Probability fold change: A robust computational approach for identifying differentially expressed gene lists

Computer Methods and Programs in Biomedicine

Volumn 93, Issue 2, 2009, Pages 124-139

  Deng, Xutao ^a,b   Xu, Jun ^b   Hui, James ^a   Wang, Charles ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b BURNS AND ALLEN RESEARCH INSTITUTE   (United States)

Author keywords

Differentially expressed gene; DNA microarray; Gene expression; Gene ranking algorithm

Indexed keywords

BAYESIAN; BENCHMARK DATUMS; BIOLOGICAL INTERPRETATIONS; COMPUTATIONAL APPROACHES; CONFIDENCE INTERVALS; DATA SETS; DIFFERENTIALLY EXPRESSED GENE; DNA MICROARRAY; EXPERIMENTAL CONDITIONS; EXTENSIVE TESTING; GENE LISTS; LATIN SQUARES; MICROARRAY DATA ANALYSES; NEW ALGORITHMS; QUALITY ASPECTS; RANKING METHODS; REPRODUCIBILITY; STATISTICAL ACCURACIES;

AC GENERATOR MOTORS; BENCHMARKING; BIOACTIVITY; CUSTOMER SATISFACTION; DNA; GENE EXPRESSION; GENES; NUCLEIC ACIDS; ORGANIC ACIDS; PROBABILITY; QUALITY ASSURANCE; QUALITY FUNCTION DEPLOYMENT; SUGAR (SUCROSE); TOTAL QUALITY MANAGEMENT;

QUALITY CONTROL;

ALGORITHMS; ANIMALS; BAYES THEOREM; BIOMETRY; CONFIDENCE INTERVALS; DATA INTERPRETATION, STATISTICAL; DATABASES, GENETIC; GENE EXPRESSION; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; QUALITY CONTROL; REPRODUCIBILITY OF RESULTS; REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION; SAMPLE SIZE;

EID: 58149277477     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2008.07.013     Document Type: Article

References (58)

1. Clark T.A., Sugnet C.W., and Ares Jr. M. Genomewide analysis of mRNA processing in yeast using splicing-specific microarrays. Science 296 (2002) 907-910
2. Debouck C., and Goodfellow P.N. DNA microarrays in drug discovery and development. Nat. Genet. 21 (1999) 48-50
3. Heller R.A., Schena M., Chai A., Shalon D., Bedilion T., Gilmore J., Woolley D.E., and Davis R.W. Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. Proc. Natl. Acad. Sci. U.S.A. 94 (1997) 2150-2155
4. Lim L.P., Lau N.C., Garrett-Engele P., Grimson A., Schelter J.M., Castle J., Bartel D.P., Linsley P.S., and Johnson J.M. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433 (2005) 769-773
5. Mills J.C., Roth K.A., Cagan R.L., and Gordon J.I. DNA microarrays and beyond: completing the journey from tissue to cell. Nat. Cell Biol. 3 (2001) E175-E178
6. Wang C., Chelly M.R., Chai N., Tan Y., Hui T., Li H., Farkas D.L., and Demetriou A.A. Transcriptomic fingerprinting of bone marrow-derived hepatic beta2m-/Thy-1+ stem cells. Biochem. Biophys. Res. Commun. 327 (2005) 252-260
7. Tan Y., Shi L., Tong W., and Wang C. Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data. Nucleic Acids Res. 33 (2005) 56-65
8. Tan Y., Shi L., Tong W., Hwang G.T., and Wang C. Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models. Comput. Biol. Chem. 28 (2004) 235-244
9. Deng X., Geng H., Bastola D.R., and Ali H.H. Link test-a statistical method for finding prostate cancer biomarkers. Comput. Biol. Chem. 30 (2006) 425-433
10. Deng X., Geng H., and Ali H.H. Cross-platform analysis of cancer biomarkers: a Bayesian network approach to incorporating mass spectrometry and microarray data. Cancer Inform. 2 (2007) 183-202
11. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
12. Zhou X., Liu K.Y., and Wong S.T. Cancer classification and prediction using logistic regression with Bayesian gene selection. J. Biomed. Inform. 37 (2004) 249-259
13. Lim D.A., Suarez-Farinas M., Naef F., Hacker C.R., Menn B., Takebayashi H., Magnasco M., Patil N., and Alvarez-Buylla A. In vivo transcriptional profile analysis reveals RNA splicing and chromatin remodeling as prominent processes for adult neurogenesis. Mol. Cell Neurosci. 31 (2006) 131-148
14. Michaut L., Flister S., Neeb M., White K.P., Certa U., and Gehring W.J. Analysis of the eye developmental pathway in Drosophila using DNA microarrays. Proc. Natl. Acad. Sci. U.S.A. 100 (2003) 4024-4029
15. Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A., Paulovich A., Pomeroy S.L., Golub T.R., Lander E.S., and Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 102 (2005) 15545-15550
16. Guo L., Lobenhofer E.K., Wang C., Shippy R., Harris S.C., Zhang L., Mei N., Chen T., Herman D., Goodsaid F.M., Hurban P., Philips K.M., Xu J., Deng X., Sun Y.A., Tong W., Dragan Y.P., and Shi L. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat. Biotechnol. 24 (2006) 1162-1169
17. Qin L.X., and Kerr K.F. Empirical evaluation of data transformations and ranking statistics for microarray analysis. Nucleic Acids Res. 32 (2004) 5471-5479
18. Marshall E. Getting the noise out of gene arrays. Science 306 (2004) 630-631
19. Miklos G.L., and Maleszka R. Microarray reality checks in the context of a complex disease. Nat. Biotechnol. 22 (2004) 615-621
20. Tan P.K., Downey T.J., Spitznagel Jr. E.L., Xu P., Fu D., Dimitrov D.S., Lempicki R.A., Raaka B.M., and Cam M.C. Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 31 (2003) 5676-5684
21. Bammler T., Beyer R.P., Bhattacharya S., Boorman G.A., Boyles A., Bradford B.U., Bumgarner R.E., Bushel P.R., Chaturvedi K., Choi D., Cunningham M.L., Deng S., Dressman H.K., Fannin R.D., Farin F.M., Freedman J.H., Fry R.C., Harper A., Humble M.C., Hurban P., Kavanagh T.J., Kaufmann W.K., Kerr K.F., Jing L., Lapidus J.A., Lasarev M.R., Li J., Li Y.J., Lobenhofer E.K., Lu X., Malek R.L., Milton S., Nagalla S.R., O'malley J.P., Palmer V.S., Pattee P., Paules R.S., Perou C.M., Phillips K., Qin L.X., Qiu Y., Quigley S.D., Rodland M., Rusyn I., Samson L.D., Schwartz D.A., Shi Y., Shin J.L., Sieber S.O., Slifer S., Speer M.C., Spencer P.S., Sproles D.I., Swenberg J.A., Suk W.A., Sullivan R.C., Tian R., Tennant R.W., Todd S.A., Tucker C.J., Van Houten B., Weis B.K., Xuan S., and Zarbl H. Standardizing global gene expression analysis between laboratories and across platforms. Nat. Meth. 2 (2005) 351-356
22. Irizarry R.A., Warren D., Spencer F., Kim I.F., Biswal S., Frank B.C., Gabrielson E., Garcia J.G., Geoghegan J., Germino G., Griffin C., Hilmer S.C., Hoffman E., Jedlicka A.E., Kawasaki E., Martinez-Murillo F., Morsberger L., Lee H., Petersen D., Quackenbush J., Scott A., Wilson M., Yang Y., Ye S.Q., and Yu W. Multiple-laboratory comparison of microarray platforms. Nat. Meth. 2 (2005) 345-350
23. Larkin J.E., Frank B.C., Gavras H., Sultana R., and Quackenbush J. Independence and reproducibility across microarray platforms. Nat. Meth. 2 (2005) 337-344
24. Shi L., Tong W., Fang H., Scherf U., Han J., Puri R.K., Frueh F.W., Goodsaid F.M., Guo L., Su Z., Han T., Fuscoe J.C., Xu Z.A., Patterson T.A., Hong H., Xie Q., Perkins R.G., Chen J.J., and Casciano D.A. Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential, BMC. Bioinformatics 6 Suppl 2 (2005) S12
25. Shi L., Reid L.H., Jones W.D., Shippy R., Warrington J.A., Baker S.C., Collins P.J., de L.F., Kawasaki E.S., Lee K.Y., Luo Y., Sun Y.A., Willey J.C., Setterquist R.A., Fischer G.M., Tong W., Dragan Y.P., Dix D.J., Frueh F.W., Goodsaid F.M., Herman D., Jensen R.V., Johnson C.D., Lobenhofer E.K., Puri R.K., Schrf U., Thierry-Mieg J., Wang C., Wilson M., Wolber P.K., Zhang L., Amur S., Bao W., Barbacioru C.C., Lucas A.B., Bertholet V., Boysen C., Bromley B., Brown D., Brunner A., Canales R., Cao X.M., Cebula T.A., Chen J.J., Cheng J., Chu T.M., Chudin E., Corson J., Corton J.C., Croner L.J., Davies C., Davison T.S., Delenstarr G., Deng X., Dorris D., Eklund A.C., Fan X.H., Fang H., Fulmer-Smentek S., Fuscoe J.C., Gallagher K., Ge W., Guo L., Guo X., Hager J., Haje P.K., Han J., Han T., Harbottle H.C., Harris S.C., Hatchwell E., Hauser C.A., Hester S., Hong H., Hurban P., Jackson S.A., Ji H., Knight C.R., Kuo W.P., LeClerc J.E., Levy S., Li Q.Z., Liu C., Liu Y., Lombardi M.J., Ma Y., Magnuson S.R., Maqsodi B., McDaniel T., Mei N., Myklebost O., Ning B., Novoradovskaya N., Orr M.S., Osborn T.W., Papallo A., Patterson T.A., Perkins R.G., Peters E.H., Peterson R., Philips K.L., Pine P.S., Pusztai L., Qian F., Ren H., Rosen M., Rosenzweig B.A., Samaha R.R., Schena M., Schroth G.P., Shchegrova S., Smith D.D., Staedtler F., Su Z., Sun H., Szallasi Z., Tezak Z., Thierry-Mieg D., Thompson K.L., Tikhonova I., Turpaz Y., Vallanat B., Van C., Walker S.J., Wang S.J., Wang Y., Wolfinger R., Wong A., Wu J., Xiao C., Xie Q., Xu J., Yang W., Zhang L., Zhong S., Zong Y., and Slikker Jr. W. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24 (2006) 1151-1161
26. Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18 (2002) 546-554
27. Patterson T.A., Lobenhofer E.K., Fulmer-Smentek S.B., Collins P.J., Chu T.M., Bao W., Fang H., Kawasaki E.S., Hager J., Tikhonova I.R., Walker S.J., Zhang L., Hurban P., de L.F., Fuscoe J.C., Tong W., Shi L., and Wolfinger R.D. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat. Biotechnol. 24 (2006) 1140-1150
28. Affymetrix, http://www.affymetrix.com/analysis/download_center2.affx, 2007.
29. Allison D.B. A mixture model approach for the analysis of microarray gene expression data. Comput. Stat. Data Anal. 39 (2002) 1-20
30. Cui X., and Churchill G.A. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 4 (2003) 210
31. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U.S.A. 98 (2001) 5116-5121
32. Baldi P., and Long A.D. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17 (2001) 509-519
33. Mutch D.M., Berger A., Mansourian R., Rytz A., and Roberts M.A. The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data. BMC Bioinformat. 3 (2002) 17
34. Yang I.V., Chen E., Hasseman J.P., Liang W., Frank B.C., Wang S., Sharov V., Saeed A.I., White J., and Li J. Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biol. 3 (2002) (research006-0062)
35. Breitling R., Armengaud P., Amtmann A., and Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573 (2004) 83-92
36. Brody J.P., Williams B.A., Wold B.J., and Quake S.R. Significance and statistical errors in the analysis of DNA microarray data. Proc. Natl. Acad. Sci. U.S.A. 99 (2002) 12975-12978
37. Tibshirani R. A simple method for assessing sample sizes in microarray experiments. BMC Bioinformat. 7 (2006) 106
38. Pan W. On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Bioinformatics 19 (2003) 1333-1340
39. Xie Y., Pan W., and Khodursky A.B. A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. Bioinformatics 21 (2005) 4280-4288
40. Affymetrix, Guide to Probe Logarithmic Intensity Error (PLIER) Estimation, Affymetrix, 2005.
41. 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
42. Rajeevan M.S., Ranamukhaarachchi D.G., Vernon S.D., and Unger E.R. Use of real-time quantitative PCR to validate the results of cDNA array and differential display PCR technologies. Methods 25 (2001) 443-451
43. Jeffery I.B., Higgins D.G., and Culhane A.C. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformat. 7 (2006) 359
44. Thomas J.G., Olson J.M., Tapscott S.J., and Zhao L.P. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res. 11 (2001) 1227-1236
45. Benjamini Y., and Hochberg Y. Controlling the false discovery rate-a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57 (1995) 289-300
46. DeRisi J.L., Iyer V.R., and Brown P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278 (1997) 680-686
47. Schena M., Shalon D., Heller R., Chai A., Brown P.O., and Davis R.W. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc. Natl. Acad. Sci. U.S.A. 93 (1996) 10614-10619
48. B. Efron, R. Tibshirani, V. Goss, G. Chu, Microarrays and their use in comparative experiments. Technical Report TR 213, Stanford University, 2000.
49. Cui X., Kerr M.K., and Churchill G.A. Transformations for cDNA microarray data. Stat. Appl. Genet. Mol. Biol. 2 (2003) (Article 4)
50. Troyanskaya O.G., Garber M.E., Brown P.O., Botstein D., and Altman R.B. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 18 (2002) 1454-1461
51. Lonnstedt I., and Speed T. Replicated microarray data. Stat. Sin. 12 (2006) 31-46
52. Newton M.A., Noueiry A., Sarkar D., and Ahlquist P. Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 (2004) 155-176
53. 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)
54. Nilsson R., Pena J.M., Bjorkegren J., and Tegner J. Detecting multivariate differentially expressed genes. BMC Bioinformat. 8 (2007) 150
55. Zhou Y., Cras-Meneur C., Ohsugi M., Stormo G.D., and Permutt M.A. A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments. Bioinformatics 23 (2007) 2073-2079
56. Lai Y. On the identification of differentially expressed genes: improving the generalized F-statistics for Affymetrix microarray gene expression data. Comput. Biol. Chem. 30 (2006) 321-326
57. Yang Y.H., Xiao Y., and Segal M.R. Identifying differentially expressed genes from microarray experiments via statistic synthesis. Bioinformatics 21 (2005) 1084-1093
58. Kenney J.F., and Keeping E.S. The Distribution of the Standard Deviation, Mathematics of Statistics (1951), Van Nostrand, Princeton, NJ pp. 170-173