selectParams: Selection of Contrastive and Penalization Parameters
In scPCA: Sparse Contrastive Principal Component Analysis

Description Usage Arguments Value References

A wrapper function for fitting various internal functions to select the optimal setting of the contrastive and penalization parameters. For internal use only.

selectParams(
  target,
  background,
  center,
  scale,
  n_eigen,
  alg,
  contrasts,
  penalties,
  clust_method,
  n_centers,
  max_iter,
  linkage_method,
  n_medoids,
  parallel,
  clusters,
  eigdecomp_tol,
  eigdecomp_iter
)

`target`	The target (experimental) data set, in a standard format such as a `data.frame` or `matrix`.
`background`	The background data set, in a standard format such as a `data.frame` or `matrix`. Note that the number of features must match the number of features in the target data.
`center`	A `logical` indicating whether the target and background data sets should be centered to mean zero.
`scale`	A `logical` indicating whether the target and background data sets should be scaled to unit variance.
`n_eigen`	A `numeric` indicating the number of eigenvectors (or sparse contrastive components) to be computed. The default is to compute two such eigenvectors.
`alg`	A `character` indicating the SPCA algorithm used to sparsify the contrastive loadings. Currently supports `iterative` for the \insertCitezou2006sparse;textualscPCA implementation, `var_proj` for the non-randomized \insertCiteerichson2018sparse;textualscPCA solution, and `rand_var_proj` for the randomized \insertCiteerichson2018sparse;textualscPCA result.
`contrasts`	A `numeric` vector of the contrastive parameters. Each element must be a unique non-negative real number. The default is to use 40 logarithmically spaced values between 0.1 and 1000.
`penalties`	A `numeric` vector of the L1 penalty terms on the loadings. The default is to use 20 equidistant values between 0.05 and 1.
`clust_method`	A `character` specifying the clustering method to use for choosing the optimal contrastive parameter. Currently, this is limited to either k-means, partitioning around medoids (PAM), and hierarchical clustering. The default is k-means clustering.
`n_centers`	A `numeric` giving the number of centers to use in the clustering algorithm. If set to 1, cPCA, as first proposed by Abid et al., is performed, regardless of what the `penalties` argument is set to.
`max_iter`	A `numeric` giving the maximum number of iterations to be used in k-means clustering, defaulting to 10.
`linkage_method`	A `character` specifying the agglomerative linkage method to be used if `clust_method = "hclust"`. The options are `ward.D2`, `single`, `complete`, `average`, `mcquitty`, `median`, and `centroid`. The default is `complete`.
`n_medoids`	A `numeric` indicating the number of medoids to consider if `n_centers` is set to 1. The default is 8 such medoids.
`parallel`	A `logical` indicating whether to invoke parallel processing via the BiocParallel infrastructure. The default is `FALSE` for sequential evaluation.
`clusters`	A `numeric` vector of cluster labels for observations in the `target` data. Defaults to `NULL`, but is otherwise used to identify the optimal set of hyperparameters when fitting the scPCA and the automated version of cPCA.
`eigdecomp_tol`	A `numeric` providing the level of precision used by eigendecompositon calculations. Defaults to `1e-10`.
`eigdecomp_iter`	A `numeric` indicating the maximum number of interations performed by eigendecompositon calculations. Defaults to `1000`.

Output structure matching either that of fitCPCA or fitGrid (or their parallelized variants, namely either bpFitCPCA and link{bpFitGrid}, respectively).

\insertAllCited

scPCA documentation built on Nov. 8, 2020, 6 p.m.