectories. Required when plotting trajectories using the `add.trajectory.lineages` method. Names of clusters inside the metadata should be the same as the contents of `add.trajectory.lineages` vectors.
`trajectory.arrow.size` | Number representing the size of trajectory arrows, in inches. Default = 0.15.
`data.out` | Logical. When set to `TRUE`, changes the output, from the plot alone, to a list containing the plot ("p"), a data.frame containing the underlying data for target cells ("Target_data"), and a data.frame containing the underlying data for non-target cells ("Others_data"). Note: `do.hover` plotly conversion is turned off in this setting, but hover.data is still calculated.

### Details

The function creates a dataframe containing the metadata or expression data associated with the given `var` (or if a vector of data is provided directly, it just uses that), plus X and Y coordinates data determined by the `reduction.use` and `dim.1` (x-axis) and `dim.2` (y-axis) inputs. Any extra data requested with `shape.by`, `split.by` or `extra.var` is added as well. For expression/counts data, `assay`, `slot`, and `adjustment` inputs can be used to change which data is used, and if it should be adjusted in some way.

Next, if a set of cells or samples to use is indicated with the `cells.use` input, then the dataframe is split into `Target_data` and `Others_data` based on subsetting by the target cells/samples.

Finally, a scatter plot is then created using these dataframes where non-target cells will be displayed in gray if `show.others=TRUE`, and target cell data is displayed on top, colored based on the `var`-associated data, and with shapes determined by the `shape.by`-associated data. If `split.by` was used, the plot will be split into a matrix of panels based on the associated groupings.

### Value

A ggplot or plotly object where colored dots (or other shapes) are overlayed onto a tSNE, PCA, UMAP, ..., plot of choice.

Alternatively, if `data.out=TRUE`, a list containing three slots is output: the plot (named 'p'), a data.table containing the underlying data for target cells (named 'Target_data'), and a data.table containing the underlying data for non-target cells (named 'Others_data').

Alternatively, if `do.hover` is set to `TRUE`, the plot is coverted from ggplot to plotly & cell/sample information, determined by the `hover.data` input, is retrieved, added to the dataframe, and displayed upon hovering the cursor over the plot.

### Many characteristics of the plot can be adjusted using discrete inputs

- `size` and `opacity` can be used to adjust the size and transparency of the data points.

- Color can be adjusted with `color.panel` and/or `colors` for discrete data, or `min`, `max`, `min.color`, and `max.color` for continuous data.

- Shapes can be adjusted with `shape.panel`.

- Color and shape labels can be changed using `rename.var.groups` and `rename.shape.groups`.

- Titles and axes labels can be adjusted with `main`, `sub`, `xlab`, `ylab`, and `legend.title` arguments.

- Legends can also be adjusted in other ways, using variables that all start with "`legend.`" for easy tab-completion lookup.

### Additional Features

Many other tweaks and features can be added as well. Each is accessible through 'tab' autocompletion starting with "`do.`"`---` or "`add.`"`---`, and if additional inputs are involved in implementing or tweaking these, the associated inputs will start with the "`---.`":

- If `do.label` is set to `TRUE`, labels will be added based on median centers of the discrete `var`-data groupings. The size of the text in the labels can be adjusted using the `labels.size` input. By default labels will repel eachother and the bounds of the plot, and labels will be highlighted with a white background. Either of these can be turned off by setting `labels.repel = FALSE` or `labels.highlight = FALSE`,

- If `do.ellipse` is set to `TRUE`, ellipses will be added to highlight distinct `var`-data groups' positions based on median positions of their cell/sample components.

- If `add.trajectory.lineages` is provided a list of vectors (each vector being cluster names from start-cluster-name to end-cluster-name), and a metadata name pointing to the relevant clustering information is provided to `trajectory.cluster.meta`, then median centers of the clusters will be calculated and arrows will be overlayed to show trajectory inference paths in the current dimmenionality reduction space.

- If `add.trajectory.curves` is provided a list of matrices (each matrix containing x, y coordinates from start to end), paths and arrows will be overlayed to show trajectory inference curves in the current dimmenionality reduction space. Arrow size is controlled with the `trajectory.arrow.size` input.

### Author(s)

Daniel Bunis

### See Also

`getGenes` and `getMetas` to see what the `var`, `shape.by`, etc. options are.

`importDittoBulk` for how to create a `SingleCellExperiment` object from bulk seq data that dittoSeq functions can use & `addDimReduction` for how to specifically add calculated dimensionality reductions that `dittoDimPlot` can utilize.

`dittoScatterPlot` for showing very similar data representations, but where genes or metadata are wanted as the axes.

`dittoPlot` for an alternative continuous data display method where data is shown on a y- (or x-) axis.

`dittoBarPlot` for an alternative discrete data display and quantification method.

### Examples

    # dittoSeq handles bulk and single-cell data quit similarly.
    # The SingleCellExperiment object structure is used for both,
    # but all functions can be used similarly directly on Seurat
    # objects as well.

    example(importDittoBulk, echo = FALSE)
    myRNA

    # Display discrete data:
    dittoDimPlot(myRNA, "clustering")
    # Display continuous data:
    dittoDimPlot(myRNA, "gene1")

    # To show currently set clustering for seurat objects, you can use "ident".
    # To change the dimensional reduction type, use 'reduction.use'.
    dittoDimPlot(myRNA, "clustering",
        reduction.use = "pca",
        dim.1 = 3,
        dim.2 = 4)

    # Subset to certain cells with cells.use
    dittoDimPlot(myRNA, "clustering",
        cells.us = !myRNA$SNP)

    # Data can also be split in other ways with 'shape.by' or 'split.by'
    dittoDimPlot(myRNA, "gene1",
        shape.by = "clustering",
        split.by = "SNP") # single split.by element
    dittoDimPlot(myRNA, "gene1",
        split.by = c("groups","SNP")) # row and col split.by elements

    # Modify the look with intuitive inputs
    dittoDimPlot(myRNA, "clustering",
        size = 2, opacity = 0.7, show.axes.numbers = FALSE,
        ylab = NULL, xlab = "tSNE",
        main = "Plot Title",
        sub = "subtitle",
        legend.title = "clustering")

    # MANY addtional tweaks are possible.
    # Also, many extra features are easy to add as well:
    dittoDimPlot(myRNA, "clustering",
        do.label = TRUE, do.ellipse = TRUE)
    dittoDimPlot(myRNA, "clustering",
        do.label = TRUE, labels.highlight = FALSE, labels.size = 8)
    if (requireNamespace("plotly", quietly = TRUE)) {
        dittoDimPlot(myRNA, "gene1", do.hover = TRUE,
            hover.data = c("gene2", "clustering", "timepoint"))
    }
    dittoDimPlot(myRNA, "gene1", add.trajectory.lineages = list(c(1,2,4), c(1,3)),
        trajectory.cluster.meta = "clustering",
        sub = "Pseudotime Trajectories")

[Package dittoSeq version 1.0.2 Index]