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EMA (version 1.4.4)

Easy Microarray data Analysis

Description

We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.

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Version

Install

install.packages('EMA')

Monthly Downloads

3

Version

1.4.4

License

GPL-3

Maintainer

Pierre Gestraud

Last Published

March 28th, 2014

Functions in EMA (1.4.4)

PLS

Partial Least Squares
normAffy

Normalisation of Affymetrix expression arrays
myPalette

Microarray color palette
as.colors

Convert labels to colors
jaccard

Compute Jaccard distance on a data matrix
makeAllContrasts

Create all pairwise contrasts
genes.selection

Genes selection
runTtest

Computing Multiple Student Tests
bioMartAnnot

Annotation of probesets using biomaRt
htmlresult

Html report from the result of the 'hyperGTest' function
FWER.Bonf

Multiple testing correction using FWER
probePlots

Plot the expression profiles of the probes corresponding to given probesets
runMFA

Function to perform a Multiple Factor Analysis.
expFilter

Filter expression data
runHyperGO

Run Gene Ontology analysis based on hypergeometric test from a probeset list
plotVariable

Variable representation for Principal Component Analysis
clust.dist

Computes distances on a data matrix
qualitySample

Sample quality computation in PCA
runIndTest

Computing Differential Analysis for each gene
runHyperKEGG

Run KEGG pathway analysis based on hypergeometric test from a probeset list
runPCA

Perform an Principal Component Analysis
multiple.correction

Multiple testing correction
EMA-package

EMA - Easy Microarray Analysis
setdiffg

Generalized version of setdiff for n objects
plotInertia

Barplot of component inertia percentage for PCA
FDR.BH

FDR.BH
clustering.kmeans

Kmeans and hierarchical clustering
GSA.correlate.txt

Correlation between Genes collection and Genes Array
foldchange

Compute foldchange
sample.plot

barplot of genes expression level
intersectg

Generalized version of intersect for n objects
km

Compute survival curves and test difference between the curves
ordinal.chisq

Chisq test for ordinal values
marty

marty data
plotSample

Sample representation for Principal Component Analysis
runSAM

SAM analysis with siggenes package
runGSA

GSA analysis
clustering.plot

Clustering plots for one or two ways representation
marty.type.cl

marty class data for Basal vs HER2 cancer type
test.LC

Test linear combinations of parameters of a linear model
dice

Compute Dice distance on a data matrix
test.nested.model

Test for nested ANOVA models
htmlheader

htmlheader
keggReport

Text report from the result of the 'hyperGTest' function for KEGG pathway analysis
distrib.plot

Distribution plots of genes expression level
plotBiplot

Sample and variable representation on a same graph for PCA
clustering

Agglomerative hierarchical clustering
runWilcox

Computing Multiple Wilcoxon Tests
eval.stability.clustering

Compares several clustering methods by means of its stability.
goReport

Text report from the 'hyperGTest' function
inverse

inverse
MFAreport

Function to create a txt and pdf report with the main statistics and graphics of the MFA.