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.

Author
Shannon M. Bell <bell.shannonm@gmail.com>, Lyle D. Burgoon <burgoon.lyle@epa.gov>
Date of publication
2012-01-27 11:27:41
Maintainer
Shannon M. Bell <bell.shannonm@gmail.com>
License
GPL (>= 3)
Version
1.2

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Man pages

cdf.pval
Generate Empirical pvalues from Cumulative Distribution...
find_hits
Identification of putative hits using Zvalues or MIPHENO...
mad.scores
Calculates the mad score (zscore)
rm.outliers
Post-Hoc outlier removal for high throughput data

Files in this package

MIPHENO
MIPHENO/MD5
MIPHENO/R
MIPHENO/R/rm.outliers.R
MIPHENO/R/mad.scores.R
MIPHENO/R/find_hits.R
MIPHENO/R/cdf.pval.R
MIPHENO/NAMESPACE
MIPHENO/man
MIPHENO/man/rm.outliers.Rd
MIPHENO/man/mad.scores.Rd
MIPHENO/man/find_hits.Rd
MIPHENO/man/cdf.pval.Rd
MIPHENO/DESCRIPTION