scITD (version 1.0.4)

get_min_sig_genes: Evaluate the minimum number for significant genes in any factor for a given number of factors extracted by the decomposition

Description

Evaluate the minimum number for significant genes in any factor for a given number of factors extracted by the decomposition

Usage

get_min_sig_genes(
  container,
  donor_rank_range,
  gene_ranks,
  use_lm = TRUE,
  tucker_type = "regular",
  rotation_type = "hybrid",
  n_fibers = 100,
  n_iter = 500,
  n.cores = container$experiment_params$ncores,
  thresh = 0.05
)

Value

The project container with a plot of the minimum significant genes for each decomposition with varying number of donor factors located in container$plots$min_sig_genes.

Arguments

container

environment Project container that stores sub-containers for each cell type as well as results and plots from all analyses. Should have

donor_rank_range

numeric Range of possible number of donor factors to use.

gene_ranks

numeric The number of gene ranks to use in the decomposition

use_lm

logical Set to true to use get_lm_pvals otherwise uses jackstraw (default=TRUE)

tucker_type

character Set to 'regular' to run regular tucker or to 'sparse' to run tucker with sparsity constraints (default='regular')

rotation_type

character Set to 'hybrid' to perform hybrid rotation on resulting donor factor matrix and loadings. Otherwise set to 'ica_lds' to perform ica rotation on loadings or ica_dsc to perform ica on donor scores. (default='hybrid')

n_fibers

numeric The number of fibers the randomly shuffle in each jackstraw iteration (default=100)

n_iter

numeric The number of jackstraw shuffling iterations to complete (default=500)

n.cores

Number of cores to use in get_lm_pvals() (default = container$experiment_params$ncores)

thresh

numeric Pvalue threshold for significant genes in calculating the number of significant genes identified per factor. (default=0.05)

Examples

test_container <- get_min_sig_genes(test_container, donor_rank_range=c(2:4),
gene_ranks=4, tucker_type='regular', rotation_type='hybrid', n.cores=1)