scde.expression.prior: Estimate prior distribution for gene expression magnitudes

In hms-dbmi/scde: Single Cell Differential Expression

Estimate prior distribution for gene expression magnitudes

Description

Use existing count data to determine a prior distribution of genes in the dataset

Usage

scde.expression.prior(models, counts, length.out = 400, show.plot = FALSE,
  pseudo.count = 1, bw = 0.1, max.quantile = 1 - 0.001,
  max.value = NULL)

Arguments

models	models determined by scde.error.models
counts	count matrix
length.out	number of points (resolution) of the expression magnitude grid (default: 400). Note: larger numbers will linearly increase memory/CPU demands.
show.plot	show the estimate posterior
pseudo.count	pseudo-count value to use (default 1)
bw	smoothing bandwidth to use in estimating the prior (default: 0.1)
max.quantile	determine the maximum expression magnitude based on a quantile (default : 0.999)
max.value	alternatively, specify the exact maximum expression magnitude value

Value

a structure describing expression magnitude grid ($x, on log10 scale) and prior ($y)

Examples

data(es.mef.small)
cd <- clean.counts(es.mef.small, min.lib.size=1000, min.reads = 1, min.detected = 1)
data(o.ifm)  # Load precomputed model. Use ?scde.error.models to see how o.ifm was generated
o.prior <- scde.expression.prior(models = o.ifm, counts = cd, length.out = 400, show.plot = FALSE)

hms-dbmi/scde documentation built on April 19, 2023, 10:21 p.m.