Run tensor PLS-DA analysis — tplsda • tensorOmics

Note: always returns a compressed-matrix form output.

tplsda(
  x,
  y,
  multi_label = FALSE,
  ncomp = NULL,
  m = NULL,
  minv = NULL,
  center = TRUE,
  bpparam = NULL
)

Arguments

x: Tensor input.
y: A vector / column matrix / column tensor with n class labels, or a n x t matrix / n x 1 x t tensor if multi_label == TRUE.
multi_label: Set to TRUE if y contains repeated class measurements across the t timepoints specified in the x tensor.
ncomp: The estimated number of components. ncomp must be explicitly set as an integer in tpls.
m: A function which applies an orthogonal tensor tubal transform.
minv: The inverse of m.
center: If set to false, the data tensor will not be centralized into Mean Deviation Form (see Mor et al. 2022). By default, the mean horizontal slice of the input tensor(s) are subtracted, so that all of the horizontal slices sum to 0, analgous to centering matrix data.
bpparam: A BiocParallelParam-class object indicating the type of parallelisation. Does not have any effect if transform functions explicitly set using m, minv.

Adds y_original: the original labels input (vector/matrix/tensor) passed to tplsda().

Developed @ Melbourne Integrative Genomics

Brendan Lu