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Compute how aligned the common component for a feature is with that feature in the original data modality

postprocess_modality_alignment.Rd

The common matrix for modality input_assay is extracted, and then compared against another matrix (depending on bool_use_denoised is set). (For example, if bool_use_denoised=TRUE, then this is the common plus distinct matrix.) Then, a regression is performed, one per feature (i.e., gene or protein) that regresses latter matrix onto the common matrix, and the R-squared (one per feature) is returned.

Usage

postprocess_modality_alignment(
  input_obj,
  bool_use_denoised,
  input_assay,
  bool_center = T,
  bool_scale = T,
  bool_regression_include_intercept = T,
  min_subsample_cell = NULL,
  seurat_celltype_variable = "celltype",
  seurat_obj = NULL,
  seurat_assay = NULL,
  seurat_slot = "data",
  verbose = 1
)

Arguments

input_obj

a multiSVD_obj that was the output of tiltedCCA::tiltedCCA_decomposition

bool_use_denoised

boolean. If TRUE, then the common component is compared against the common plus distinct component. If FALSE, then the common component is compared against the original data matrix in slot seurat_slot in seurat_obj[[seurat_assay]]

input_assay

integer of 1 or 2, denoting which modality is being analyzed

bool_center

boolean if all the features in the common component are centered prior to the comparison

bool_scale

boolean if all the features in the common component are rescaled prior to the comparison

bool_regression_include_intercept

boolean if the regression analysis

min_subsample_cell

if not NULL, subsample min_subsample_cell number of cells of each cell type (denoted by in seurat_obj$seurat_celltype_variable)

seurat_celltype_variable

a character where seurat_obj$seurat_celltype_variable denotes the cell type for each cell in seurat_obj

seurat_obj

the Seurat object that was used to compute input_obj, the multiSVD_obj

seurat_assay

the assay to extract the data matrix, which is relevant bool_use_denoised=FALSE

seurat_slot

the slot to extract the data matrix, which is relevant bool_use_denoised=FALSE

verbose

non-negative integer

Value

a vector of R-squared values for each variable