Load libraries

Install WormTensor package from CRAN or GitHub in advance and then type the code below in the R console window.

library(WormTensor)

worm_download

worm_download is a function to retrieve data from figshare for a total of 28 animals (24 normal and 4 noisy). If there is no argument, mSBD distance matrices (including 24 normal animals) will be downloaded.

object <- worm_download()

as_worm_tensor

as_worm_tensor is a function to generate a WormTensor object from distance matrices. A WormTensor object S4 class is used by worm_membership, worm_clustering, worm_evaluate, and worm_visualize.

object <- as_worm_tensor(object$Ds)

worm_membership

worm_membership is a function to generate a membership tensor from a WormTensor object with distance matrices. Set the assumed number of clusters to k(>=2).

object <- worm_membership(object, k=6)

worm_clustering

worm_clustering is a function to generate a clustering result from a WormTensor object with a membership tensor.

object <- worm_clustering(object)

worm_evaluate

worm_evaluate is a function to generate an evaluation result from a WormTensor object with a worm_clustering result.

object <- worm_evaluate(object)

worm_visualizeworm_visualizeis a function to visualizeworm_clusteringandworm_evaluate` results.

object <- worm_visualize(object)

Figure1a : Silhouette plots

Figure1b : Dimensional reduction Plots colored by cluster

Figure1c : Dimensional reduction Plots colored by no. of identified cells

Figure1d : ARI with a merge result and each animal(with MCMI)

Pipe Operation

The above functions can also be run by connecting them with R’s native pipe.

worm_download()$Ds |>
    as_worm_tensor() |>
        worm_membership(k=6) |>
            worm_clustering() |>
                worm_evaluate() |>
                    worm_visualize() -> object

Pipe Operation (with Labels)

If you have a label for the cells, you can use it for external evaluation.

# Sample Labels
worm_download()$Ds |>
    as_worm_tensor() |>
        worm_membership(k=6) |>
            worm_clustering() -> object
labels <- list(
    label1 = sample(3, length(object@clustering), replace=TRUE),
    label2 = sample(4, length(object@clustering), replace=TRUE),
    label3 = sample(5, length(object@clustering), replace=TRUE))
# WormTensor (with Labels)
worm_download()$Ds |>
    as_worm_tensor() |>
        worm_membership(k=6) |>
            worm_clustering() |>
                worm_evaluate(labels) |>
                    worm_visualize() -> object_labels