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EMA (version 1.4.3)

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

1.4.3

License

GPL-3

Maintainer

Pierre Gestraud

Last Published

August 5th, 2013

Functions in EMA (1.4.3)

genes.selection

Genes selection
FDR.BH

FDR.BH
GSA.correlate.txt

Correlation between Genes collection and Genes Array
dice

Compute Dice distance on a data matrix
distrib.plot

Distribution plots of genes expression level
EMA-package

EMA - Easy Microarray Analysis
sample.plot

barplot of genes expression level
runWilcox

Computing Multiple Wilcoxon Tests
intersectg

Generalized version of intersect for n objects
km

Compute survival curves and test difference between the curves
htmlresult

Html report from the result of the 'hyperGTest' function
htmlheader

htmlheader
test.LC

Test linear combinations of parameters of a linear model
clust.dist

Computes distances on a data matrix
myPalette

Microarray color palette
runTtest

Computing Multiple Student Tests
clustering.kmeans

Kmeans and hierarchical clustering
eval.stability.clustering

Compares several clustering methods by means of its stability.
bioMartAnnot

Annotation of probesets using biomaRt
runHyperKEGG

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

Text report from the 'hyperGTest' function
foldchange

Compute foldchange
runIndTest

Computing Differential Analysis for each gene
expFilter

Filter expression data
clustering.plot

Clustering plots for one or two ways representation
multiple.correction

Multiple testing correction
runGSA

GSA analysis
keggReport

Text report from the result of the 'hyperGTest' function for KEGG pathway analysis
marty

marty data
marty.type.cl

marty class data for Basal vs HER2 cancer type
plotVariable

Variable representation for Principal Component Analysis
qualitySample

Sample quality computation in PCA
runMFA

Function to perform a Multiple Factor Analysis.
makeAllContrasts

Create all pairwise contrasts
FWER.Bonf

Multiple testing correction using FWER
plotSample

Sample representation for Principal Component Analysis
runHyperGO

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

inverse
test.nested.model

Test for nested ANOVA models
plotInertia

Barplot of component inertia percentage for PCA
ordinal.chisq

Chisq test for ordinal values
as.colors

Convert labels to colors
PLS

Partial Least Squares
plotBiplot

Sample and variable representation on a same graph for PCA
MFAreport

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

Generalized version of setdiff for n objects
runPCA

Perform an Principal Component Analysis
jaccard

Compute Jaccard distance on a data matrix
normAffy

Normalisation of Affymetrix expression arrays
clustering

Agglomerative hierarchical clustering
probePlots

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

SAM analysis with siggenes package