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DEDS (version 1.46.0)

Differential Expression via Distance Summary for Microarray Data

Description

This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach.

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Version

Version

1.46.0

License

LGPL

Maintainer

Yuanyuan Xiao

Last Published

February 15th, 2017

Functions in DEDS (1.46.0)

deds.chooseTest

Selection of the Most Common Statistics for Differential Expression
deds.genExtra

Generating Extra Parameters for Test Statistics Functions for Differential Expression
pairs-methods

Pairs Plot for DEDS Objects
deds.pval

Differential Expression via Distance Summary of p Values from Multiple Models
comp.stat

Computing Test Statistics for Differential Expression
topgenes

Table of Top Genes from DEDS
DEDS-internal

Internal DEDS functions
comp.ebayes

Computing Empirical Bayes Statistics for Differential Expression
deds.stat

Differential Expression via Distance Summary of Multiple Statistics
hist-methods

Histogram for DEDS Objects
comp.FC

Computing Fold Change for Differential Expression
comp.modF

Computing Moderated t-statistics for Differential Expression
comp.unadjp

Computing permutation based unadjusted p values for each row of a matrix
comp.t

Computing One and Two Sample t-statistic for Differential Expression
aggregateFun

Aggregate Statistical Functions for DEDS
qqnorm-methods

Normal Q-Q Plot for DEDS Objects
affySpikeIn

Gene expression dataset from Affymetrix Spike-in Experiments
comp.B

Computing B-statistics for Differential Expression
comp.adjp

Computing permutation based step-down maxT adjusted p values for each row of a matrix
comp.SAM

Computing SAM Statistics for Differential Expression
deds.stat.linkC

Differentail Expression via Distance Summary of Multiple Statistics
comp.F

Computing F-statistic for Differential Expression
ApoA1

Gene expression dataset from the ApoA1 Experiment
DEDS-class

DEDS Result List - class
comp.fdr

Computing permutation based q values controlling false discovery rate for each row of a matrix
comp.modt

Computing Moderated t-statistics for Differential Expression