completeFeatures {miRcomp} | R Documentation

## Determine the Number of Completely Observed Features

### 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)

[Package miRcomp version 1.16.0 Index]