LPE: Methods for analyzing microarray data using Local Pooled Error (LPE) method
Version 1.50.0

This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library.

Browse man pages Browse package API and functions Browse package files

AuthorNitin Jain <emailnitinjain@gmail.com>, Michael O'Connell <michaelo@warath.com>, Jae K. Lee <jaeklee@virginia.edu>. Includes R source code contributed by HyungJun Cho <hcho@virginia.edu>
Bioconductor views DifferentialExpression Microarray
Date of publicationNone
MaintainerNitin Jain <emailnitinjain@gmail.com>
LicenseLGPL
Version1.50.0
URL http://www.r-project.org http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ http://sourceforge.net/projects/r-lpe/
Package repositoryView on Bioconductor
InstallationInstall the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("LPE")

Man pages

am.trans: Transform replicated arrays into (A,M) format
baseOlig.error: Evaluates LPE variance function of M for quantiles of A...
baseOlig.error.step1: Evaluates LPE variance function of M for quantiles of A...
baseOlig.error.step2: Evaluates LPE variance function of M for quantiles of A...
fdr.adjust: FDR adjustment procedures
fixbounds.predict.smooth.spline: Makes the predicted variance non negative
iqr: Inter-quartile range
Ley: Gene Expression Data from Mouse Immune response study, (2002)
lowess.normalize: lowess normalization of the data (based on M vs A graph)
lpe: Evaluates local pooled error significance test
mt.rawp2adjp: Adjusted p-values for simple multiple testing procedures
n.genes.adaptive.int: Calcuates the number of genes in various intervals...
permute: Calculating all possible permutations of a vector
preprocess: Preprocessing the data (IQR normalization, thresholding, log-...
quan.norm: Finding quartile range
quartile.normalize: Normalization based on quartile range
resamp.adj: Resampling based fdr adjustment

Functions

Ley Man page
am.trans Man page Source code
baseOlig.error Man page Source code
baseOlig.error.step1 Man page Source code
baseOlig.error.step2 Man page Source code
fdr.adjust Man page Source code
fixbounds.predict.smooth.spline Man page Source code
iqr Man page Source code
lowess.normalize Man page Source code
lpe Man page Source code
mt.rawp2adjp.LPE Man page Source code
n.genes.adaptive.int Man page Source code
permute Man page Source code
preprocess Man page Source code
quan.norm Man page Source code
quartile.normalize Man page Source code
resamp.adj Man page Source code

Files

DESCRIPTION
NAMESPACE
NEWS
R
R/am.trans.R
R/baseOlig.error.R
R/baseOlig.error.step1.R
R/baseOlig.error.step2.R
R/fdr.adjust.R
R/fixbounds.predict.smooth.spline.R
R/iqr.R
R/lowess.normalize.R
R/lpe.R
R/mt.rawp2adjp.R
R/n.genes.adaptive.int.R
R/permute.R
R/preprocess.R
R/quan.norm.R
R/quartile.normalize.R
R/resamp.adj.R
TODO
build
build/vignette.rds
data
data/Ley.RData
inst
inst/doc
inst/doc/LPE.R
inst/doc/LPE.Rnw
inst/doc/LPE.pdf
man
man/Ley.Rd
man/am.trans.Rd
man/baseOlig.error.Rd
man/baseOlig.error.step1.Rd
man/baseOlig.error.step2.Rd
man/fdr.adjust.Rd
man/fixbounds.predict.smooth.spline.Rd
man/iqr.Rd
man/lowess.normalize.Rd
man/lpe.Rd
man/mt.rawp2adjp.Rd
man/n.genes.adaptive.int.Rd
man/permute.Rd
man/preprocess.Rd
man/quan.norm.Rd
man/quartile.normalize.Rd
man/resamp.adj.Rd
vignettes
vignettes/LPE.Rnw
vignettes/isorot.sty
LPE documentation built on May 20, 2017, 10:29 p.m.