getRoc

getRoc,logical,logical-method

getRoc,numeric,numeric-method

getRoc,matrix,matrix-method

Binary vector or matrix indicating simulation truth
    (<code>FALSE</code> or 0 for non differential expression, <code>TRUE</code> or 1
    for differential expression)

simTruth

Binary vector or matrix with differential expression
    calls based on some data analysis.

decision


  <code>getRoc</code> compares simulation truth and data analysis results to
  determine False Positives (FP), False Negatives (FP), True Positives
  (TP), True Negatives (TN), Positives (FP+TP), False Discovery
  Proportion (FP/P) and Power (TP/(TP+FN)). 


 htest

Infer alternative splicing from paired-end RNA-seq data.
The model is based on counting paths across exons, rather than
pairwise exon connections, and estimates the fragment size and
start distributions non-parametrically, which improves
estimation precision.

David Rossell

casper

Characterization of Alternative Splicing based on Paired-End
Reads

getRoc function

<dl>
    <dt><code>signature(simTruth='logical',decision='logical')</code></dt><dd>
      Operating characteristics are computed for a single simulation
    </dd> <dt><code>signature(simTruth='numeric',decision='numeric')</code></dt><dd>
      Operating characteristics are computed for a single simulation
    </dd> <dt><code>signature(simTruth='matrix',decision='matrix')</code></dt><dd>
      <code>simTruth</code> and <code>decision</code> contain truth and calls for
      several simulations (in columns).
      <code>getRoc</code> returns a data.frame with operating characteristics
      in each simulation.
    </dd> </dl> 

Methods

<code>getRoc</code> compares simulation truth and data analysis results to
  determine False Positives (FP), False Negatives (FP), True Positives
  (TP), True Negatives (TN), Positives (FP+TP), False Discovery
  Proportion (FP/P) and Power (TP/(TP+FN)).

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getRoc: Operating characteristics of differential expression analysis

Description

Usage

Arguments

Value

Methods

Examples