checkArgs: Check Arguments passed to the scPCA Function
In PhilBoileau/scPCA: Sparse Contrastive Principal Component Analysis

checkArgs

R Documentation

Check Arguments passed to the scPCA Function

Description

Checks whether or not the all arguments in the scPCA functions are input properly.

Usage

checkArgs(
  target,
  background,
  center,
  scale,
  n_eigen,
  contrasts,
  penalties,
  clust_method,
  linkage_method,
  clusters,
  eigdecomp_tol,
  eigdecomp_iter,
  n_centers,
  scaled_matrix
)

Arguments

`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`.
`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 to be computed.
`contrasts`	A `numeric` vector of the contrastive parameters.
`penalties`	A `numeric` vector of the penalty terms.
`clust_method`	A `character` specifying the clustering method to use for choosing the optimal constrastive parameter. Currently, this is limited to either k-means, partitioning around medoids (PAM), and hierarchical clustering. The default is k-means clustering.
`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`.
`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.
`eigdecomp_iter`	A `numeric` indicating the maximum number of interations performed by eigendecompositon calculations.
`n_centers`	A `numeric` giving the number of centers to use in the clustering algorithm. If set to 1, cPCA, as first proposed by \insertCiteerichson2018sparse;textualscPCA, is performed, regardless of what the `penalties` argument is set to.
`scaled_matrix`	A `logical` indicating whether to output a `ScaledMatrix` object. The centering and scaling procedure is delayed until later, permitting more efficient matrix multiplication and row or column sums downstream. However, this comes at the at the cost of numerical precision.