Reproducibility assessment is essential in extracting reliable scientific insights from high-throughput experiments. While the Irreproducibility Discovery Rate (IDR) method has been instrumental in assessing reproducibility, its standard implementation is constrained to handling only two replicates. Package 'eCV' introduces an enhanced Coefficient of Variation (eCV) metric to assess the likelihood of omic features being reproducible. Additionally, it offers alternatives to the Irreproducible Discovery Rate (IDR) calculations for multi-replicate experiments. These tools are valuable for analyzing high-throughput data in genomics and other omics fields. The methods implemented in 'eCV' are described in Gonzalez-Reymundez et al., (2023) <doi:10.1101/2023.12.18.572208>.
Package details |
|
---|---|
Author | Agustin Gonzalez-Reymundez [aut, cre] |
Maintainer | Agustin Gonzalez-Reymundez <agustin.gonrey@eclipsebio.com> |
License | GPL (>= 3) |
Version | 0.0.2 |
URL | https://github.com/eclipsebio/eCV |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.