wThreshold: Imputation Weighting Threshold

In VCCRI/CIDR: Clustering through Imputation and Dimensionality Reduction

Description

Determines the imputation weighting threshold.

Usage

## S4 method for signature 'scData'
wThreshold(object, cutoff = 0.5, plotTornado = FALSE)

Arguments

object	an scData class object.
cutoff	parameter in the range (0,1), used in the calculation of imputation weighting threshold. Default is 0.5.
plotTornado	Boolean; if TRUE, the Tornado Plot is produced.

Details

This method finds a function P(u) that maps the average expression level of a gene to the probability of a dropout occurring. It does this by fitting a negative logistic function to the empirical dropouts vs average expression data. The imputation weighting threshold is calculated as the value of u at which P(u) = cutoff.

Value

an updated scData class object with the following attribute updated

wThreshold	imputation weighting threshold.
pDropoutCoefA	the steepness parameter of the negative logistic function that fits the data.
pDropoutCoefB	the midpoint parameter of the negative logistic function that fits the data.

Examples

example(cidr)

VCCRI/CIDR documentation built on May 9, 2019, 9:41 p.m.