form_tensor: Form the pseudobulk tensor as preparation for running the...

In scITD: Single-Cell Interpretable Tensor Decomposition

Form the pseudobulk tensor as preparation for running the tensor decomposition.

Description

Form the pseudobulk tensor as preparation for running the tensor decomposition.

Usage

form_tensor(
  container,
  donor_min_cells = 5,
  norm_method = "trim",
  scale_factor = 10000,
  vargenes_method = "norm_var",
  vargenes_thresh = 500,
  batch_var = NULL,
  scale_var = TRUE,
  var_scale_power = 0.5,
  custom_genes = NULL,
  verbose = TRUE
)

Arguments

container	environment Project container that stores sub-containers for each cell type as well as results and plots from all analyses
donor_min_cells	numeric Minimum threshold for number of cells per donor (default=5)
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)
vargenes_method	character The method by which to select highly variable genes from each cell type. Set to 'anova' to select genes by anova. Set to 'norm_var' to select the top genes by normalized variance or 'norm_var_pvals' to select genes by significance of their overdispersion (default='norm_var')
vargenes_thresh	numeric The threshold to use in variable gene selection. For 'anova' and 'norm_var_pvals' this should be a p-value threshold. For 'norm_var' this should be the number of most variably expressed genes to select from each cell type (default=500)
batch_var	character A batch variable from metadata to remove (default=NULL)
scale_var	logical TRUE to scale the gene expression variance across donors for each cell type. If FALSE then all genes are scaled to unit variance across donors for each cell type. (default=TRUE)
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)
custom_genes	character A vector of genes to include in the tensor. Overrides the default gene selection if not NULL. (default=NULL)
verbose	logical Set to TRUE to print out progress (default=TRUE)

Value

The project container with a list of tensor data added in the container$tensor_data slot.

Examples

test_container <- form_tensor(test_container, donor_min_cells=0,
norm_method='trim', scale_factor=10000, vargenes_method='norm_var', vargenes_thresh=500,
scale_var = TRUE, var_scale_power = 1.5)

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