prep_LR_interact: Prepare data for LR analysis and get soft thresholds to use...

In scITD: Single-Cell Interpretable Tensor Decomposition

Prepare data for LR analysis and get soft thresholds to use for gene modules

Description

Prepare data for LR analysis and get soft thresholds to use for gene modules

Usage

prep_LR_interact(
  container,
  lr_pairs,
  norm_method = "trim",
  scale_factor = 10000,
  var_scale_power = 0.5,
  batch_var = NULL
)

Arguments

container	environment Project container that stores sub-containers for each cell type as well as results and plots from all analyses
lr_pairs	data.frame Data of ligand-receptor pairs. First column should be ligands and second column should be one or more receptors separated by an underscore such as receptor1_receptor2 in the case that multiple receptors are required for signaling.
norm_method	character The normalization method to use on the pseudobulked count data. Set to 'regular' to do standard normalization of dividing by library size. Set to 'trim' to use edgeR trim-mean normalization, whereby counts are divided by library size times a normalization factor. (default='trim')
scale_factor	numeric The number that gets multiplied by fractional counts during normalization of the pseudobulked data (default=10000)
var_scale_power	numeric Exponent of normalized variance that is used for variance scaling. Variance for each gene is initially set to unit variance across donors (for a given cell type). Variance for each gene is then scaled by multiplying the unit scaled values by each gene's normalized variance (where the effect of the mean-variance dependence is taken into account) to the exponent specified here. If NULL, uses var_scale_power from container$experiment_params. (default=.5)
batch_var	character A batch variable from metadata to remove (default=NULL)

Value

The project container with added container$scale_pb_extra slot that contains the tensor with additional ligands and receptors. Also has container$no_scale_pb_extra slot with pseudobulked, normalized data that is not scaled.

scITD documentation built on Sept. 8, 2023, 5:11 p.m.