get_ctype_vargenes: Partition main gene by cell matrix into per cell type...

In scITD: Single-Cell Interpretable Tensor Decomposition

Partition main gene by cell matrix into per cell type matrices with significantly variable genes only. Generally, this should be done through calling the form_tensor() wrapper function.

Description

Partition main gene by cell matrix into per cell type matrices with significantly variable genes only. Generally, this should be done through calling the form_tensor() wrapper function.

Usage

get_ctype_vargenes(
  container,
  method,
  thresh,
  ncores = container$experiment_params$ncores,
  seed = container$experiment_params$rand_seed
)

Arguments

container	environment Project container that stores sub-containers for each cell type as well as results and plots from all analyses
method	character The method used to select significantly variable genes across donors within a cell type. Can be either "anova" to use basic anova with cells grouped by donor or "norm_var" to get the top overdispersed genes by normalized variance. Set to "norm_var_pvals" to use normalized variance p-values as calculated in pagoda2.
thresh	numeric A pvalue threshold to use for gene significance when method is set to "anova" or "empir". For the method "norm_var" thresh is the number of top overdispersed genes from each cell type to include.
ncores	numeric The number of cores to use (default=container$experiment_params$ncores)
seed	numeric Seed passed to set.seed() (default=container$experiment_params$rand_seed)

Value

The project container with pseudobulk matrices limted to the selected most variable genes.

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