Analysis of microarray experiments of gene expression profiling

American Journal of Obstetrics and Gynecology

Volumn 195, Issue 2, 2006, Pages 373-388

  Tarca, Adi L ^a,b   Romero, Roberto ^a,c   Draghici, Sorin ^b,d  

^a EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT   (United States)

^b Department of Computer Science ^*  (United States)

^c WAYNE STATE UNIVERSITY   (United States)

^d KARMANOS CANCER INSTITUTE   (United States)

Author keywords

Clustering; Data preprocessing; Differential expression; Expression profiling; Functional profiling; Prediction; Reliability

Indexed keywords

ARTICLE; COMPARATIVE STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; DNA FINGERPRINTING; DNA MICROARRAY; EPISTEMOLOGY; GENE EXPRESSION; GENOME ANALYSIS; HUMAN; MEDICAL LITERATURE; MICROARRAY ANALYSIS; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE;

ALGORITHMS; BIAS (EPIDEMIOLOGY); GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; RESEARCH DESIGN; STATISTICS;

EID: 33746683125     PISSN: 00029378     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ajog.2006.07.001     Document Type: Article

References (157)

1. Schena M., Shalon D., Davis R.W., and Brown P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270 (1995) 467-470
2. Schena M. Microarray biochip technology (2000), Eaton Publishing, Sunnyvale, CA
3. Aguan K., Carvajal J.A., Thompson L.P., and Weiner C.P. Application of a functional genomics approach to identify differentially expressed genes in human myometrium during pregnancy and labour. Mol Hum Reprod 6 (2000) 1141-1145
4. Berchuck A., Iversen E.S., Lancaster J.M., Dressman H.K., West M., Nevins J.R., et al. Prediction of optimal versus suboptimal cytoreduction of advanced-stage serous ovarian cancer with the use of microarrays. Am J Obstet Gynecol 190 (2004) 910-925
5. Bethin K.E., Nagai Y., Sladek R., Asada M., Sadovsky Y., Hudson T.J., et al. Microarray analysis of uterine gene expression in mouse and human pregnancy. Mol Endocrinol 17 (2003) 1454-1469
6. Bukowski R., Hankins G.D., Saade G.R., Anderson G.D., and Thornton S. Labor-associated gene expression in the human uterine fundus, lower segment, and cervix. PLoS Med 3 (2006) e169
7. Chan E.C., Fraser S., Yin S., Yeo G., Kwek K., Fairclough R.J., et al. Human myometrial genes are differentially expressed in labor: a suppression subtractive hybridization study. J Clin Endocrinol Metab 87 (2002) 2435-2441
8. Charpigny G., Leroy M.J., Breuiller-Fouche M., Tanfin Z., Mhaouty-Kodja S., Robin P., et al. A functional genomic study to identify differential gene expression in the preterm and term human myometrium. Biol Reprod 68 (2003) 2289-2296
9. Chien E.K., Tokuyama Y., Rouard M., Phillippe M., and Bell G.I. Identification of gestationally regulated genes in rat myometrium by use of messenger ribonucleic acid differential display. Am J Obstet Gynecol 177 (1997) 645-652
10. Chin K.V., Seifer D.B., Feng B., Lin Y., and Shih W.C. DNA microarray analysis of the expression profiles of luteinized granulosa cells as a function of ovarian reserve. FertilSteril 77 (2002) 1214-1218
11. Critchely H.O.D., Robertson K.A., Forster T., Henderson T.A., Williams A.R.W., and Ghazal P. Gene expression profiling of mid to late secretory phase endometrial biopsies from women with menstrual complaint. Am J Obstet Gynecol (2006)
12. Esplin M.S., Fausett M.B., Peltier M.R., Hamblin S., Silver R.M., Branch D.W., et al. The use of cDNA microarray to identify differentially expressed labor-associated genes within the human myometrium during labor. Am J Obstet Gynecol 193 (2005) 404-413
13. Giudice L.C., Telles T.L., Lobo S., and Kao L. The molecular basis for implantation failure in endometriosis: on the road to discovery. Ann NY Acad Sci 955 (2002) 252-264
14. Haddad R., Tromp G., Kuivaniemi H., Chaiworapongsa T., Kim Y.M., Mazor M., et al. Human spontaneous labor without histologic chorioamnionitis is characterized by an acute inflammation gene expression signature. Am J Obstet Gynecol (2006)
15. Keelan J.A., Blumenstein M., Helliwell R.J., Sato T.A., Marvin K.W., and Mitchell M.D. Cytokines, prostaglandins and parturition-a review. Placenta 24 (2003) S33-S46
16. Leppert P.C., Catherino W.H., and Segars J. A new hypothesis about the origin of uterine fibroids based on gene expression profiling with microarrays. Am J Obstet Gynecol (2006)
17. Maynard S.E., Min J.Y., Merchan J., Lim K.H., Li J., Mondal S., et al. Excess placental soluble fms-like tyrosine kinase 1 (sFlt1) may contribute to endothelial dysfunction, hypertension, and proteinuria in preeclampsia. J Clin Invest 111 (2003) 649-658
18. Muhle R.A., Pavlidis P., Grundy W.N., and Hirsch E. A high-throughput study of gene expression in preterm labor with a subtractive microarray approach. Am J Obstet Gynecol 185 (2001) 716-724
19. Romero R., Kuivaniemi H., and Tromp G. Functional genomics and proteomics in term and preterm parturition. J Clin Endocrinol Metab 87 (2002) 2431-2434
20. Romero R., Kuivaniemi H., and Tromp G. Functional genomics and proteomics in term and preterm parturition. J Clin Endocrinol Metab 87 (2002) 2431-2434
21. Romero R., Tarca A.L., and Tromp G. Insights into the Physiology of Childbirth Using Transcriptomics. PLoS Med 3 (2006) e276
22. Soleymanlou N., Jurisica I., Nevo O., Ietta F., Zhang X., Zamudio S., et al. Molecular evidence of placental hypoxia in preeclampsia. J Clin Endocrinol Metab 90 (2005) 4299-4308
23. Tromp G., Kuivaniemi H., Romero R., Chaiworapongsa T., Kim Y.M., Kim M.R., et al. Genome-wide expression profiling of fetal membranes reveals a deficient expression of proteinase inhibitor 3 in premature rupture of membranes. Am J Obstet Gynecol 191 (2004) 1331-1338
24. Venkatesha S., Toporsian M., Lam C., Hanai J.I., Mammoto T., Kim Y.M., et al. Soluble endoglin contributes to the pathogenesis of preeclampsia. Nat Med 12 (2006) 642-649
25. Ward K. Microarray technology in obstetrics and gynecology: a guide for clinicians. Am J Obstet Gynecol (2006)
26. Zhang X., Jafari N., Barnes R.B., Confino E., Milad M., and Kazer R.R. Studies of gene expression in human cumulus cells indicate pentraxin 3 as a possible marker for oocyte quality. Fertil Steril 83 (2005) 1169-1179
27. Knudsen S. Guide to analysis of DNA microarray data (2004), John Wiley & Sons, Inc., Hoboken, NJ
28. Gibson G., and Muse S.V. A primer of genome science (2002), Sinauer Associates, Inc., Sunderland, MA
29. Analysing gene expression: a handbook of methods, possibilities, and pitfalls (2003), Wiley-VCH, Weinheim, Germany
30. Khatri P., and Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21 (2005) 3587-3595
31. Yang Y.H., Buckley M.J., Dudoit S., and Speed T.P. Comparison of methods for image analysis on cDNA microarray data. J Comput Graph Stat 11 (2002) 108-136
32. Affymetrix. Statistical algorithms description document. 2002. Affymetrix, Inc.
33. Wu Z, Irizarry R, Gentleman RC, Murillo FM, Spencer F. A model based background adjustment for oligonucleotide expression arrays. Working paper 1. 2004. Johns Hopkins University, Department of Biostatistics.
34. Irizarry R.A., Hobbs B., Collin F., Beazer-Barclay Y.D., Antonellis K.J., Scherf U., et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4 (2003) 249-264
35. Long A.D., Mangalam H.J., Chan B.Y., Tolleri L., Hatfield G.W., and Baldi P. Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12. J Biol Chem 276 (2001) 19937-19944
36. Speed T. Hints and prejudices - always log spot intensities and ratios. 2000. University of California, Berkeley.
37. Cui X., Kerr M.K., and Churchill G.A. Transformations for cDNA microarray data. Stat Appl Genet Mol Biol 2 (2003) Article4
38. Dudoit S., Yang Y.H., Speed T., and Callow M.J. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 12 (2002) 111-139
39. Quackenbush J. Computational analysis of microarray data. Nat Rev Genet 2 (2001) 418-427
40. Li C., and Wong W.H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA 98 (2001) 31-36
41. Chen Y., Dougherty E.R., and Bittner M.L. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Optics 2 (1997) 364-374
42. Draghici S. Data analysis tools for DNA microarrays (2003), Chapman and Hall/CRC Press, Boca Raton (FL)
43. Finkelstein D.B., Ewing R., Gollub J., Sterky F., Somerville S., and Cherry J.M. Iterative linear regression by sector. In: Lin S.M., and Johnson K.F. (Eds). Methods of microarray data analysis (2002), Kluwer Academic, Cambridge, MA 57-68
44. Hegde P., Qi R., Abernathy K., Gay C., Dharap S., Gaspard R., et al. A concise guide to cDNA microarray analysis. Biotechniques 29 (2000) 548-4, 556
45. Houts TM. Improved 2-color exponential normalization for microarray analyses employing cyanine dyes. Lin, S. 2000. Durham, NC, Duke University Medical Center. Proceedings of CAMDA 2000, "Critical assessment of techniques for microarray data mining".
46. Kepler T.B., Crosby L., and Morgan K.T. Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biol 3 (2002) RESEARCH0037
47. Newton M.A., Kendziorski C.M., Richmond C.S., Blattner F.R., and Tsui K.W. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J Comput Biol 8 (2001) 37-52
48. Schuchhardt J., Beule D., Malik A., Wolski E., Eickhoff H., Lehrach H., et al. Normalization strategies for cDNA microarrays. Nucleic Acids Res 28 (2000) E47
49. Stanford University. Arabidopsis. Normalization method comparison. 2001.
50. Tarca A.L., Cooke J.E., and Mackay J. A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data. Bioinformatics 21 (2005) 2674-2683
51. Wang Y., Lu J., Lee R., Gu Z., and Clarke R. Iterative normalization of cDNA microarray data. IEEE Trans Inf Technol Biomed 6 (2002) 29-37
52. Yang S., Dudoit S., Luu P., and Speed T.P. Normalization for cDNA microarray data. Proc of SPIE BiOS 4266 (2001) 31
53. Yang Y, Buckley MJ, Dudoit S, Speed TP. Comparison of methods for image analysis on cDNA. 2000. University of California, Berkeley.
54. Yang Y.H., Dudoit S., Luu P., Lin D.M., Peng V., Ngai J., et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 30 (2002) e15
55. Yue H., Eastman P.S., Wang B.B., Minor J., Doctolero M.H., Nuttall R.L., et al. An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression. Nucleic Acids Res 29 (2001) E41
56. Bolstad B.M., Irizarry R.A., Astrand M., and Speed T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19 (2003) 185-193
57. Gentleman R.C., Carey V.J., Bates D.M., Bolstad B., Dettling M., Dudoit S., et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5 (2004) R80
58. Dudoit S, Yang YH, Callow M, Speed T. Statistical models for identifying differentially expressed genes in replicated cDNA microarray experiments. 578. 2000. University of California, Berkeley.
59. Beer D.G., Kardia S.L., Huang C.C., Giordano T.J., Levin A.M., Misek D.E., et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8 (2002) 816-824
60. Swagell C.D., Henly D.C., and Morris C.P. Expression analysis of a human hepatic cell line in response to palmitate. Biochem Biophys Res Commun 328 (2005) 432-441
61. Pass H.I., Liu Z., Wali A., Bueno R., Land S., Lott D., et al. Gene expression profiles predict survival and progression of pleural mesothelioma. Clin.Cancer Res 10 (2004) 849-859
62. Kerr M.K., Martin M., and Churchill G.A. Analysis of variance for gene expression microarray data. J Comput Biol 7 (2000) 819-837
63. Kerr M.K., and Churchill G.A. Statistical design and the analysis of gene expression microarray data. Genet Res 77 (2001) 123-128
64. Kerr M.K., Afshari C.A., Bennett L., Bushel P., Martinez J., Walker N.J., et al. Statistical analysis of a gene expression microarray experiment with replication. Statistica Sinica 12 (2001) 203-218
65. Simon R., Radmacher M.D., and Dobbin K. Design of studies using DNA microarrays. Genet Epidemiol 23 (2002) 21-36
66. DeRisi J., Penland L., Brown P.O., Bittner M.L., Meltzer P.S., Ray M., et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 14 (1996) 457-460
67. ter Linde J.J., Liang H., Davis R.W., Steensma H.Y., van Dijken J.P., and Pronk J.T. Genome-wide transcriptional analysis of aerobic and anaerobic chemostat cultures of Saccharomyces cerevisiae. J Bacteriol 181 (1999) 7409-7413
68. Wellmann A., Thieblemont C., Pittaluga S., Sakai A., Jaffe E.S., Siebert P., et al. Detection of differentially expressed genes in lymphomas using cDNA arrays: identification of clusterin as a new diagnostic marker for anaplastic large-cell lymphomas. Blood 96 (2000) 398-404
69. Draghici S. Statistical intelligence: effective analysis of high-density microarray data. Drug Discov Today 7 (2002) S55-S63
70. Nadon R., and Shoemaker J. Statistical issues with microarrays: processing and analysis. Trends Genet 18 (2002) 265-271
71. Sebastiani P, Gussoni E. KI, M.F. R. Statistical challenges in functional genomics. Stat Sci 2003;18:33-70.
72. Budhraja V., Spitznagel E., Schaiff W.T., and Sadovsky Y. Incorporation of gene-specific variability improves expression analysis using high-density DNA microarrays. BMC Biol 1 (2003) 1
73. Lonnstedt I., and Speed T. Replicated microarray data. Statistica Sinica 12 (2002) 31-46
74. Smyth G.K., Yang Y.H., and Speed T. Statistical issues in cDNA microarray data analysis. Methods Mol Biol 224 (2003) 111-136
75. Smyth G.K. Limma: linear models for microarray data (2005), Springer, New York, NY
76. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98 (2001) 5116-5121
77. Tao H., Bausch C., Richmond C., Blattner F.R., and Conway T. Functional genomics: expression analysis of Escherichia coli growing on minimal and rich media. J Bacteriol 181 (1999) 6425-6440
78. Draghici S., Kuklin A., Hoff B., and Shams S. Experimental design, analysis of variance and slide quality assessment in gene expression arrays. Curr Opin Drug Discov Devel 4 (2001) 332-337
79. Draghici S., Kulaeva O., Hoff B., Petrov A., Shams S., and Tainsky M.A. Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays. Bioinformatics 19 (2003) 1348-1359
80. Dudoit S., Shaffer J., and Boldrick J. Multiple hypothesis testing in microarray experiments. Stat Sci 18 (2003) 71-103
81. Hochberg Y., and Tamhane A.C. Multiple comparison procedures (1987), John Wiley and Sons, Inc., New York, NY
82. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 6 (1979) 65-70
83. Shaffer J.P. Modified sequentially rejective multiple test procedures. J Am Stat Assoc 81 (1986) 826-831
84. Shaffer J.P. Multiple hypothesis testing. Ann Rev Psych 46 (1995) 561-584
85. Westfall P.H., and Young S.S. Resampling-based multiple testing: examples and methods for p-value adjustment (1993), Wiley, New York, NY
86. Benjamini Y., and Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc B 57 (1995) 289-300
87. Bonferroni CE. Il calcolo delle assicurazioni su gruppi di teste. In: Studi in Onore del Professore Salvatore Ortu Carboni. Rome: 1935. p. 13-60.
88. Quackenbush J. Microarray analysis and tumor classification. N Engl J Med 354 (2006) 2463-2472
89. Calvano S.E., Xiao W., Richards D.R., Felciano R.M., Baker H.V., Cho R.J., et al. A network-based analysis of systemic inflammation in humans. Nature 437 (2005) 1032-1037
90. Allison D.B., Cui X., Page G.P., and Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 7 (2006) 55-65
91. Dobbin K., and Simon R. Comparison of microarray designs for class comparison and class discovery. Bioinformatics 18 (2002) 1438-1445
92. Pan W., Lin J., and Le C.T. How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol 3 (2002) research0022
93. Dubitzky W., Granzow M., and Berrar D. Data mining and machine learning methods for microarray analysis. In: Lin S.M., and Johnson K.F. (Eds). Methods of microarray data analysis (2002), Kluwer Academic, Cambridge (MA) 5-22
94. Dubitzky W., Granzow M., and Berrar D. Comparing symbolic and subsymbolic machine learning approaches to classification of cancer and gene identification. In: Lin S.M., and Johnson K.F. (Eds). Methods of microarray data analysis (2002), Kluwer Academic, Cambridge (MA) 151-166
95. Horwood E. Machine learning, neural and statistical classification. Available at: http://www.amsta.leeds.ac.uk/charles/statlog/, 1994.
96. Hwang K.B., Cho D.Y., Park S.W., Kim S.D., and Zhang B.T. Applying machine learning techniques to analysis of gene expression data: cancer diagnosis. In: Lin S.M., and Johnson K.F. (Eds). Methods of microarray data analysis (2002), Kluwer Academic, Cambridge, MA 167-182
97. Bellman R. Adaptive control processes (1961), Princeton University Press, Princeton, NJ
98. Efron B., and Tibshirani R.J. An introduction to bootstrap (1993), Chapman and Hall, London, UK
99. Cortes C., and Vapnik V. Support-vector networks. Machine Learning 20 (1995) 273-297
100. Dudoit S., Fridlyand J., and Speed T. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97 (2002) 77-87
101. Hagan M.T., Demuth H.B., and Beale M.H. Neural network design (1995), Brooks Cole, Boston, MA
102. Haykin S. Neural networks - a comprehensive foundation (1999), Prentice Hall, Upper Saddle River, NJ
103. Rogers S., Williams R., and Campbell C. Class prediction with microarray datasets. Bioinformatics using computational intelligence paradigms (2005), Springer, Cambridge (MA) p. 119-42
104. Romero R. The child is the father of the man. Prenat Neonat Med 1 (1996) 8-11
105. Romero R., Espinoza J., Mazor M., and Chaiworapongsa T. The preterm parturition syndrome. In: Critchley H., Bennett P., and Thornton S. (Eds). Preterm birth (2004), RCOG Press, London, United Kingdom 28-60
106. Aach J., Rindone W., and Church G.M. Systematic management and analysis of yeast gene expression data. Genome Res 10 (2000) 431-445
107. Ben Dor A., Shamir R., and Yakhini Z. Clustering gene expression patterns. J Comput Biol 6 (1999) 281-297
108. Brazma A., Jonassen I., Vilo J., and Ukkonen E. Predicting gene regulatory elements in silico on a genomic scale. Genome Res 8 (1998) 1202-1215
109. Claverie J.M. Computational methods for the identification of differential and coordinated gene expression. Hum Mol Genet 8 (1999) 1821-1832
110. Eisen M.B., Spellman P.T., Brown P.O., and Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95 (1998) 14863-14868
111. Ewing R.M., Ben Kahla A., Poirot O., Lopez F., Audic S., and Claverie J.M. Large-scale statistical analyses of rice ESTs reveal correlated patterns of gene expression. Genome Res 9 (1999) 950-959
112. Getz G., Levine E., and Domany E. Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci USA 97 (2000) 12079-12084
113. Herwig R., Poustka A.J., Muller C., Bull C., Lehrach H., and O'Brien J. Large-scale clustering of cDNA-fingerprinting data. Genome Res 9 (1999) 1093-1105
114. Heyer L.J., Kruglyak S., and Yooseph S. Exploring expression data: identification and analysis of coexpressed genes. Genome Res 9 (1999) 1106-1115
115. Pietu G., Mariage-Samson R., Fayein N.A., Matingou C., Eveno E., Houlgatte R., et al. The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics. Genome Res 9 (1999) 195-209
116. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96 (1999) 2907-2912
117. Tsoka S., and Ouzounis C.A. Recent developments and future directions in computational genomics. FEBS Lett 480 (2000) 42-48
118. van Helden J., Rios A.F., and Collado-Vides J. Discovering regulatory elements in non-coding sequences by analysis of spaced dyads. Nucleic Acids Res 28 (2000) 1808-1818
119. Wang M.L., Belmonte S., Kim U., Dolan M., Morris J.W., and Goodman H.M. A cluster of ABA-regulated genes on Arabidopsis thaliana BAC T07M07. Genome Res 9 (1999) 325-333
120. White K.P., Rifkin S.A., Hurban P., and Hogness D.S. Microarray analysis of Drosophila development during metamorphosis. Science 286 (1999) 2179-2184
121. Zhang M.Q. Large-scale gene expression data analysis: a new challenge to computational biologists. Genome Res 9 (1999) 681-688
122. Zhu J., and Zhang M.Q. Cluster, function and promoter: analysis of yeast expression array. Pac Symp Biocomput (2000) 479-490
123. Bethin K.E., Nagai Y., Sladek R., Asada M., Sadovsky Y., Hudson T.J., et al. Microarray analysis of uterine gene expression in mouse and human pregnancy. Mol Endocrinol 17 (2003) 1454-1469
124. Draghici S., Khatri P., Martins R.P., Ostermeier G.C., and Krawetz S.A. Global functional profiling of gene expression. Genomics 81 (2003) 98-104
125. Khatri P., Draghici S., Ostermeier G.C., and Krawetz S.A. Profiling gene expression using onto-express. Genomics 79 (2002) 266-270
126. Draghici S., Khatri P., Bhavsar P., Shah A., Krawetz S., and Tainsky M.A. Onto-Tools, The toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Nucleic Acid Research 31 (2003) 3775-3781
127. Khatri P., Bhavsar P., Bawa G., and Draghici S. Onto-Tools: an ensemble of web-accessible, ontology-based tools for the functional design and interpretation of high-throughput gene expression experiments. Nucleic Acids Research 32 (2004) W449-W456
128. Pavlidis P., Qin J., Arango V., Mann J.J., and Sibille E. Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochem Res 29 (2004) 1213-1222
129. Goeman J.J., van de Geer S.A., de Kort F., and van Houwelingen H.C. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 20 (2004) 93-99
130. Mootha V.K., Lindgren C.M., Eriksson K.F., Subramanian A., Sihag S., Lehar J., et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34 (2003) 267-273
131. Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A., et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102 (2005) 15545-15550
132. Tian L., Greenberg S.A., Kong S.W., Altschuler J., Kohane I.S., and Park P.J. Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci USA 102 (2005) 13544-13549
133. Damian D., and Gorfine M. Statistical concerns about the GSEA procedure. Nat Genet 36 (2004) 663
134. Khatri P., Done B., Rao A., Done A., and Draghici S. A semantic analysis of the annotations of the human genome. Bioinformatics 21 (2005) 3416-3421
135. Doniger S.W., Salomonis N., Dahlquist K.D., Vranizan K., Lawlor S.C., and Conklin B.R. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 4 (2003) R7
136. Khatri P., Sellamuthu S., Malhotra P., Amin K., Done A., and Draghici S. Recent additions and improvements to the Onto-Tools. Nucleic Acids Res 33 (2005) W762-W765
137. Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13 (2003) 2498-2504
138. Karp PD, Paley S, Romero P. The Pathway Tools software. Bioinformatics 2002;18 Suppl 1:S225-S232.
139. Grosu P., Townsend J.P., Hartl D.L., and Cavalieri D. Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks. Genome Res 12 (2002) 1121-1126
140. MetaCore, GeneGo, St Joseph, MI. Available at: http://www.genego.com. 2003.
141. Audi R. Epistemology: a contemporary introduction to the theory of knowledge (2003), Routledge, New York, NY
142. Mehta T., Tanik M., and Allison D.B. Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nat Genet 36 (2004) 943-947
143. Draghici S., Khatri P., Eklund A.C., and Szallasi Z. Reliability and reproducibility issues in DNA microarray measurements. Trends Genet 22 (2006) 101-109
144. Holland M.J. Transcript abundance in yeast varies over six orders of magnitude. J Biol Chem 277 (2002) 14363-14366
145. Yuen T., Wurmbach E., Pfeffer R.L., Ebersole B.J., and Sealfon S.C. Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. Nucleic Acids Res 30 (2002) e48
146. Czechowski T., Bari R.P., Stitt M., Scheible W.R., and Udvardi M.K. Real-time RT-PCR profiling of over 1400 Arabidopsis transcription factors: unprecedented sensitivity reveals novel root- and shoot-specific genes. Plant J 38 (2004) 366-379
147. Bakay M., Chen Y.W., Borup R., Zhao P., Nagaraju K., and Hoffman E.P. Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics 3 (2002) 4
148. Bammler T., Beyer R.P., Bhattacharya S., Boorman G.A., Boyles A., Bradford B.U., et al. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2 (2005) 351-356
149. Jarvinen A.K., Hautaniemi S., Edgren H., Auvinen P., Saarela J., Kallioniemi O.P., et al. Are data from different gene expression microarray platforms comparable?. Genomics 83 (2004) 1164-1168
150. Jenssen T.K., Langaas M., Kuo W.P., Smith-Sorensen B., Myklebost O., and Hovig E. Analysis of repeatability in spotted cDNA microarrays. Nucleic Acids Res 30 (2002) 3235-3244
151. Carter S.L., Eklund A.C., Mecham B.H., Kohane I.S., and Szallasi Z. Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC Bioinformatics 6 (2005) 107
152. Halgren R.G., Fielden M.R., Fong C.J., and Zacharewski T.R. Assessment of clone identity and sequence fidelity for 1189 IMAGE cDNA clones. Nucleic Acids Res 29 (2001) 582-588
153. Taylor E., Cogdell D., Coombes K., Hu L., Ramdas L., Tabor A., et al. Sequence verification as quality-control step for production of cDNA microarrays. Biotechniques 31 (2001) 62-65
154. Kuo W.P., Jenssen T.K., Butte A.J., Ohno-Machado L., and Kohane I.S. Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18 (2002) 405-412
155. Lee E.W., Michalkiewicz M., Kitlinska J., Kalezic I., Switalska H., Yoo P., et al. Neuropeptide Y induces ischemic angiogenesis and restores function of ischemic skeletal muscles. J Clin Invest 111 (2003) 1853-1862
156. Mecham B.H., Klus G.T., Strovel J., Augustus M., Byrne D., Bozso P., et al. Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Res 32 (2004) e74
157. Tan P.K., Downey T.J., Spitznagel Jr. E.L., Xu P., Fu D., Dimitrov D.S., et al. Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 31 (2003) 5676-5684