Abstract
Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. This article presents a co-expression network construction and a biologically relevant network module extraction technique based on fuzzy set theoretic approach. The technique is able to handle both positive and negative correlations among genes. The constructed network for some benchmark gene expression datasets have been validated using topological internal and external measures. The effectiveness of network module extraction technique has been established in terms of well-known p-value, Q-value and topological statistics.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Benjamini Y and Hochberg Y 1995 Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Series B (Methodological) 57 289–300
Berriz GF, King OD, Bryant B, Sander C and Roth FP 2003 Characterizing gene sets with FuncAssociate. Bioinformatics 19 2502–2504
Bezdek JC 1981 Pattern recognition with fuzzy objective function algorithms (Norwell, MA, USA: Kluwer Academic Publishers)
Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg T G, Gabrielian AE et al. 1998 A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2 65–73
Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO and Herskowitz I 1998 The transcriptional program of sporulation in budding yeast. Science 282 699–705
DeSarbo WS, Carroll JD, Clark LA and Green PE 1984 Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables. Psychometrika 49 57–78
Fu L and Medico E 2007 FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics 8 3
Gasch AP and Eisen MP 2002 Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol. 3 0059.1–0059.22
Glaab E, Baudot A, Krasnogor N and Valencia A 2010 TopoGSA: network topological gene set analysis. Bioinformatics 26 1271–1272
Hall LO, Ozyurt B and Bezdek JC 1999 Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput. 3 103–112
Horvath S and Dong J 2008 Geometric Interpretation of gene coexpression network analysis. PLoS Comput. Biol. 4 e1000117
Huang JZ, Ng MK, Rong H and Li Z 2005 Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Analysis Machine Intelligence 27 657–668
Jing L, Ng MK and Huang JZ 2007 An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans. Knowl. Data Eng. 19 1026–1041
Mahanta P, Ahmed HA, Bhattacharyya DK and Kalita JK 2012 An effective method for network module extraction from microarray data. BMC Bioinformatics 13 S4
Maji P and Paul S 2012 Rough-fuzzy clustering for grouping functionally similar genes from microarray data. IEEE/ACM Trans. Comput. Biol. Bioinformatics 10 286–299
Nasser S, Alkhaldi R and Vert G 2006 A modified fuzzy k-means clustering using expectation maximization. Fuzzy Syst. doi: 10.1109/FUZZY.2006.1681719
Presson A, Sobel E, Papp J, Suarez C, Whistler T, Rajeevan M, Vernon S and Horvath S 2008 Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome. BMC Syst. Biol. 2 95
Ruan J, Dean A and Zhang W 2010 A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst. Biol. 4 8
Tavazoie S, Hughes JD, Campbell MJ, Cho RJ and Church GM 1999 Systematic determination of genetic network architecture. Nat. Genet. 22 281–285
Wang Q, Ye Y, Huang JZ and Feng S 2010 Fuzzy soft subspace clustering method for gene co-expression network analysis; Bioinformatics and Biomedicine Workshops (BIBMW) 2010 IEEE International Conference on (IEEE) pp 47–50
Warde−Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, et al 2010 The Gen-eMANIA prediction server: biological network integration for gene prioritiza-tion and predicting gene function. Nucleic Acids Res. 38 W214–W220
Zhang B and Horvath S 2005 A general framework for weighted gene co-expression network analysis. Stat. App. Genet. Mol. Biol. 4 Article17
Acknowledgements
This article is an outcome of a research project supported by DST, India, in collaboration with CSCR, ISI, Kolkata.
Author information
Authors and Affiliations
Corresponding author
Additional information
Corresponding editor: SHEKHAR C MANDE
MS received 22 October 2012; accepted 05 February 2014
Corresponding editor:Shekhar C Mande
[Mahanta P, Ahmed HA, Bhattacharyya DK and Ghosh A 2014 FUMET: A fuzzy network module extractiontechnique for gene expression data. J. Biosci. 39 1–14] DOI 10.1007/s12038-014-9423-2
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM 1
(PDF 2.04 mb)
Rights and permissions
About this article
Cite this article
Mahanta, P., Ahmed, H.A., Bhattacharyya, D.K. et al. FUMET: A fuzzy network module extraction technique for gene expression data. J Biosci 39, 351–364 (2014). https://doi.org/10.1007/s12038-014-9423-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12038-014-9423-2