Methods used for Identification of Differentially Expressing Genes (DEGs) from Microarray Gene Dataset: A Review

 
 
International Journal of Biotech Trends and Technology (IJBTT)
 
© 2017 by IJBTT Journal
Volume - 7 Issue - 2                          
Year of Publication : 2017
Authors : Chanda Panse (Wajgi), Manali Kshirsagar, Dipak Wajgi
DOI :  10.14445/22490183/IJBTT-V21P601

Citation

Chanda Panse(Wajgi), Manali Kshirsagar, Dipak Wajgi "Methods used for Identification of Differentially Expressing Genes (DEGs) from Microarray Gene Dataset: A Review", International Journal of Biotech Trends and Technology (IJBTT), V7(2): 1-6 Apr to Jun 2017, Published by Seventh Sense Research Group.

Abstract

Genes contain blue print of living organism. Malfunctioning occurred in cellular life is indicated by proteins which are responsible for behavior of genes. Fixed set of genes decides behavior and functioning of cells. They guide the cells what to do and when to do. To analyze the insight of biological activities, analysis of gene expressions is necessary. Advanced technology like microarray plays an important role in gene analysis. It captures expressions of thousands of genes under different conditions simultaneously. Out of thousands of genes, very few behave differently which are called as Differentially Expressed Genes (DEGs). Identification of these most significant genes is a crucial task in molecular biology and is a major area of research for bioinformaticians because DEGs are the major source of disease prediction. They help in planning therapeutic strategies for a disease through Gene Regulatory Network (GRN) which is constructed from them. GRN is a graphical representation containing genes as nodes and regulatory interactions between them as edges. GRN helps in knowing how genes regulate each other and in sense maintain underlined state of art working of cells. Deregulation between genes is the cause of major genetic diseases. In this paper we have discussed many methods proposed by researchers for identifying differentially expressing genes based upon changes in their expressions patterns.

References

[1] Tusher V.G., Tibshirani R., and Chu G, “Significance Analysis of Microarrays Applied to the Ionizing Radiation Response,” Proceeding National Academy of Sciences USA, vol. 98, 2001
[2] Kerr,M.K., Martin,M. and Churchill,G.A. “Analysis of variance for gene expression microarray data”, Journal of Computational. Biology., 7,2000.
[3] Ola EiBakry,M.Omair Ahmad and M.N.S. Swamy, “Identification of Differentially Expressed Genes for Time- Course Microarray Data Based on Modified RM ANOVA”, IEEE/ACM Transactions on Computational Biology and Bioinformatics,vol.9, 2012.
[4] Thomas JG, Olson JM, Tapscott SJ, Zhao LP, “An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles”, Genome Research vol.11,2001.
[5] Tusher VG, Tibshirani R, Chu G, “Significance analysis of microarrays applied to the ionizing radiation response”, Proc Natl Acad Sci USA, vol.98, 2001.
[6] Efron B, Tibshirani R, Gross V, Tusher V G, “Empirical Bayes analysis of a microarray experiment”, Journal of American Statistic Association, vol. 96,2001.
[7] Lee ML, Kuo FC,Whitemore GA and Sklar J. “Importance of replication in microarray studies: statistical methods and evidence from repetitive cDNA hybridization”, Proceeding National Academic Science,USA, vol.97,2000.
[8] Qin LX, Kerr KF, “Empirical evaluation of data transformations and ranking statistics for microarray analysis”, Nucleic Acids Res, vol.32, 2004.
[9] Sioson AA, Mane SP, Li P, Sha W, Heath LS, Bohnert HJ, Grene R, “The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison”, BMC Bioinformatics vol.7,2006.
[10] Carl Murie,Owen Woody,Anna Y Lee and Robert Nadon, “Comparison of small n statistical tests of differential expression applied to microarrays”. BMC Bioinformatics,vol.10,2009.
[11] Luis Ospina and Liliana Lopez-Kleine , “Indentification of differentially expressed genes in microarray data in a principal component space”, SpringerPlus, vol.2,2013.
[12] Hisham Al-Mubaid and Noushin Ghaffari, “Identifying the Most Significant genes from Gene expression Profiles for Sample Classification”, Proceeding ,IEEE conference on Granular Computation,2006.
[13] Olga G. Toyanskaya, Mitchell E. Garber, Patrick O. Brown, David Botstein and Russ B. Altman, “Nonparametric methods for identifying differentially expressed genes in microarray”, Bioinofrmatics, vol.18,2002.
[14] Stephen C Billupus, Margaret C Neville, Michael Rudolph, Weston Porter and Pepper Schedin, “Identifying significant temporal variation in time curse microarray data without replicated”, BMC Bioinformatics, vol.10,2009.
[15] Ujjwal Maulik, Anirban Mukhopadhyay,Sangmitra Bandopadhyay, “Combining Pareto-optimal clustering using supervised learning for identifying co-expressed genes”, BMC Bioinformatics, vol.10,2009.
[16] Sofia Wichert, Konstantinos Fokianos and Korbinian Strimmer, “Indentifying periodically expressed transcripts in microarray time series data”, Bioinformatics, vol.20,2004.
[17] Fisher R.A. “Tests of significance in harmonic analysis”, Proceeding Royal Society Publishing, vol.125,1929.
[18] Jerry Chen, and Paul Paolini, “Fourier Analysis of Time Course Microarray data and its Relevance to Gene Expressions Dynamics”, Proceeding ACSESS , 2008.
[19] 19. Ping Ma, Wenxuan Zhong, Jun S. Liu, “Identifying Differentially Expressed Genes in time Course Microarray Data”, Statistic in Bioscience, vol,1. 2009.
[20] Chen Kun, Wang, Jane-Ling, “Identifying Differentially Expressed Genes for Time-course Microarray Data through Functional Data Analysis”, Statistic in biosciences,vol.2,2010.
[21] Jaehee Kim, Robert Todd Ogden and Haseong Kim, “ A method to identify differential expression profiles of timecourse gene data with Fourier transform”, BMC Bioinformatics, col.14,2013.
[22] Shuang Wu, Hulin Wu, “More powerful significant testing for time course gene expression data using functional principal component analysis approaches”, BMC Bioinformatics, vol.14, 2013.
[23] J. Sreekumar and K.K. Jose, “Statistical tests for identification of differentially expressed genes in cDNA microarray experiments”, Indian Journal of Biotechnology, vol.7,no.10,2008.
[24] Biju J., Anuparna S and Govindswami K, “Microarray - chipping in genomics”, Indian Journal of Biotechnology ,vol.1,2002.
[25] Tanaka T.S.,Jaradat S.A., Lim M.K., Kargul G.J.,Wang X., “Genome-wide expression profiling and mid-gestation placenta and embriyo using a 15000 mouse development cDNA microarray”, Proceeding National Academy of science USA, vol.97, 2002.
[26] Devore J. And Peck R. “Satistics: The exploration and analysis of data” 3rd edition, Duxury Press, Pacific Grove,CA,1997.
[27] Welch B.I., “The significance of the difference between two means when population means are unequal”,Biometrika, vol.29,1938.
[28] Anirban Mukhopadhyay and Monalisa Mandal, “Identifying Non-Redundant Gene Markers from Microarray Data: A Multiobjective variable Length PSO-Based Approach”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.11, 2014.
[29] Jaehee Kim, Robert Todd Ogden and Haseong Kim, “A method to identify differential expression profiles of timecourse gene data with Fourier transform”, BMC Bioinformatics, vol.14,no.310,2013.
[30] Xu Han, “ PEM: A General Statistical Approach for Identifying Differentially Expressed Genes in the Time- Course cDNA Microarray Experiment Withour Replicate”, Journal of Bioinformatics and Computational Biology,2006.
[31] Ziv Bar-Joseph, Georg Gerber, Itamar Simon, David K. Gifford and Tommi Jaakkola, “Comparing the Continuous Representation of time-seriess expressions profiles to identify Differentially expressed genes”, PNAS, vol.100,no.18,2003.
[32] J.D. Storey,W.Xiao, J.T. Leek, R.G.Tompkins, and R.W. Davis, “Significance Analysis of Time Course Microarray Experiments”, Proceeding National Academy of science USA, vol. 102,2005.
[33] C. Angelini, D. De Canditiis, M. Mutarelli and M. Pensky, “A Bayesian approach to estimation and testing in timecourse microarray Experiments”, Statistical application in Genetics and Molecular Biology,vol.6,2007.
[34] Han-Yu Chuang, Hongfang Liu, Stuart Brown, Cameron McMunn-Coffran, “Identifying Significant Genes from Microarray Data”, Fourth IEEE Symposium on Bioinformatics and Bioengineering, 2004.
[35] Khalid Raza and Rajni Jaiswal, “Reconstruction and Analysis of Cancer-specific Gene Regulatory Networks from Gene Expression Profiles”, International Journal on Bioinformatics & Biosciences, Vol. 3, No. 2, pp. 25-34, 2013.

Keywords
Gene Regulatory Network (GRN), gene expressions, differentially expressed gene.