completeFeatures: Determine the Number of Completely Observed Features

In mccallm/miRcomp-shiny: Tools to assess and compare miRNA expression estimatation methods

Description

This function determines the number of features that are good quality and non-NA across all samples using a given quality threshold.

Usage

completeFeatures(object1, qcThreshold1, object2=NULL, qcThreshold2=NULL,
label1=NULL, label2=NULL)

Arguments

object1	a list containing two elements: ct (the expression estiamtes) and qc (quality scores)
qcThreshold1	a numeric threshold corresponding to object1$qc below which values are considered low quality.
object2	an optional second list of the same format as object1, used to compare two methods.
qcThreshold2	a numeric threshold corresponding to object2$qc below which values are considered low quality.
label1	optional label corresponding to object 1 to be used in plotting.
label2	optional label corresponding to object 2 to be used in plotting.

Value

The function generates a table of the number of complete, partial, and absent features.

Author(s)

Matthew N. McCall

Examples

  data(lifetech)
  completeFeatures(object1=lifetech,qcThreshold1=1.25)
  data(qpcRdefault)
  completeFeatures(object1=lifetech,qcThreshold1=1.25,
           object2=qpcRdefault,qcThreshold2=0.99)

mccallm/miRcomp-shiny documentation built on May 29, 2019, 4:41 a.m.