testPrior {RaceID}

Posterior check of the model

Description

This functions compares variance estimates obtained from the maximum a posterior estimate with a given prior to the data. The ratio between the predicted variance and the actual variance for a random subset of genes is computed across all pruned k nearest neighbourhoods.

Usage

testPrior(
  res,
  expData,
  gamma = c(0.2, 0.5, 1, 5, 1000),
  rseed = 12345,
  ngenes = 200,
  pvalue = 0.01,
  minN = 5,
  no_cores = NULL,
  x0 = 0,
  lower = 0,
  upper = 100
)

Arguments

res

List object with k nearest neighbour information returned by pruneKnn.

expData

Matrix of gene expression values with genes as rows and cells as columns. These values have to correspond to unique molecular identifier counts.

gamma

Vector of gamma-values to test for the Cauchy prior distribution. Default is c(0.2,0.5,1,5,1000). Large values correspond to weak priors (gamma=1000 corresponds to a maximum likelihood estimate).

rseed

Integer number. Random seed to enforce reproducible gene sampling. Default is 12345.

ngenes

Positive integer number. Randomly sampled number of genes (from rownames of expData) used for noise estimation. Genes are sampled uniformly across the entire expression range. Default is 200.

pvalue

Input parameter for compTBNoise. See help(compTBNoise).

minN

Input parameter for compTBNoise. See help(compTBNoise).

no_cores

Input parameter for compTBNoise. See help(compTBNoise).

x0

Input parameter for compTBNoise. See help(compTBNoise).

lower

Input parameter for compTBNoise. See help(compTBNoise).

upper

Input parameter for compTBNoise. See help(compTBNoise).

Value

List of three components:

pp.var.ratio

List of vectors for each gamma value of ratios between predicted and actual variances across all sampled genes and neighbourhoods.

noise

List of noise objects obtained from compTBNoise for each gamma value.

tc

Vector of total transcript counts for all cells