Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery

PLoS ONE

Volumn 7, Issue 11, 2012, Pages

  Kumari, Sapna ^a   Nie, Jeff ^b   Chen, Huann Sheng ^c   Ma, Hao ^d   Stewart, Ron ^b   Li, Xiang ^e   Lu, Meng Zhu ^f   Taylor, William M ^g   Wei, Hairong ^a,e  

^a MICHIGAN TECHNOLOGICAL UNIVERSITY   (United States)

^b MORGRIDGE INSTITUTE FOR RESEARCH   (United States)

^c NATIONAL CANCER INSTITUTE   (United States)

^d WEST VIRGINIA UNIVERSITY   (United States)

^e Michigan Technological University   (United States)

^f RESEARCH INSTITUTE OF FORESTRY   (China)

^g University of Wisconsin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTION FACTOR;

ANALYSIS OF COVARIANCE; ANALYTIC METHOD; ARABIDOPSIS; ARTICLE; CONTROLLED STUDY; GENE CLUSTER; GENE EXPRESSION REGULATION; GENE FUNCTION; GENE IDENTIFICATION; GENE REGULATORY NETWORK; GENETIC ASSOCIATION; GENETIC TRAIT; KNOWLEDGE DISCOVERY; MICROARRAY ANALYSIS; NONHUMAN; NUCLEOTIDE SEQUENCE; PLANT DEVELOPMENT; PLANT GENE; PLANT GENOME; REGULATOR GENE; VALIDATION PROCESS;

ALGORITHMS; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; STATISTICS, NONPARAMETRIC; SYSTEMS BIOLOGY; TRANSCRIPTION FACTORS;

EID: 84870626688     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0050411     Document Type: Article

References (46)

1. Nie J, Stewart R, Zhang H, Thomson JA, Ruan F, et al. (2011) TF-Cluster: a pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM). BMC systems biology 5: 53.
2. Wei H, Persson S, Mehta T, Srinivasasainagendra V, Chen L, et al. (2006) Transcriptional coordination of the metabolic network in Arabidopsis. Plant physiology 142: 762-774.
3. Persson S, Wei H, Milne J, Page GP, Somerville CR, (2005) Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets. Proceedings of the National Academy of Sciences of the United States of America 102: 8633-8638.
4. van den Berg RA, Braaksma M, van der Veen D, van der Werf MJ, Punt PJ, et al. (2010) Identification of modules in Aspergillus niger by gene co-expression network analysis. Fungal genetics and biology: FG & B 47: 539-550.
5. Stuart JM, Segal E, Koller D, Kim SK, (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302: 249-255.
6. Minguez P, Dopazo J, (2011) Assessing the biological significance of gene expression signatures and co-expression modules by studying their network properties. PloS one 6: e17474.
7. Kawasaki S, Borchert C, Deyholos M, Wang H, Brazille S, et al. (2001) Gene expression profiles during the initial phase of salt stress in rice. Plant Cell 13: 889-905.
8. Brezina V, Orekhova IV, Weiss KR, (1997) Control of time-dependent biological processes by temporally patterned input. Proceedings of the National Academy of Sciences of the United States of America 94: 10444-10449.
9. Ingram PJ, Stumpf MP, Stark J, (2008) Nonidentifiability of the source of intrinsic noise in gene expression from single-burst data. PLoS computational biology 4: e1000192.
10. Holloway DM, Lopes FJ, da Fontoura Costa L, Travencolo BA, Golyandina N, et al. (2011) Gene expression noise in spatial patterning: hunchback promoter structure affects noise amplitude and distribution in Drosophila segmentation. PLoS computational biology 7: e1001069.
11. Tu Y, Stolovitzky G, Klein U, (2002) Quantitative noise analysis for gene expression microarray experiments. Proceedings of the National Academy of Sciences of the United States of America 99: 14031-14036.
12. Wang Z, Zhang J, (2011) Impact of gene expression noise on organismal fitness and the efficacy of natural selection. Proceedings of the National Academy of Sciences of the United States of America 108: E67-76.
13. Kaern M, Elston TC, Blake WJ, Collins JJ, (2005) Stochasticity in gene expression: from theories to phenotypes. Nature reviews Genetics 6: 451-464.
14. Rowicka M, Kudlicki A, Tu BP, Otwinowski Z, (2007) High-resolution timing of cell cycle-regulated gene expression. Proceedings of the National Academy of Sciences of the United States of America 104: 16892-16897.
15. Aulehla A, Pourquie O, (2008) Oscillating signaling pathways during embryonic development. Current opinion in cell biology 20: 632-637.
16. Fujita A, Sato JR, Demasi MA, Sogayar MC, Ferreira CE, et al. (2009) Comparing Pearson, Spearman and Hoeffding's D measure for gene expression association analysis. Journal of bioinformatics and computational biology 7: 663-684.
17. Peng H, Wang S, Wang X, (2007) Consistency and asymptotic distribution of the Theil-Sen estimator. Journal of Statistical Planning and Inference 138: 1836-1850.
18. Wilcox RR, (1998) A note on the Theil-Sen regression estimator when the regressor is random and the error term Is heteroscedastic. Biometrical Journal 40: 261-268.
19. Szekely GJ, Rizzo ML, (2009) Brownian Distance Covariance. Annals of Applied Statistics 3: 1236-1265.
20. Prokhorov AV (2001) Kendall coefficient of rank correlation; Hazewinkel M, editor: Springer.
21. Pinto da Costa JF, Soares C, (2005) A weighted rank measure of correlation. Australian & New Zealand Journal of Statistics 47: 515-529.
22. Mao L, Van Hemert JL, Dash S, Dickerson JA, (2009) Arabidopsis gene co-expression network and its functional modules. BMC bioinformatics 10: 346.
23. Aggarwal A, Guo DL, Hoshida Y, Yuen ST, Chu KM, et al. (2006) Topological and functional discovery in a gene coexpression meta-network of gastric cancer. Cancer research 66: 232-241.
24. Zhang B, Horvath S, (2005) A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology 4: Article17.
25. Boyer LA, Lee TI, Cole MF, Johnstone SE, Levine SS, et al. (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122: 947-956.
26. Bennett T, van den Toorn A, Sanchez-Perez GF, Campilho A, Willemsen V, et al. (2010) SOMBRERO, BEARSKIN1, and BEARSKIN2 regulate root cap maturation in Arabidopsis. Plant Cell 22: 640-654.
27. Zhong R, Lee C, Zhou J, McCarthy RL, Ye ZH, (2008) A battery of transcription factors involved in the regulation of secondary cell wall biosynthesis in Arabidopsis. Plant Cell 20: 2763-2782.
28. Ohashi-Ito K, Bergmann DC, (2007) Regulation of the Arabidopsis root vascular initial population by LONESOME HIGHWAY. Development 134: 2959-2968.
29. Kubo M, Udagawa M, Nishikubo N, Horiguchi G, Yamaguchi M, et al. (2005) Transcription switches for protoxylem and metaxylem vessel formation. Genes Dev 19: 1855-1860.
30. Newson R, (2002) Parameters behind "nonparametric" statistics: Kendall's tau, Somers' D and median differences. Stata Journal 2: 45-64.
31. Rodgers JL, Nicewander WA, (1988) Thirteen ways to look at the correlation coefficient. The American Statistician 42: 59-66.
32. Chok NS (2010) Pearson's versus spearman's and kendall's correlation coefficient for continuous data: University of Pittsburgh.
33. Hardin J, Mitani A, Hicks L, VanKoten B, (2007) A robust measure of correlation between two genes on a microarray. BMC bioinformatics 8: 220.
34. Devlin SJ, Gnanadesikan R, Kettenring JR, (1975) Robust Estimation and Outlier Detection with Correlation Coefficients. Biometrika 62: 531-545.
35. Szekely GJ, Rizzo ML, Bakirov NK, (2007) Measuring and Testing Dependence by Correlation of Distances. Annals of Statistics 35: 2769-2794.
36. Young RA, (2011) Control of the embryonic stem cell state. Cell 144: 940-954.
37. Spearman C, (1904) General Intelligence," Objectively Determined and Measured. The American Journal of Psychology 15: 201-292.
38. Redman JC, Haas BJ, Tanimoto G, Town CD, (2004) Development and evaluation of an Arabidopsis whole genome Affymetrix probe array. Plant J 38: 545-561.
39. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249-264.
40. Cui X, Wang T, Chen HS, Busov V, Wei H, (2010) TF-finder: a software package for identifying transcription factors involved in biological processes using microarray data and existing knowledge base. BMC bioinformatics 11: 425.
41. Nelsen RB (2001) Kendall tau metric; Hazewinkel M, editor: Springer.
42. Rahman NA (1968) A Course in Theoretical Statistics: Charles Griffin and Company.
43. Myers JL, Well AD (2003) Research Design and Statistical Analysis (second edition): Lawrence Erlbaum.
44. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical Recipes in C: The Art of Scientific Computing: Cambridge University Press.
45. Pinto da Costa JF, Roque LAC, (2006) Limit distribution for the weighted rank correlation coefficient. Revstal Statistical Journal 4(3): 189-200.
46. Hoeffding W, (1948) Non-parametric test of independence. The Annals of Mathematical Statistics 19:546-557, 1948 19: 546-557.