worm_visualize,WormTensor-method {WormTensor}

Plots evaluation result A visualization result is generated from a WormTensor object.

Description

Plots evaluation result A visualization result is generated from a WormTensor object.

Usage

## S4 method for signature 'WormTensor'
worm_visualize(
  object,
  out.dir = tempdir(),
  algorithm = c("tSNE", "UMAP"),
  seed = 1234,
  tsne.dims = 2,
  tsne.perplexity = 15,
  tsne.verbose = FALSE,
  tsne.max_iter = 1000,
  umap.n_neighbors = 15,
  umap.n_components = 2,
  silhouette.summary = FALSE
)

Arguments

object

WormTensor object with a result of worm_evaluate

out.dir

Output directory (default: tempdir())

algorithm

Dimensional reduction methods

seed

Arguments passed to set.seed (default: 1234)

tsne.dims

Output dimensionality (default: 2)

tsne.perplexity

Perplexity parameter (default: 15)

tsne.verbose

logical; Whether progress updates should be printed (default: TRUE)

tsne.max_iter

Number of iterations (default: 1000)

umap.n_neighbors

The size of the local neighborhood (default: 15)

umap.n_components

The dimension of the space to embed into (default: 2)

silhouette.summary

logical; If true a summary of cluster silhouettes are printed.

Value

Silhouette plots. ARI with a merge result and each animal(with MCMI). Dimensional reduction plots colored by cluster, no. of identified cells, consistency(with labels), Class_label(with labels).

References

The .dist_nn function is quoted from dist_nn (not exported function) in package uwot(https://github.com/jlmelville/uwot/tree/f467185c8cbcd158feb60dde608c9da153ed10d7).

Examples

    # Temporary directory to save figures
    out.dir <- tempdir()

    # Labels
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) |>
                worm_clustering() -> object
    Ds_mSBD <- worm_download("mSBD", qc = "PASS")
    labels <- list(
        label1 = replace(
            Ds_mSBD$labels$Class,
            which(is.na(Ds_mSBD$labels$Class)),
            "NA"
        ),
        label2 = sample(4, length(object@clustering), replace = TRUE),
        label3 = sample(5, length(object@clustering), replace = TRUE)
    )

    # Pipe Operation (without Labels)
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) |>
                worm_clustering() |>
                    worm_evaluate() |>
                        worm_visualize(out.dir) -> object_no_labels

    # Pipe Operation (with Labels)
    worm_download("mSBD", qc = "PASS")$Ds |>
        as_worm_tensor() |>
            worm_membership(k = 6) |>
                worm_clustering() |>
                    worm_evaluate(labels) |>
                        worm_visualize(out.dir) -> object_labels