EMA: Easy Microarray Data Analysis

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.

AuthorNicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe
Date of publication2016-09-07 13:01:55
MaintainerPierre Gestraud <pierre.gestraud@curie.fr>
LicenseGPL-3
Version1.4.5

View on CRAN

Man pages

as.colors: Convert labels to colors

bioMartAnnot: Annotation of probesets using biomaRt

clust.dist: Computes distances on a data matrix

clustering: Agglomerative hierarchical clustering

clustering.kmeans: Kmeans and hierarchical clustering

clustering.plot: Clustering plots for one or two ways representation

dice: Compute Dice distance on a data matrix

distrib.plot: Distribution plots of genes expression level

EMA-package: EMA - Easy Microarray Analysis

eval.stability.clustering: Compares several clustering methods by means of its...

expFilter: Filter expression data

FDR.BH: FDR.BH

foldchange: Compute foldchange

FWER.Bonf: Multiple testing correction using FWER

genes.selection: Genes selection

goReport: Text report from the 'hyperGTest' function

GSA.correlate.txt: Correlation between Genes collection and Genes Array

htmlheader: htmlheader

htmlresult: Html report from the result of the 'hyperGTest' function

intersectg: Generalized version of intersect for n objects

inverse: inverse

jaccard: Compute Jaccard distance on a data matrix

keggReport: Text report from the result of the 'hyperGTest' function for...

km: Compute survival curves and test difference between the...

makeAllContrasts: Create all pairwise contrasts

marty: marty data

marty.type.cl: marty class data for Basal vs HER2 cancer type

MFAreport: Function to create a txt and pdf report with the main...

multiple.correction: Multiple testing correction

myPalette: Microarray color palette

normAffy: Normalisation of Affymetrix expression arrays

ordinal.chisq: Chisq test for ordinal values

plotBiplot: Sample and variable representation on a same graph for PCA

plotInertia: Barplot of component inertia percentage for PCA

plotSample: Sample representation for Principal Component Analysis

plotVariable: Variable representation for Principal Component Analysis

PLS: Partial Least Squares

probePlots: Plot the expression profiles of the probes corresponding to...

qualitySample: Sample quality computation in PCA

runGSA: GSA analysis

runHyperGO: Run Gene Ontology analysis based on hypergeometric test from...

runHyperKEGG: Run KEGG pathway analysis based on hypergeometric test from a...

runIndTest: Computing Differential Analysis for each gene

runMFA: Function to perform a Multiple Factor Analysis.

runPCA: Perform an Principal Component Analysis

runSAM: SAM analysis with siggenes package

runTtest: Computing Multiple Student Tests

runWilcox: Computing Multiple Wilcoxon Tests

sample.plot: barplot of genes expression level

setdiffg: Generalized version of setdiff for n objects

test.LC: Test linear combinations of parameters of a linear model

test.nested.model: Test for nested ANOVA models

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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