Warning: The NCBI web site requires JavaScript to function. more...
Permutation-validated principal components analysis of microarray data.
Landgrebe J, Wurst W, Welzl G.
Genome Biol. 2002;3(4):RESEARCH0019. Epub 2002 Mar 22.
Related citations
Microarray data analysis: a practical approach for selecting differentially expressed genes.
Mutch DM, Berger A, Mansourian R, Rytz A, Roberts MA.
Genome Biol. 2001;2(12):PREPRINT0009. Epub 2001 Nov 16.
Interpretation of ANOVA models for microarray data using PCA.
de Haan JR, Wehrens R, Bauerschmidt S, Piek E, van Schaik RC, Buydens LM.
Bioinformatics. 2007 Jan 15;23(2):184-90. Epub 2006 Nov 14.
Analysis of strain and regional variation in gene expression in mouse brain.
Pavlidis P, Noble WS.
Genome Biol. 2001;2(10):RESEARCH0042. Epub 2001 Sep 27.
A unified framework for finding differentially expressed genes from microarray experiments.
Shaik JS, Yeasin M.
BMC Bioinformatics. 2007 Sep 18;8:347.
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes.
Rodenburg W, Heidema AG, Boer JM, Bovee-Oudenhoven IM, Feskens EJ, Mariman EC, Keijer J.
Physiol Genomics. 2008 Mar 14;33(1):78-90. Epub 2007 Dec 27.
Group testing for pathway analysis improves comparability of different microarray datasets.
Manoli T, Gretz N, Gröne HJ, Kenzelmann M, Eils R, Brors B.
Bioinformatics. 2006 Oct 15;22(20):2500-6. Epub 2006 Aug 7.
A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription.
Johansson D, Lindgren P, Berglund A.
Bioinformatics. 2003 Mar 1;19(4):467-73.
Mining gene expression data by interpreting principal components.
Roden JC, King BW, Trout D, Mortazavi A, Wold BJ, Hart CE.
BMC Bioinformatics. 2006 Apr 7;7:194.
The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.
Mansourian R, Mutch DM, Antille N, Aubert J, Fogel P, Le Goff JM, Moulin J, Petrov A, Rytz A, Voegel JJ, Roberts MA.
Bioinformatics. 2004 Nov 1;20(16):2726-37. Epub 2004 May 14.
Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays.
Wang Y, Barbacioru C, Hyland F, Xiao W, Hunkapiller KL, Blake J, Chan F, Gonzalez C, Zhang L, Samaha RR.
BMC Genomics. 2006 Mar 21;7:59.
A comparison of parametric versus permutation methods with applications to general and temporal microarray gene expression data.
Xu R, Li X.
Bioinformatics. 2003 Jul 1;19(10):1284-9.
Evaluation of gene importance in microarray data based upon probability of selection.
Fu LM, Fu-Liu CS.
BMC Bioinformatics. 2005 Mar 22;6:67.
Biologically valid linear factor models of gene expression.
Girolami M, Breitling R.
Bioinformatics. 2004 Nov 22;20(17):3021-33. Epub 2004 Jun 16.
Methods for evaluating gene expression from Affymetrix microarray datasets.
Jiang N, Leach LJ, Hu X, Potokina E, Jia T, Druka A, Waugh R, Kearsey MJ, Luo ZW.
BMC Bioinformatics. 2008 Jun 17;9:284.
Bayesian hierarchical error model for analysis of gene expression data.
Cho H, Lee JK.
Bioinformatics. 2004 Sep 1;20(13):2016-25. Epub 2004 Mar 25.
Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data.
Jonnalagadda S, Srinivasan R.
BMC Bioinformatics. 2008 Jun 6;9:267.
Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments.
Gao X.
Bioinformatics. 2006 Jun 15;22(12):1486-94. Epub 2006 Mar 30.
Analysis of variance components in gene expression data.
Chen JJ, Delongchamp RR, Tsai CA, Hsueh HM, Sistare F, Thompson KL, Desai VG, Fuscoe JC.
Bioinformatics. 2004 Jun 12;20(9):1436-46. Epub 2004 Feb 12.
Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation.
Comander J, Natarajan S, Gimbrone MA Jr, García-Cardeña G.
BMC Genomics. 2004 Feb 27;5(1):17.
Filter your results:
Your browsing activity is empty.
Activity recording is turned off.
Turn recording back on