WormTensorInstall WormTensor package from CRAN or GitHub in
advance and then type the code below in the R console window.
library(WormTensor)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 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$Ds)worm_membership is a function to generate a membership
tensor from a WormTensor object with distance matrices. Set
the assumed number of clusters to k(>=2).
object <- worm_membership(object, k=6)worm_clustering is a function to generate a clustering
result from a WormTensor object with a membership
tensor.
object <- worm_clustering(object)worm_evaluate is a function to generate an evaluation
result from a WormTensor object with a
worm_clustering result.
object <- worm_evaluate(object)is a function to visualizeworm_clusteringandworm_evaluate`
results.object <- worm_visualize(object)
Figure1a : Silhouette plots
Figure1b : Dimensional reduction Plots colored by cluster
Figure1c : Dimensional reduction Plots colored by no. of identified cells
Figure1d : ARI with a merge result and each animal(with MCMI)
The above functions can also be run by connecting them with R’s native pipe.
worm_download()$Ds |>
as_worm_tensor() |>
worm_membership(k=6) |>
worm_clustering() |>
worm_evaluate() |>
worm_visualize() -> objectIf 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_labelsFigure2a : Silhouette plots
Figure2b : Dimensional reduction Plots colored by cluster
Figure2c : Dimensional reduction Plots colored by no. of identified cells
Figure2d : ARI with a merge result and each animal(with MCMI)
Figure2e : Dimensional reduction Plots colored by label
Figure2f : Consistency of labels and cluster members
worm_distance helps you analyze your time-series data
matrices with WormTensor. worm_distance is a
function to convert time-series data matrices into distance matrices.
The distance matrices can be used for analysis by
WormTensor.
# Toy data (data of 3 animals)
n_cell_x <- 13
n_cell_y <- 24
n_cell_z <- 29
n_cells <- 30
n_time_frames <- 100
# animal_x : 13 cells, 100 time frames
animal_x <- matrix(runif(n_cell_x*n_time_frames),
nrow=n_cell_x, ncol=n_time_frames)
rownames(animal_x) <- sample(seq(n_cells), n_cell_x)
colnames(animal_x) <- seq(n_time_frames)
# animal_y : 24 cells, 100 time frames
animal_y <- matrix(runif(n_cell_y*n_time_frames),
nrow=n_cell_y, ncol=n_time_frames)
rownames(animal_y) <- sample(seq(n_cells), n_cell_y)
colnames(animal_y) <- seq(n_time_frames)
# animal_z : 29 cells, 100 time frames
animal_z <- matrix(runif(n_cell_z*n_time_frames),
nrow=n_cell_z, ncol=n_time_frames)
rownames(animal_z) <- sample(seq(n_cells), n_cell_z)
colnames(animal_z) <- seq(n_time_frames)
# Input list for worm_distnce
X <- list(animal_x=animal_x,
animal_y=animal_y,
animal_z=animal_z)
# Pipe Operation
# tsne.perplexity must be adjusted for data size
worm_distance(X, "mSBD") |>
as_worm_tensor() |>
worm_membership(k=6) |>
worm_clustering() |>
worm_evaluate() |>
worm_visualize(tsne.perplexity=5) -> object#> R Under development (unstable) (2022-07-07 r82559)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] WormTensor_0.1.1
#>
#> loaded via a namespace (and not attached):
#> [1] ade4_1.7-22 tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2
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#> [21] knitr_1.48 ggsignif_0.6.4 labeling_0.4.3 plyr_1.8.9
#> [25] abind_1.4-5 Rtsne_0.17 withr_3.0.0 purrr_1.0.2
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#> [93] nlme_3.1-158 mgcv_1.8-40 xfun_0.46 pkgconfig_2.0.3