MIPHENO: Mutant Identification through Probabilistic High throughput Enabled NOrmalization
This package contains functions to carry out processing of high throughput data analysis and detection of putative hits/mutants. Contents include a function for post-hoc quality control for removal of outlier sample sets, a median-based normalization method for use in datasets where there are no explicit controls and where most of the responses are of the wildtype/no response class (see accompanying paper). The package also includes a way to prioritize individuals of interest using am empirical cumulative distribution function. Methods for generating synthetic data as well as data from the Chloroplast 2010 project are included.
- Shannon M. Bell <email@example.com>, Lyle D. Burgoon <firstname.lastname@example.org>
- Date of publication
- 2012-01-27 11:27:41
- Shannon M. Bell <email@example.com>
- GPL (>= 3)
- Generate Empirical pvalues from Cumulative Distribution...
- Identification of putative hits using Zvalues or MIPHENO...
- Calculates the mad score (zscore)
- Post-Hoc outlier removal for high throughput data
Files in this package