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LPE (version 1.46.0)

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

Description

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.

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Version

Version

1.46.0

License

LGPL

Maintainer

Nitin Jain

Last Published

February 15th, 2017

Functions in LPE (1.46.0)

fixbounds.predict.smooth.spline

Makes the predicted variance non negative
mt.rawp2adjp.LPE

Adjusted p-values for simple multiple testing procedures
fdr.adjust

FDR adjustment procedures
iqr

Inter-quartile range
lpe

Evaluates local pooled error significance test
calculateLpeAdj

Evaluates local pooled error significance test with user chosen variance adjustments.
Ley

Gene Expression Data from Mouse Immune response study, (2002)
quan.norm

Finding quartile range
lowess.normalize

lowess normalization of the data (based on M vs A graph)
baseOlig.error.step1

Evaluates LPE variance function of M for quantiles of A within and experimental condition by divinding the A in 100 intervals.
adjBaseOlig.error.step2

Evaluates LPE variance function of M for quantiles of A within and experimental condition. It is based on the adaptive number of intervals.
quartile.normalize

Normalization based on quartile range
permute

Calculating all possible permutations of a vector
adjBaseOlig.error.step1

Evaluates LPE variance function of M for quantiles of A within and experimental condition by dividing the A in 100 intervals.
n.genes.adaptive.int

Calcuates the number of genes in various intervals adaptively.
lpeAdj

High level lpeAdj function that executes the adjusted local pooled error significance test. If more control over parameters is needed then see documentation for calculateLpeAdj.
resamp.adj

Resampling based fdr adjustment
baseOlig.error.step2

Evaluates LPE variance function of M for quantiles of A within and experimental condition. It is based on the adaptive number of intervals.
baseOlig.error

Evaluates LPE variance function of M for quantiles of A within and experimental condition and then interpolates it for all genes.
am.trans

Transform replicated arrays into (A,M) format
adjBaseOlig.error

Evaluates LPE variance function of M for quantiles of A within and experimental condition and then interpolates it for all genes.
preprocess

Preprocessing the data (IQR normalization, thresholding, log- transformation, and lowess normalization)