Computing Consensus PCA

Usage

consensus_pca(
  mat_1,
  mat_2,
  dims_1,
  dims_2,
  dims_consensus,
  apply_pca = T,
  center_1 = T,
  center_2 = T,
  center_consensus = T,
  normalize_row = F,
  normalize_singular_value = T,
  recenter_1 = F,
  recenter_2 = F,
  recenter_consensus = F,
  rescale_1 = F,
  rescale_2 = F,
  rescale_consensus = F,
  scale_1 = T,
  scale_2 = T,
  scale_consensus = T,
  scale_max_1 = NULL,
  scale_max_2 = NULL,
  scale_max_consensus = NULL,
  svd_1 = NULL,
  svd_2 = NULL,
  tol = 0.001,
  verbose = 0
)

Arguments

mat_1: data matrix of n cells and p1 features for Modality 1
mat_2: data matrix of n cells and p2 features for Modality 2
dims_1: vector of latent dimensions for mat_1 used for analysis
dims_2: vector of latent dimensions for mat_2 used for analysis
dims_consensus: vector of latent dimensions for the Consensus PCA
apply_pca: boolean, where PCA is combined set of latent dimensions (from both modalities) if T or not if F
center_1: boolean, to center each the feature in Modality 1 prior to computing latent dimensions
center_2: boolean, to center each the feature in Modality 2 prior to computing latent dimensions
center_consensus: boolean, to center the combined set of latent dimensions (from both modalities)
normalize_row: boolean, to normalize each cell's latent vector after dimension-reduction for both modalities
normalize_singular_value: boolean, to normalize each modality by its largest singular value
recenter_1: boolean, to center each latent dimension in Modality 1 after computing latent dimensions
recenter_2: boolean, to center each latent dimension in Modality 2 after computing latent dimensions
recenter_consensus: boolean, to center the latent dimension after Consensus PCA
rescale_1: boolean, to rescale each latent dimension in Modality 1 after computing latent dimensions
rescale_2: boolean, to rescale each latent dimension in Modality 2 after computing latent dimensions
rescale_consensus: boolean, to rescale the latent dimension after Consensus PCA
scale_1: boolean, to rescale each the feature in Modality 1 prior to computing latent dimensions
scale_2: boolean, to rescale each the feature in Modality 2 prior to computing latent dimensions
scale_consensus: boolean, to rescale the combined set of latent dimensions (from both modalities)
scale_max_1: numeric or NULL, to threshold Modality 1 in magnitude prior to computing latent dimensions
scale_max_2: numeric or NULL, to threshold Modality 2 in magnitude prior to computing latent dimensions
scale_max_consensus: numeric or NULL, to threshold the combined set of latent dimensions in magnitude prior to computing latent dimensions
svd_1: list of u, d, v for the SVD of Modality 1 if it's already computed
svd_2: list of u, d, v for the SVD of Modality 2 if it's already computed
tol: small positive number
verbose: non-negative integer

Value

consensusPCA object