get_log_p_D_KL_continuous: Estimates the significance of the observed Kullback-Leibler...

In singleCellHaystack: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data

Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations for the continuous version of haystack.

Description

Estimates the significance of the observed Kullback-Leibler divergence by comparing to randomizations for the continuous version of haystack.

Usage

get_log_p_D_KL_continuous(
  D_KL.observed,
  D_KL.randomized,
  all.coeffVar,
  train.coeffVar,
  output.dir = NULL,
  spline.method = "ns"
)

Arguments

D_KL.observed	A vector of observed Kullback-Leibler divergences.
D_KL.randomized	A matrix of Kullback-Leibler divergences of randomized datasets.
all.coeffVar	Coefficients of variation of all genes. Used for fitting the Kullback-Leibler divergences.
train.coeffVar	Coefficients of variation of genes that will be used for fitting the Kullback-Leibler divergences.
output.dir	Optional parameter. Default is NULL. If not NULL, some files will be written to this directory.
spline.method	Method to use for fitting splines "ns" (default): natural splines, "bs": B-splines.

Value

A vector of log10 p values, not corrected for multiple testing using the Bonferroni correction.

singleCellHaystack documentation built on Dec. 28, 2022, 1:29 a.m.