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I started this list in 2001. Since then, most R packages for genomic analysis have now come together under the roof of the Bioconductor project. For that reason, and because of time constraints on my side, I will not update this list any more (last change July 2005). For a well-organized list of the packages available in Bioconductor see the BioC task list (version 1.8). Also helpful should be the CRAN Multivariate Statistics task view.


This list contains R packages, and software based on the R system, to analyze gene expression data from DNA array experiments, both for olignonucleotide chips and cDNA microarrays. Please drop me a line ( if there are any inaccuracies or to suggest other packages that should be listed here.


General-purpose Packages:

Package Description Author Contact Version License
(Stable and
development packages)
Collaborative open-source project to develop a modular general framework for the analysis of cDNA arrays and gene chips. Includes and unifies some of the packages below. R. Gentleman
(and many others)
FunDaMiner All-purpose package for gene expression analysis. Collection of various methods from preprocesssing over differential expression and multiple testing to clustering and classification. Michael T. Mader 1.0.1 GNU GPL
OOMAL Object-Oriented Microarray Analysis Library implemented in S-Plus. ? ? 4.0 No commercial use
DNAMR Companion R package to "Exploration and Analysis of DNA Microarray and Protein Array Data" book. J. Cabrera cabrera@
0.1 GPL
SMIDA Companion R package to "Statistics for Microarrays" book. J. McClure and E. Wit johndm@
0.1 GPL


Specialized Packages: Differential Expression (Error Models)

Package Description Author Contact Version License
LPE Local pooled error (LPE) test for gene expression data with a small number of replicates (2-3). N. Jain nitin.jain@
1.1.5 GNU GPL
HEM Heterogeneous Error Model for Analysis of Microarray Data. HJ. Cho 1.0.2 GNU GPL
SMA Data import from GenePIX and SPOT image programs, data management utilities, normalization and differential expression, diagonal discriminant analysis, various plot functions etc. Includes some mouse data. S. Dudoit
(and others)
0.5.13 GNU GPL
vsn Variance stabilization and calibration for microarray data. W. Huber 1.5 GNU GPL
YASMA ANOVA analysis, filtering and interpolation functions. Includes some tuberculosis data. L. Wernisch
(and others)
0.20 GNU GPL
maanova Analysis of two-dye Micro Array experiment (using ANOVA, permutation and bootstrap, cluster and consensus tree). Hao Wu 0.91-3 GPL 2
LIMMA Linear models for microarray data. G. Smyth 1.8.9 GNU GPL
DEDS Various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. Y. Xiao and J. Yang jean@biostat.
1.03 GNU GPL


Specialized Packages: Differential Expression (Empirical Bayes and FDR)

Package Description Author Contact Version License
siggenes Significance Analysis of Microarrays (SAM) and Empirical Bayes Analyses of Microarrays (EBAM). H. Schwender holger.schw@
1.2.11 Free for non-commercial use
EBarrays Empirical Bayes methods for microarray analysis. C. Kendziorski
and M. Newton
1.0-19 GNU GPL
qvalue Several approaches to estimate the false discovery rate (FDR). J. Storey (and others)
fdrtool Estimation and Control of (Local) False Discovery Rates. K. Strimmer
1.1.0 GNU GPL
Improved estimates of local FDR to detect differentially expressed genes. S. Pounds stanley.pounds@
? ?
Decision-theoretic improvement of FDR (=dFDR) to detect differentially expressed genes. D. Bickel 1.0-2 GPL
locfdr Computation of local false discovery rates. B. Efron and B. Narasimhan brad@stat.
1.0-3 GPL
twilight Improved estimate of local FDR to detect differentially expressed genes. S. Scheid stefanie.scheid@
1.0.1 GNU GPL
LBE Improved estimate of local FDR to detect differentially expressed genes. C. Dalmasso (and others) dalmasso@
? ?
Improved estimate of local FDR from p-values to detect differentially expressed genes. J. Aubert (and others) ? ?
localFDR Estimation of local FDR from p-values using stochastic order models. J.G. Liao ? ?
FDVAR Variance estimation of FDR. A.B. Owen ? ?
OCplus Computes theoretical and empirical FDR, sensitivity, false positive rate and sample size requirements when selecting differentially expressed genes in simple microarray experiments. Y. Pawitan and A. Ploner alexander.ploner@meb.
1.2.0 GNU GPL
SAGx Statistical Analysis of the GeneChip. Includes methods for identifying differentially expressed genes (pava FDR). P. Broberg per.broberg@
1.5.2 GNU GPL


Specialized Packages: Networks

Package Description Author Contact Version License
GeneNet Modeling and inferring gene networks. K. Strimmer
(and others)
1.1.0 GNU GPL
GeneNT Testing of edges in gene networks with two-stage screening algorithm. D. Zhu
(and others) 1.0 GNU GPL


Specialized Packages: Other

Package Description Author Contact Version License
aroma Object-oriented microarray analysis package. H. Bengtsson 0.75 GNU GPL
pickgene Normalization, differential expression, simulation, etc. B. S. Yandell yandell@
1.0.0 GNU GPL
som Clustering using self-organizing maps. Also provides simple filtering and normalization functions. Includes yeast cell cycle data. J. Yan jyan@
0.3-4 GNU GPL
permax Permutation tests for microarray data (2-sample t-test, correlation test, etc.). R. J. Gray gray@
hdarray Bayesian t-tests for expression change. Part of GeneX/Cyber T. A. D. Long 3.70 No commercial use
ISIS Class discovery based on maximizing a discriminant score. A. von Heydebreck heydebre@
2.0 No commercial use
GeneClust Exploratory analysis of gene expression microarray data (S-Plus). Implements Gene Shaving. K.-A. Do
(and others)
1.0b11 No commercial use
affyR Analysis of data from Affymetrix oligonucleotide arrays. L. Gautier laurent@
0.3.3 No commercial use
maffy Routines for normalizing Affymetrix Oligonucleotide Arrays. M. Åstrand magnus.astrand@
tRMA Tools for microarray analysis: normalisation, differential expression, visualisation etc. P. Baker
(and others)
1.7.0 ?
POE Probability of expression (POE). An approach to the analysis of gene expression microarrays using three-component mixtures. G. Parmigiani
and E. Garrett
Li-Wong Model S-Plus Scripts for Li-Wong full and reduced model estimates. F. A. Wright fwright@bios.
? ?
PAM Sample classification from gene expression data, by the method of nearest shrunken centroids. R. Tibshirani
(and others)
1.24 ?
statomics Statistical analysis of genomic and proteomic data D. Bickel 0.2 GNU GPL
plsgenomics PLS analyses for genomics. A.-L. Bourlesteix boulesteix@stat.


Frontends Using R (Web-based or Windows-based):

Package Description Author Contact Version License
GeneX General platform for the analysis and comparison of gene expression data. NCGR and the Computational Genomics Group at the University of California, Irvine ? GNU LGPL
GeneTraffic General platform for the data management and the analysis of two-colour microarray experiments (requires Windows). IOBION Informatics LCC 2.5 Commercial
SNOMAD Various procedures for standardization and normalization of microarray data. C. Colantuoni
(and others) ? ?
Analysis of two-color microarray data (requires Windows). G. C. Tseng ? ?


General Packages:

Package Description Author Contact Version License
mva Classical multivariate analysis: principal component analysis, K-means, hierarchical clustering, factor analysis, etc.
R base package.
R Core Team R-core@
1.5.0 GNU GPL
modreg Modern regression analysis: Smoothing and Local Methods.
R base package.
R Core Team R-core@
1.5.0 GNU GPL
multiv Multivariate data analysis routines: hierarchical clustering, principal component analysis, Sammon's nonlinear mapping, correspondence analysis, K-means, etc. F. Murtagh 1.1-4 No commercial use
fastICA Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit. J. L.Marchini marchini@
1.1-4 GNU GPL
cluster Functions for (hierarchical) cluster analysis. P. Rousseeuw
(and others) 1.6-4 GNU GPL
cclust Convex clustering methods, including K-means algorithm and calculation of several indexes for finding the number of clusters in a data set. E. Dimitriadou 0.6-9 GNU GPL
tree Classification and regression trees. B. Ripley 1.0-12 GNU GPL
class Various functions for classification. B. Ripley 6.2-6 GNU GPL
nnet Feed-forward neural networks and multinomial log-linear models. B. Ripley 6.2-6 GNU GPL
mclust Model-based clustering and discriminant analysis, including hierarchical clustering and EM for parameterized Gaussian mixtures and Poisson noise. C. Fraley
(and others)
1.1-4 No commercial use
e1071 Functions for latent class analysis, support vector machines, fuzzy clustering, bagged clustering, etc. F. Leisch
(and others)
1.3-11 GNU GPL
dr Dimension reduction regression, incl. sliced inverse regression (SIR) S. Weisberg 1.0-3 GNU GPL
randomForest Classification based on a forest of classification trees using random inputs. A. Liaw andy_liaw@
3.4-4 GNU GPL
LogitBoost Classification with LogitBoost. M. Dettling dettling@stat.
kmethods Kernel based dimensionality reduction and clustering methods (ISOMAP etc). M. Kuss 0.1-1 GNU GPL