datasets

EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527).

Chia Kuan Hui Burton

EDDA

Experimental Design in Differential Abundance analysis

Wu function

Data frames with 18982 observations on the following 2 variables.
  <dl>
    <dt><code>gene</code></dt><dd>a character vector</dd> <dt><code>expression</code></dt><dd>a numeric vector</dd> </dl> 

Wu: Average abundance for RNA-seq data from schizophrenia.

Description

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References