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Compute Self-Bidirectional Correlation from Transferred Cell Scores

Source: R/compute_self_bidirectional_corr.R
getTransferSelfBidirCorr.Rd

Given transferred cell scores for each cell type, compute the within-cell-type bidirectional correlation using self-kernel matrices. This function computes spatial autocorrelation within each cell type using transferred scores, complementing the cross-type analysis provided by getTransferBidirCorr().

Usage

getTransferSelfBidirCorr(
  tar_obj,
  transfer_cell_scores,
  sigma_choice,
  calculationMode = NULL,
  normalize_K = c("row_or_col", "sinkhorn_knopp", "none"),
  filter_kernel = TRUE,
  K_row_sum_cutoff = 0.005,
  K_col_sum_cutoff = 0.005,
  sigma_choice_tar = NULL,
  verbose = TRUE
)

Arguments

tar_obj

A CoProSingle or CoProMulti object containing self-kernel matrices and metadata needed for alignment. Must have self-kernel matrices computed using computeSelfKernel().

transfer_cell_scores

A named list of matrices, with one entry per cell type (names must be the cell type names). Each matrix should be cells-by-CCs, where rows are cell IDs and columns are CC_1, CC_2, ..., as returned by getTransferCellScores(agg_cell_type = FALSE).

sigma_choice

Numeric scalar specifying the sigma value of the self-kernel to use.

calculationMode

For CoProMulti objects only, either "perSlide" or "aggregate". Ignored for CoProSingle. Default "perSlide" if tar_obj is multi-slide.

normalize_K

Character; method for normalizing the kernel matrix, one of "row_or_col", "sinkhorn_knopp", or "none". Default "row_or_col".

filter_kernel

Logical; whether to filter the kernel matrix. Default TRUE.

K_row_sum_cutoff

Numeric; cutoff for row sums when normalizing kernel matrix. Default 5e-3.

K_col_sum_cutoff

Numeric; cutoff for column sums when normalizing kernel matrix. Default 5e-3.

sigma_choice_tar

Numeric; sigma value for target object self-kernel matrices. If NULL (default), uses sigma_choice. Not recommended for general use.

verbose

Logical; whether to print progress messages.

Value

A list with one element named paste0("sigma_", sigma_choice), whose value is a data.frame of results. For single-slide objects, the data.frame has columns sigmaValue, cellType, CC_index, selfBidirCorrelation. For multi-slide objects in perSlide mode, the data.frame additionally includes slideID. For aggregate mode, the correlation column is named aggregateSelfCorrelation.

Details

The self-bidirectional correlation is computed as the mean of two correlations: cor(t(K) %% A_w, A_w) and cor(A_w, K %% A_w), where A_w is the transferred cell score vector for a cell type and K is the self-kernel matrix for that cell type.

Examples

if (FALSE) { # \dontrun{
# Assuming you have a CoPro object with multiple cell types
# First compute standard workflow
object <- computeDistance(object)
object <- computeKernelMatrix(object, sigmaValues = c(0.01, 0.05, 0.1))

# Add self-distances and self-kernels
object <- computeSelfDistance(object)
object <- computeSelfKernel(object, sigmaValues = c(0.01, 0.05, 0.1))

# Compute transferred cell scores
trans_scores <- getTransferCellScores(ref_obj, tar_obj, sigma_choice = 0.05, 
                                     agg_cell_type = FALSE)

# Compute self-bidirectional correlation from transferred scores
self_bidir <- getTransferSelfBidirCorr(tar_obj, trans_scores, sigma_choice = 0.05)
} # }