Wrapper function to run `cell2cell_tensor` decomposition on a prebuilt tensor.
Source:R/liana_tensor.R
decompose_tensor.RdWrapper function to run `cell2cell_tensor` decomposition on a prebuilt tensor.
Usage
decompose_tensor(
prebuilt_tensor,
rank = NULL,
tf_optimization = "robust",
seed = 1337,
upper_rank = 25,
elbow_metric = "error",
smooth_elbow = FALSE,
init = "svd",
svd = "numpy_svd",
factors_only = TRUE,
verbose = TRUE,
...
)Arguments
- prebuilt_tensor
Tensor-cell2cell Prebuilt.Tensor class instance
- rank
Ranks for the Tensor Factorization (number of factors to deconvolve the original tensor). If NULL, then rank selection is performed using the `elbow_rank_selection` function.
- tf_optimization
indicates whether running the analysis in the `'regular'` or the `'robust'` way. The regular way means that the tensor decomposition is run 10 times per rank evaluated in the elbow analysis, and 1 time in the final decomposition. Additionally, the optimization algorithm has less number of iterations in the regular than the robust case (100 vs 500) and less precision (tolerance of 1e-7 vs 1e-8). The robust case runs the tensor decomposition 20 times per rank evaluated in the elbow analysis, and 100 times in the final decomposition. Here we could use the `tf_optimization='regular'`, which is faster but generates less robust results. We recommend using `tf_optimization='robust`, which takes longer to run (more iteractions and more precise too).
- seed
Random seed integer
- upper_rank
Upper bound of ranks to explore with the elbow analysis.
- init
Initialization method for computing the Tensor Factorization. ‘svd’, ‘random’
- factors_only
whether to return only the factors after factorization
- verbose
verbosity logical
- ...
Dictionary containing keyword arguments for the c2c.compute_tensor_factorization function. The function deals with `random_state` (seed) and `rank` internally.