SingleCell

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

SingleCell function

Data frames with 51516 rows and 96 columns.
  <dl>
    <dt>Details</dt><dd>This is single-cell RNA-seq data from which counts were generated when using RNA-seq model free approach.</dd> </dl> 

SingleCell: Single-cell RNA-seq data for model free simulation

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