Literature on Microarray Data
Analysis
Overview
The chipping forecast.
Special
supplement to Nature Genetics Vol. 21, 1999.
The chipping forecast II.
Special
supplement to Nature Genetics Vol. 32, 2002.
A
Brazma, J Vilo: Gene expression data analysis. FEBS Letters 480,
17-24, 2000.
W Huber, A v Heydebreck, M Vingron:
Analysis of microarray gene expression data. To appear.
K
Kerr and G Churchill: Statistical Design and the Analysis of Gene Expression
Microarrays. Genetical Research, 77:123-128. 2001.
Experimental design, normalization
T
Beissbarth, K Fellenberg, B Brors, et al.: Processing and quality control
of DNA array hybridization data, Bioinformatics 16, 1014-1022, 2000.
Bolstad, B.M., Irizarry RA, Astrand, M, and Speed, TP (2003): A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics, 19, 185-193.
E Chudin, R Walker, A Kosaka, Sue Wu, D Rabert, TK Chang, DE Kreder:
Assessment of the relationship between signal intensities and transcript concentration for
Affymetrix GeneChip arrays. Genome Biology, 3(1): research0005.1-0005.10, 2001.
B Durbin, J Hardin, D Hawkins, DM Rocke: A Variance-Stabilizing Transformation for Gene-Expression Microarray Data. Bioinformatics, Vol.18, Supplement 1, S105-110, 2002.
AA Hill, EL Brown, MZ Whitley, Greg Tucker-Kellogg, CP Hunter, DK Slonim:
Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA
controls. Genome Biology, 3(1): research0055.1-0055.13, 2001.
W Huber, A v Heydebreck, H Sültmann, A Poustka, M Vingron: Variance
Stabilization Applied to Microarray Data Calibration and to the Quantification
of Differential Expression. Bioinformatics, Vol.18, Supplement 1, S96-S104, 2002.
W Huber, A von Heydebreck, H Sültmann, A Poustka, M Vingron:
Parameter estimation for the calibration and variance stabilization of microarray data.
Statistical Applications in Genetics and Molecular Biology, to appear.
Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2003) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. Vol. 4, Number 2: 249-264.
Irizarry, RA, Bolstad BM, Collin, F, Cope, LM, Hobbs, B, and, Speed, TP (2003): Summaries of Affymetrix GeneChip Probe Level Data. Nucleic Acids Research Vol. 31, No. 4e15.
TB Kepler, L Crosby and KT Morgan: Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biology 2002 3:research0037.1-0037.12.
MK
Kerr, G Churchill: Experimental design for gene expression microarrays.
Biostatistics, 2:183-201, 2001.
C Li, and WH Wong:
Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.
PNAS 98:31-36, 2001.
J Schuchhardt, D Beule et al.: Normalization strategies for cDNA microarrays.
Nucleic Acids Research, Vol. 28, No.10, e47, 2000.
G.C. Tseng, Oh MK, Rohlin
L, Liao JC, Wong WH.: Issues in cDNA microarray analysis: quality filtering,
channel normalization, models of variations and assessment of gene effects.
Nucleic Acids Res. 2001 Jun 15;29(12):2549-57.
YH Yang, S Dudoit, P Luu, DM Lin, V Peng, J Ngai and TP Speed.
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucl. Acids Res. 30(4):e15, 2002.
YH Yang and TP Speed.
Design issues for cDNA microarray experiments. Nat. Rev. Gen. 3, 579 - 588, 2002.
Identification of differentially
expressed genes
KA Baggerly; KR Coombes; KR Hess; DN Stivers; LV Abruzzo; W Zhang:
Identifying Differentially Expressed Genes in cDNA Microarray Experiments.Journal of Computational Biology 8(6),
639-659, 2001.
P.
Baldi, A.D. Long: A Bayesian framework for the analysis of microarray expression
data: regularized t -test and statistical inferences of gene changes. Bioinformatics
2001 17: 509-519.
A.
J. Butte, J.Ye, G. Niederfellner, K. Rett, H.U. Häring, M.F.White,
and I. S. Kohane: Determining Significant Fold Differences in Gene Expression
Analysis. Pacific Symposium on Biocomputing 6:6-17 (2001).
Y. Chen, ER Dougherty, and
ML Bittner: Ratio based decisions and the quantitative analysis of cDNA
microarray images, J. of Biomedical Optics 2(4), 364-374,1997.
JM Claverie: Computational methods for the identification of differential and coordinated gene expression.
Human
Molecular Genetics, Vol. 8, No. 10, 1821-1832, 1999.
RO Dror, JG Murnick, NJ Rinaldi, VD Marinescu, RM Rifkin, and RA Young:
A Bayesian approach to transcript estimation from gene array data. Proceedings of RECOMB 2002.
S.
Dudoit, Y.H. Yang, M. J. Callow and T.P.Speed: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray
experiments. Statistica Sinica 12:111-139, 2002.
S Dudoit, JP Shaffer, JC Boldrick: Multiple Hypothesis Testing in Microarray Experiments.Statistical Science Vol. 18, No. 1, p. 71-103, 2003.
Efron B, Tibshirani R, Storey JD, and Tusher V. (2001) Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association, 96: 1151-1160.
R Herwig, P Aanstad, M Clark and H Lehrach:
Statistical evaluation of differential expression on cDNA nylon arrays with replicated
experiments. Nucleic Acids Research, Vol. 29, No. 23 e117, 2001.
T. Ideker; V. Thorsson; A.F.
Siegel; L.E. Hood: Testing for Differentially-Expressed Genes by Maximum-Likelihood
Analysis of Microarray Data. Journal of Computational Biology 7(6),
805-818, 2000.
ML Ting Lee, FC Kuo, GA Whitmore, and J Sklar: Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci U S A.97:9834-9839, 2000.
I Lönnstedt, T Speed: Replicated microarray data. Preprint (to appear in Statistica Sinica), 2001.
E
Manducchi et al.: Generation of patterns from gene expression data by
assigning confidence to differentially expressed genes. Bioinformatics
, Vol.16, no. 8, 685-698, 2000.
KS Pollard and M van der Laan: Resampling-based Multiple Testing: Asymptotic Control of Type I Error and Applications to Gene Expression Data. 2003.
M.A. Newton; C.M. Kendziorski;
C.S. Richmond; F.R. Blattner; K.W. Tsui: On Differential Variability of
Expression Ratios: Improving Statistical Inference about Gene Expression
Changes from Microarray Data. Journal of Computational Biology 8(1),
37-52, 2001.
W Pan: A comparative review of statistical methods for discovering differentially expressed genes in replicated
microarray experiments. Bioinformatics
, Vol.18, no. 4, 546-554, 2002.
Reiner A, Yekutieli D, Benjamini Y.: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics Vol. 19,368-375, 2003.
Storey JD and Tibshirani R. (2001) Estimating false discovery rates under dependence, with applications to DNA microarrays. Technical Report 2001-28, Department of Statistics, Stanford University.
Storey JD and Tibshirani R. (2003): SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays. In: The Analysis of Gene Expression Data: Methods and Software, by G Parmigiani, ES Garrett, RA Irizarry and SL Zeger (editors). Springer, New York.
J Theilhaber; S Bushnell; A Jackson; R Fuchs:
Bayesian Estimation of Fold-Changes in the Analysis of Gene Expression: The PFOLD Algorithm.
Journal of Computational Biology 8(6),
585-614, 2001.
JG Thomas, JM Olson, SJ Tapscott,and LP Zhao: An Efficient and Robust
Statistical Modeling Approach to Discover Differentially Expressed Genes Using Genomic Expression Profiles. Genome Research 11:1227-1236, 2001.
VG Tusher, R Tibshirani, and G Chu:
Significance analysis of microarrays applied to the
ionizing radiation response. Proc Natl Acad Sci U S A, Vol. 98, 9, 5116-5121 (2001).
J
Wittes, HP Friedman: Searching for Evidence of Altered Gene Expression:
a Comment on Statistical Analysis of Microarray Data. Journal of the
National Cancer Institute, Vol. 91, No. 5, 400-401, March 3, 1999.
H Yue, P Scott Eastman: An evaluation of the performance of cDNA
microarrays for detecting changes in global mRNA expression. Nucleic Acids Research, Vol. 29, No. 8 e41, 2001.
Classification
C Ambroise and GJ McLachlan: Selection bias in gene extraction on the basis of microarray
gene-expression data. Proc. Natl. Acad. Sci. USA, Vol. 99, 6562-6566, 2002.
A Ben-Dor, L Bruhn, N Friedman,
I Nachman, M Schummer, Z Yakhini: Tissue classification with gene expression
profiles. Journal of Computational Biology, 7, 559-583, 2000.
MP
Brown, D Haussler et al.:
Knowledge-based analysis of microarray gene expression data by using support vector
machines. Proc Natl Acad Sci U S A. 2000 Jan 4;97(1):262-7.
A Califano, G Stolovitzky,
Y Tu: Analysis of gene expression microarrays for phenotype classification.
Proceedings
of the Eighth International Conference on Intelligent Systems for Molecular
Biology (ISMB), 2000
S.
Dudoit, J. Fridlyand, and T. P. Speed: Comparison of Discrimination
Methods for the Classification of Tumors Using Gene Expression Data.
Journal of the American Statistical Association 97:77-87, 2002.
T
Furey, N Cristianini et al.: Support vector machine classification and
validation of cancer tissue samples using microarray expression data. Bioinformatics
16:906-914, 2000.
TR Golub et al.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene
Expression Monitoring, Science 286, 531-536 (Oct 1999).
Keller,
Schummer, Hood, Ruzzo: Bayesian Classification of DNA Array Expression
Data. Technical Report UW-CSE-2000-08-01, August, 2000.
R Spang, C Blanchette, H Zuzan, JR Marks, J Nevins and M West: Prediction and uncertainty in the analysis of gene expression profiles. Proceedings of the German Conference on Bioinformatics GCB 2001; E. Wingender, R. Hofestädt, I. Liebich (eds.); Braunschweig, 2001.
DV Nguyen and DM Rocke: Tumor classification by partial least squares using microarray gene expression data Bioinformatics
18 (1), 39-50, 2002.
R Tibshirani, T Hastie, B Narasimhan, and
G Chu: Diagnosis of multiple cancer types by shrunken centroids
of gene expression. Proc Natl Acad Sci U S A. 2002,99(10):6567-6572.
M West, JR Nevins, JR Marks, R Spang, C Blanchette, H Zuzan: DNA microarray data analysis and regression modeling for genetic expression profiling. ISDS Discussion Paper 00-15
M West, C Blanchette, H Dressman, E Huang, S Ishida, R Spang, H Zuzan, JA Olson Jr, JR Marks, JR Nevins: Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):11462-7.
M Xiong, X Fang, J Zhao: Biomarker identification by feature wrappers. Genome Research, 11:1878-1887, 2001.
Cluster analysis
U.
Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, A. J. Levine:
Broad patterns of gene expression revealed by clustering analysis of tumor and
normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad
Sci USA 96: 6745-6750, 1999.
Y.
Barash and N. Friedman: Context-Specific Bayesian Clustering for Gene Expression
Data. In Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology
(RECOMB), 2001.
A Ben-Dor, R Shamir, Z Yakhini:
Clustering
gene expression patterns. Journal of Computational Biolog6(3/4):
281-297, 1999.
AJ
Butte, P Tamayo et al.: Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks.
Proc
Natl Acad Sci USA, 97: 12182-12186, 2000.
DB Carr, R Somogyi and G Michaels:
Templates for Looking at Gene Expression Clustering, Statistical Computing & Graphics Newsletter, Vol. 8, No. 1, pp. 20-29, 1997.
ER Dougherty, J Barrera, M Brun, S Kim, RM Cesar, Y Chen, M Bittner, JM Trent:
Inference from clustering with application to gene-expression microarrays. Journal of Computational Biolog9:
105-126, 2002.
MB
Eisen, PT Spellman, PO Brown, D Botstein: Cluster analysis and display
of genome-wide expression patterns. Proc Natl Acad Sci USA, 95:14863-14868,
1998.
D Ghosh and AM Chinnaiyan: Mixture modelling of gene expression data from microarray experiments.
Bioinformatics
2002 18:275-286.
T
Hastie, R Tibshirani et al.: 'Gene shaving' as a method for identifying
distinct sets of genes with similar expression patterns. Genome Biology
2000, I(2): research0003.I-0003.21
Herwig,
R., Poustka, A. J., Müller, C., Bull, C., Lehrach, H., O'Brien, J.:
Large-Scale
Clustering of cDNA-Fingerprinting Data. Genome Res. 9: 1093-1105, 1999.
Katsuhisa Horimoto and Hiroyuki Toh: Statistical estimation of cluster boundaries in gene
expression profile data. Bioinformatics Vol. 17 no. 12, 1143-1151 , 2001.
MK
Kerr, G Churchill: Bootstrapping Cluster Analysis: Assessing the Reliability
of Conclusions from Microarray Experiments. Proc. Natl. Acad. Sci. USA, Vol. 98, Issue 16, p. 8961-8965, 2001.
A Schliep, A Schoenhuth and C Steinhoff: Using hidden Markov models to analyze gene expression time course data. Bioinformatics, Vol. 19 Suppl. 1, i255-i263, 2003.
GJ McLachlan, RW Bean and D Peel: A mixture model-based approach to the clustering
of microarray expression data. Bioinformatics, Vol. 18, no. 3, 413-422, 2002.
R Sharan, R Shamir: CLICK:
A clustering algorithm with applications to gene expression analysis.
Proceedings of the Eighth International Conference on Intelligent Systems
for Molecular Biology (ISMB), 2000.
P
Tamayo, D Slonim et al.: Interpreting patterns of gene expression with
self-organizing maps: Methods and application to hematopoietic differentiation.
Proc
Natl Acad Sci USA, 96:2907-2912, 1999.
S
Tavazoie, JD Hughes, MJ Campbell, RJ Cho, GM Church: Systematic determination
of genetic network architecture. Nature Genetics, 22: 281-285, 1999.
PJ Waddell, H Kishino: Cluster Inference Methods and Graphical Models Evaluated on NCI60
Microarray Gene Expression Data. Genome Informatics 11, 129-140 (2000).
S
EP Xing and RM Karp: CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using ormalized cuts.
Proceedings of ISMB 2001, Bioinformatics 2001 17:306S-315S
KY
Yeung, DR Haynor, WL Ruzzo: Validating Clustering for Gene Expression Data.
Bioinformatics
2001 17: 309-318.
KY
Yeung, WL Ruzzo: Prinicipal component analysis for clustering gene expression data.
Bioinformatics
2001 17: 763-774.
KY
Yeung, C Fraley, A Murua, AE Raftery, WL Ruzzo: Model-based clustering and data transformations for gene expression data.
Bioinformatics
2001 17: 977-987.
Other unsupervised methods
A.
Ben-Dor, N. Friedman and Z. Yakhini: Class Discovery in Gene Expression
Data. In Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology
(RECOMB), 2001.
J Bryan, KS Pollard, MJ van der Laan:
Paired and unpaired comparison and clustering with gene expression data.
Preprint, 2001.
Y Chen, GM Church: Biclustering
of expression data. Proceedings of the Eighth International Conference
on Intelligent Systems for Molecular Biology (ISMB), 2000.
K Fellenberg, NC Hauser, B Brors, A Neutzner, JD Hoheisel, M Vingron:
Correspondence analysis applied to microarray data. Proc Natl Acad Sci USA, 98: 10781-10786, 2001.
N Friedman, M Linial, I Nachman, D Pe'er:
Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology, 7(3/4):
601-620, 2000.
G
Getz, E Levine, E Domany: Coupled two-way clustering analysis of gene
microarray data. Proc Natl Acad Sci USA, 97: 12079-12084, 2000.
A.v.Heydebreck,
W. Huber, A. Poustka, M. Vingron: Identifying Splits with Clear Separation:
A New Class Discovery Method for Gene Expression Data. Proceedings of
ISMB 2001, Bioinformatics 17:107S-114S.
H Kishino, PJ Waddell: Correspondence Analysis of Genes and Tissue Types and Finding Genetic Links from Microarray Data. Genome Informatics 11, 83-95 (2000).
L
Lazzeroni, AB Owen: Plaid Models for Gene Expression Data. Tech. Report,
Stanford University.
W Liebermeister: Linear modes of gene expression determined by independent
component analysis. Bioinformatics 18 (1), 51-60, 2002.
D Pe'er, A Regev, G Elidan, N Friedman: Inferring subnetworks from perturbed expression profiles. Proceedings of
ISMB 2001, Bioinformatics 17:S215-S224.
KS Pollard, MJ van der Laan:
Statistical inference for simultaneous clustering of gene expression data.
Preprint, 2001.
E Segal, B Taskar, A Gasch, N Friedman, D Koller: Rich probabilistic models for gene expression. Proceedings of
ISMB 2001, Bioinformatics 17:243S-252S.
Other statistical analysis
Alter,
O., Brown, P. O., Botstein, D.: Singular value decomposition for genome-wide
expression data processing and modeling. Proc. Natl. Acad. Sci. U.
S. A. 97: 10101-10106, 2000.
T Hastie, R Tibshirani, D Botstein, P Brown 2001. Supervised harvesting of
expression trees. Genome Biology, 2(1), research0003.1-12.
SG
Hilsenbeck, WE Friedrichs et al.: Statistical analysis of array expression
data as applied to the problem of tamoxifen resistance. J Natl Cancer
Inst 1999; 91:453-459.
DC Hoyle, M Rattray, R Jupp and A Brass: Making sense of microarray data distributions. Bioinformatics 18, 576-584, 2002.
M.K.Kerr; M. Martin; G.A.
Churchill: Analysis of Variance for Gene Expression Microarray Data. Journal
of Computational Biology 7(6), 819-838, 2000.
S Kim, ER Dougherty, Y Chen,
K Sivakumar, P Meltzer, JM Trent, M Bittner: Multivariate measurement
of gene expression relationships. Genomics 67: 201-209, 2000.
MJ
van der Laan, J Bryan: Gene Expression Analysis with the Parametric Bootstrap.
Biostatistics, 2(3), 1--17, 2001.
H Li, F Hong: Cluster-Rasch models for microarray gene expression data. Genome Biology 2001, 2(8), research0031.1-0031.13.
DV Nguyen, DM Rocke: Partial Least Squares Proportional Hazard Regression for Application to DNA Microarray Data. Preprint, 2001.
David M. Rocke; Blythe Durbin: A Model for Measurement Error for Gene Expression Arrays.
Journal of Computational Biology 8(6),
557-569, 2001.
E
Spanakis, D Brouty-Boye:
Discrimination of fibroblast subtypes by multivariate
analysis of gene expression. Int J Cancer, 71: 402-409, 1997.
O.
Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R.Tibshirani,
D. Botstein, and R.B. Altman: Missing value estimation methods for DNA
microarrays. Bioinformatics 2001, 17: 520-525.
D Venet, F Pecasse, C Maenhaut, H Bersini: Separation of samples into their constituents using gene expression data. Proceedings of
ISMB 2001, Bioinformatics 17:279S-287S.
L Wernisch, SL Kendall, S Soneji, A Wietzorrek, T Parish, J Hinds,
PD Butcher NG Stoker: Analysis of whole-genome microarray replicates using mixed models.
Bioinformatics, Vol. 19, 53-61, 2003.
RD Wolfinger; G Gibson; ED Wolfinger; L Bennett; H Hamadeh; P Bushel: C Afshari; RS Paules:
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models.
Journal of Computational Biology 8(6),
625-637, 2001.