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EMA (version 1.4.7)

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')

Version

1.4.7

License

GPL-3

Maintainer

Pierre Gestraud

Last Published

February 14th, 2020

Functions in EMA (1.4.7)

foldchange

Compute foldchange
distrib.plot

Distribution plots of genes expression level
km

Compute survival curves and test difference between the curves
normAffy

Normalisation of Affymetrix expression arrays
keggReport

Text report from the result of the 'hyperGTest' function for KEGG pathway analysis
genes.selection

Genes selection
clustering.kmeans

Kmeans and hierarchical clustering
multiple.correction

Multiple testing correction
myPalette

Microarray color palette
marty.type.cl

marty class data for Basal vs HER2 cancer type
goReport

Text report from the 'hyperGTest' function
inverse

inverse
dice

Compute Dice distance on a data matrix
htmlheader

htmlheader
makeAllContrasts

Create all pairwise contrasts
eval.stability.clustering

Compares several clustering methods by means of its stability.
clustering

Agglomerative hierarchical clustering
clustering.plot

Clustering plots for one or two ways representation
runPCA

Perform an Principal Component Analysis
qualitySample

Sample quality computation in PCA
htmlresult

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

Compute Jaccard distance on a data matrix
expFilter

Filter expression data
ordinal.chisq

Chisq test for ordinal values
marty

marty data
plotBiplot

Sample and variable representation on a same graph for PCA
test.nested.model

Test for nested ANOVA models
runSAM

SAM analysis with siggenes package
runWilcox

Computing Multiple Wilcoxon Tests
sample.plot

barplot of genes expression level
plotVariable

Variable representation for Principal Component Analysis
test.LC

Test linear combinations of parameters of a linear model
runTtest

Computing Multiple Student Tests
runGSA

GSA analysis
setdiffg

Generalized version of setdiff for n objects
runMFA

Function to perform a Multiple Factor Analysis.
probePlots

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

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

Computing Differential Analysis for each gene
intersectg

Generalized version of intersect for n objects
plotInertia

Barplot of component inertia percentage for PCA
runHyperKEGG

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

Sample representation for Principal Component Analysis
PLS

Partial Least Squares
MFAreport

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

Computes distances on a data matrix
FWER.Bonf

Multiple testing correction using FWER
FDR.BH

FDR.BH
EMA-package

EMA - Easy Microarray Analysis
GSA.correlate.txt

Correlation between Genes collection and Genes Array
bioMartAnnot

Annotation of probesets using biomaRt
as.colors

Convert labels to colors