# Storing and Fine-Tuning APackOfTheClones Runs

## Introduction

As demonstrated in `vignette("APackOfTheClones")`, after processing the seurat & clonotype data properly with `scRepertoire`, `vizAPOTC()` provides a direct way to produce the ball-packing clonal expansion visualization, though for select users it may be somewhat clunky, if certain parameters need to be readjusted constantly. In this vignette, more details about how APackOfTheClones runs can be stored and re-adjusted will be covered - mainly through `RunAPOTC()`, `APOTCPlot()`, and `AdjustAPOTC`. Ensure to read the aforementioned vignette before this one.

As a reminder, here’s how to set up the seurat object and clonotype data

    library(scRepertoire)

    # A seurat object named `pbmc` is loaded with a corresponding `contig_list`
    pbmc <- scRepertoire::combineExpression(
      scRepertoire::combineTCR(
        contig_list,
        samples = c("P17B", "P17L", "P18B", "P18L", "P19B", "P19L", "P20B", "P20L"),
        removeNA = FALSE,
        removeMulti = FALSE,
        filterMulti = FALSE
      ),
      pbmc,
      cloneCall = "gene",
      proportion = TRUE
    )

    print(pbmc)
    #> An object of class Seurat
    #> 2000 features across 500 samples within 1 assay
    #> Active assay: RNA (2000 features, 2000 variable features)
    #>  2 layers present: counts, data
    #>  1 dimensional reduction calculated: umap

### Overview

All of `vizAPOTC()`’s arguments are actually derived from `RunAPOTC()` and `APOTCPlot()`. The former is responsible for storing data of the S4 class `ApotcData` in the seurat object under a named list in `@misc$APackOfTheClones` under some character run ID, and the latter allows the visualization of these data objects with some customization. `AdjustAPOTC` has many arguments for adjusting the data associated with some APackOfTheClones run stored by `RunAPOTC()`, including adjusting cluster positions, colors, repulsion, etc. which can be visualized again with `APOTCPlot()`.

## Managing APackOfTheClones run data

`RunAPOTC()` has approximate the first half of `vizAPOTC()`’s arguments until `max_repulsion_iter`, meaning it has all the data subsetting, circle size scaling, and cluster repulsion capabilities covered in the previous vignette. The most essential difference is the presence of the argument `run_id`, which corresponds to an id for the `ApotcData` object associated with some run. If left blank, one will be automatically generated in the following format:

> `reduction_base;clonecall;keyword_arguments;extra_filter`

where if keyword arguments and extra_filter are underscore (`_`) characters if there was no input for the `...` and `extra_filter` parameters.

    # Here is the function ran with its default parameters
    pbmc <- RunAPOTC(pbmc)

    #> Initializing APOTC run...
    #> * Setting `clone_scale_factor` to 0.3
    #> * id for this run: umap;CTstrict;_;_
    #>
    #> Packing clones into clusters
    #> [==================================================] 100%
    #>
    #> repulsing all clusters | max iterations = 20
    #> [==================================================] 100%
    #>
    #> Completed successfully, time elapsed: 0.155 seconds
    #>

From the verbal queues, one can see how the `run_id` was set. Here’s it ran again but with more optional arguments and a custom `run_id`:

    pbmc <- RunAPOTC(
        pbmc, run_id = "sample17", orig.ident = c("P17B", "P17L"), verbose = FALSE
    )

### Important Note

Practically speaking, most users probably are only working with visualizations from one run and just adjust its parameters - else they already will have several subsetted seurat objects. In these cases, feel free to completely ignore any of the parameters related to `run_id` or any other filtering parameter, as all functions default to using the latest run if no identifying parameters are passed in.

### Utilities for Managing APackOfTheClones Runs

It is to note that the data abstraction here with a `run_id` is intentional, and users should not manually touch any of the `ApotcData` objects with the seurat object unless they are extremely familiar with the latest internal implementation. Instead, here is a collection of functions that may be useful:

- `getApotcDataIds(pbmc)` gets all current `run_id`’s, if any.
- `getLastApotcDataId(pbmc)` gets the latest `run_id`, if any.
- `containsApotcRun(pbmc, run_id = "foo")` returns whether a `run_id` exists in the seurat object.
- `renameApotcRun(pbmc, old_run_id = "foo", new_run_id = "bar")` renames runs.
- `deleteApotcData(pbmc, run_id = "foo")` deletes all data associated with a certain `run_id`.

## APOTCPlot

To visualize stored APackOfTheClones runs, `APOTCPlot()` takes in a seurat object and the `run_id`. If no `run_id` is provided, it defaults to using the latest run. All other parameters are same as in the second half of `vizAPOTC()`. Although it is noteworthy that if the user had always relied on auto-generated `run_id`’s then `APOTCPlot()` also has these subsetting arguments:

And putting in identical arguments to generate the original `ApotcData` would work too, but this approach is less recommended as its a lot more (unnecessarily) verbose. Here is `APOTCPlot()` in action:

    # Here, plots for samples 17 - 20 as seen in the previous vignette are made, where
    # `orig.ident` is a custom column in the example data with levels corresponding to sample ids:
    # ("P17B" "P17L" "P18B" "P18L" "P19B" "P19L" "P20B" "P20L").

    pbmc <- RunAPOTC(
      pbmc, run_id = "P17", orig.ident = c("P17B", "P17L"), verbose = FALSE
    )

    pbmc <- RunAPOTC(
      pbmc, run_id = "P18", orig.ident = c("P18B", "P18L"), verbose = FALSE
    )

    pbmc <- RunAPOTC(
      pbmc, run_id = "P19", orig.ident = c("P19B", "P19L"), verbose = FALSE
    )

    pbmc <- RunAPOTC(
      pbmc, run_id = "P20", orig.ident = c("P20B", "P20L"), verbose = FALSE
    )

    cowplot::plot_grid(
      APOTCPlot(pbmc, run_id = "P17", retain_axis_scales = TRUE, add_size_legend = FALSE),
      APOTCPlot(pbmc, run_id = "P18", retain_axis_scales = TRUE, add_size_legend = FALSE),
      APOTCPlot(pbmc, run_id = "P19", retain_axis_scales = TRUE, add_size_legend = FALSE),
      APOTCPlot(pbmc, retain_axis_scales = TRUE, add_size_legend = FALSE), # defaults to latest
      labels = c("17", "18", "19", "20")
    )
    #> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
    #> ℹ Please use tidy evaluation idioms with `aes()`.
    #> ℹ See also `vignette("ggplot2-in-packages")` for more information.
    #> ℹ The deprecated feature was likely used in the Seurat package.
    #>   Please report the issue at <https://github.com/satijalab/seurat/issues>.
    #> This warning is displayed once every 8 hours.
    #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
    #> generated.
    #> * generated ggplot object
    #> * generated ggplot object
    #> * generated ggplot object
    #> * using the latest APackOfTheClones Run Data with object id: P20
    #> * generated ggplot object

## AdjustAPOTC

This function’s parameters help modify certain attributes about APackOfTheClones runs, and has the exact same first six parameters as `APOTCPlot()` for managing which run to modify. It also possesses the same four repulsion arguments in `vizAPOTC()` and `RunAPOTC()` if a run is to be repulsed again. See the function level documentation for the following parameters that can modify cluster locations, colors, and the adjustment of the `clone_scale_factor` and `rad_scale_factor`:

    new_rad_scale_factor = NULL,
    new_clone_scale_factor = NULL,
    relocate_cluster = NULL,
    relocation_coord = NULL,
    nudge_cluster = NULL,
    nudge_vector = NULL,
    recolor_cluster = NULL,
    new_color = NULL,
    rename_label = NULL,
    new_label = NULL,
    relocate_label = NULL,
    label_relocation_coord = NULL,
    nudge_label = NULL,
    label_nudge_vector = NULL,
    verbose = TRUE

### Tip to Speeding up Adjustments

While the `vizAPOTC()` and `RunAPOTC()` / `APOTCPlot()` functions to do APackOfTheClones runs and generate the ggplot object are relatively fast even for very large seurat objects, plot display times can get quite long the more circles there are. `AdjustAPOTC` is meant to be used in an incremental/iterative manner, where the user can adjust one aspect, inspect the plot, and adjust again if necessary, and repeat - the long plot display times may pose as a large inconvenience.

Setting the argument `detail` to `FALSE` when using `APOTCPlot()` or `vizAPOTC()` can help - this will plot entire clusters as one large circle - speeding up plotting times significantly. The details of the individual clonotype circles will be lost but the inspection of other aspects like label locations will be a much more pleasant process.

### Usage Examples

#### Renaming Cluster Labels

After identification of the cell identity of each seurat cluster, it may be useful to rename each cluster label to the actual identity of the cells of each cluster. here is a minimal example of how this process is intended to be done:

    # Do a run with just the first 4 seurat clusters, and rename labels
    pbmc <- RunAPOTC(
        pbmc,
        run_id = "first_four",
        seurat_clusters = 1:4,
        verbose = FALSE
    )

    pbmc <- AdjustAPOTC(
        pbmc,
        run_id = "first_four",
        rename_label = 1:4,
        new_label = letters[1:4],
        verbose = FALSE
    )

    APOTCPlot(
        pbmc,
        run_id = "first_four",
        show_labels = TRUE,
        retain_axis_scales = TRUE
    )
    #> * generated ggplot object

#### Extra Cluster Repulsion

Another possibility is that the default repulsion may have left the clusters too close - here this is corrected - and the labels are moved as well. Note the use of the pipe operator may also be useful.

    pbmc <- pbmc %>%
        RunAPOTC(run_id = "foo", verbose = FALSE) %>%
        AdjustAPOTC(
            run_id = "foo",
            repulse = TRUE,
            repulsion_threshold = 0.5,
            verbose = FALSE
        )

    APOTCPlot(
        pbmc,
        show_labels = TRUE,
        retain_axis_scales = TRUE,
        add_size_legend = FALSE
    )
    #> * using the latest APackOfTheClones Run Data with object id: foo
    #> * generated ggplot object