SGCP: a spectral self-learning method for clustering genes in co-expression networks | BMC Bioinformatics

Emamjomeh A, Saboori Robat E, Zahiri J, Solouki M, Khosravi P. Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data. Plant Biotechnol Rep. 2017;11(2):71–86. https://doi.org/10.1007/s11816-017-0433-z.Article 

Google Scholar 
Panahi B, Hejazi MA. Weighted gene co-expression network analysis of the salt-responsive transcriptomes reveals novel hub genes in green halophytic microalgae Dunaliella salina. Sci Rep. 2021;11(1):1607. https://doi.org/10.1038/s41598-020-80945-3.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ma X, Zhao H, Xu W, You Q, Yan H, Gao Z, Su Z. Co-expression gene network analysis and functional module identification in bamboo growth and development. Front Genet. 2018;9:574. https://doi.org/10.3389/fgene.2018.00574.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parsana P, Ruberman C, Jaffe AE, Schatz MC, Battle A, Leek JT. Addressing confounding artifacts in reconstruction of gene co-expression networks. Genome Biol. 2019;20(1):94. https://doi.org/10.1186/s13059-019-1700-9.Article 
PubMed 
PubMed Central 

Google Scholar 
Liu J, Jing L, Tu X. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease. BMC Cardiovasc Disord. 2016;16(1):54. https://doi.org/10.1186/s12872-016-0217-3.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tieri P, Farina L, Petti M, Astolfi L, Paci P, Castiglione F. Network inference and reconstruction in bioinformatics. In: Ranganathan S, Gribskov M, Nakai K, Schönbach C, editors. Encyclopedia of bioinformatics and computational biology. Oxford: Academic Press; 2019. p. 805–13. https://doi.org/10.1016/B978-0-12-809633-8.20290-2.Chapter 

Google Scholar 
Gat-Viks I, Sharan R, Shamir R. Scoring clustering solutions by their biological relevance. Bioinformatics. 2003;19(18):2381–9. https://doi.org/10.1093/bioinformatics/btg330.Article 
CAS 
PubMed 

Google Scholar 
Dam S, Võsa U, Graaf A, Franke L, Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. 2017;19(4):575–92. https://doi.org/10.1093/bib/bbw139.Article 
CAS 
PubMed Central 

Google Scholar 
Aghaieabiane N, Koutis I. A novel calibration step in gene co-expression network construction. Front Bioinform. 2021. https://doi.org/10.3389/fbinf.2021.704817.Article 
PubMed 
PubMed Central 

Google Scholar 
Khatri P, Drăghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics. 2005;21(18):3587–95. https://doi.org/10.1093/bioinformatics/bti565.Article 
CAS 
PubMed 

Google Scholar 
Botí­a JA, Vandrovcova J, Forabosco P, Guelfi S, D’Sa K, Consortium TUKBE, Hardy J, Lewis CM, Ryten M, Weale ME. An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks. BMC Syst Biol. 2017;11(1), 47. https://doi.org/10.1186/s12918-017-0420-6.Russo PST, Ferreira GR, Cardozo LE, Bürger MC, Arias-Carrasco R, Maruyama SR, Hirata TDC, Lima DS, Passos FM, Fukutani KF, Lever M, Silva JS, Maracaja-Coutinho V, Nakaya HI. CEMiTool: a bioconductor package for performing comprehensive modular co-expression analyses. BMC Bioinform. 2018;19(1):56. https://doi.org/10.1186/s12859-018-2053-1.Article 
CAS 

Google Scholar 
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Ggenet Mol Biol. 2005;4:1.CAS 

Google Scholar 
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9(1):559. https://doi.org/10.1186/1471-2105-9-559.Article 
CAS 

Google Scholar 
Petereit J, Smith S, Harris FC, Schlauch KA. Petal: co-expression network modelling in R. BMC Syst Biol. 2016;10(2):51. https://doi.org/10.1186/s12918-016-0298-8.Article 
PubMed 
PubMed Central 

Google Scholar 
Godichon-Baggioni A, Maugis-Rabusseau C, Rau A. Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. J Appl Stat. 2019;46(1):47–65. https://doi.org/10.1080/02664763.2018.1454894.Article 

Google Scholar 
Watson M. CoXpress: differential co-expression in gene expression data. BMC Bioinform. 2006. https://doi.org/10.1186/1471-2105-7-509.Article 

Google Scholar 
Hou J, Ye X, Li C, Wang Y. K-module algorithm: an additional step to improve the clustering results of WGCNA co-expression networks. Genes. 2021;12(1):87. https://doi.org/10.3390/genes12010087.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics. 2007;24(5):719–20. https://doi.org/10.1093/bioinformatics/btm563.Article 
CAS 
PubMed 

Google Scholar 
Hartigan JA, Wong MA. Algorithm AS 136: a K-means clustering algorithm. Appl Stat. 1979;28(1):100–8. https://doi.org/10.2307/2346830.Article 

Google Scholar 
Cheng CW, Beech DJ, Wheatcroft SB. Advantages of CEMiTool for gene co-expression analysis of RNA-seq data. Comput Biol Med. 2020;125:103975. https://doi.org/10.1016/j.compbiomed.2020.103975.Article 
CAS 
PubMed 

Google Scholar 
Lee JR, Gharan SO, Trevisan L. Multiway spectral partitioning and higher-order cheeger inequalities. J ACM. 2014. https://doi.org/10.1145/2665063.Article 

Google Scholar 
Bishop CM. Pattern recognition and machine learning (information science and statistics). Berlin: Springer; 2006.
Google Scholar 
Abbas-Aghababazadeh F, Li Q, Fridley BL. Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing. PLoS ONE. 2018;13(10):0206312–0206312. https://doi.org/10.1371/journal.pone.0206312.Article 
CAS 

Google Scholar 
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 2013;41:991–5. https://doi.org/10.1093/nar/gks1193.Article 
CAS 

Google Scholar 
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249–64. https://doi.org/10.1093/biostatistics/4.2.249.Article 
PubMed 

Google Scholar 
Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 1996;14(13):1675–80. https://doi.org/10.1038/nbt1296-1675.Article 
CAS 
PubMed 

Google Scholar 
McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA). Biostatistics (Oxford, England). 2010;11(2):242–53. https://doi.org/10.1093/biostatistics/kxp059.Article 
PubMed 

Google Scholar 
Ruff SE, Vasilyev N, Nudler E, Logan SK, Garabedian MJ. PIM1 phosphorylation of the androgen receptor and 14-3-3 ζ regulates gene transcription in prostate cancer. Commun Biol. 2021;4(1):1221. https://doi.org/10.1038/s42003-021-02723-9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herranz R, Larkin OJ, Hill RJ, Lopez-Vidriero I, Loon JJ, Medina FJ. Suboptimal evolutionary novel environments promote singular altered gravity responses of transcriptome during Drosophila metamorphosis. BMC Evol Biol. 2013;13(1):133. https://doi.org/10.1186/1471-2148-13-133.Article 
PubMed 
PubMed Central 

Google Scholar 
Theilhaber J, Rakhade SN, Sudhalter J, Kothari N, Klein P, Pollard J, Jensen FE. Gene expression profiling of a hypoxic seizure model of epilepsy suggests a role for mTOR and Wnt signaling in epileptogenesis. PLoS ONE. 2013;8(9):1–19. https://doi.org/10.1371/journal.pone.0074428.Article 
CAS 

Google Scholar 
Li B, Tsoi LC, Swindell WR, Gudjonsson JE, Tejasvi T, Johnston A, Ding J, Stuart PE, Xing X, Kochkodan JJ, Voorhees JJ, Kang HM, Nair RP, Abecasis GR, Elder JT. Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms. J Invest Dermatol. 2014;134(7):1828–38. https://doi.org/10.1038/jid.2014.28.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kim WJ, Lim JH, Lee JS, Lee S-D, Kim JH, Oh Y-M. Comprehensive analysis of transcriptome sequencing data in the lung tissues of COPD subjects. Int J Genom. 2015;2015:206937. https://doi.org/10.1155/2015/206937.Article 
CAS 

Google Scholar 
Chen Z-X, Oliver B. X chromosome and autosome dosage responses in Drosophila melanogaster heads. G3 (Bethesda Md). 2015;5(6):1057–63. https://doi.org/10.1534/g3.115.017632.Article 
PubMed 

Google Scholar 
Puchalski RB, Shah N, Miller J, Dalley R, Nomura SR, Yoon J-G, Smith KA, Lankerovich M, Bertagnolli D, Bickley K, Boe AF, Brouner K, Butler S, Caldejon S, Chapin M, Datta S, Dee N, Desta T, Dolbeare T, Dotson N, Ebbert A, Feng D, Feng X, Fisher M, Gee G, Goldy J, Gourley L, Gregor BW, Gu G, Hejazinia N, Hohmann J, Hothi P, Howard R, Joines K, Kriedberg A, Kuan L, Lau C, Lee F, Lee H, Lemon T, Long F, Mastan N, Mott E, Murthy C, Ngo K, Olson E, Reding M, Riley Z, Rosen D, Sandman D, Shapovalova N, Slaughterbeck CR, Sodt A, Stockdale G, Szafer A, Wakeman W, Wohnoutka PE, White SJ, Marsh D, Rostomily RC, Ng L, Dang C, Jones A, Keogh B, Gittleman HR, Barnholtz-Sloan JS, Cimino PJ, Uppin MS, Keene CD, Farrokhi FR, Lathia JD, Berens ME, Iavarone A, Bernard A, Lein E, Phillips JW, Rostad SW, Cobbs C, Hawrylycz MJ, Foltz GD. An anatomic transcriptional atlas of human glioblastoma. Science. 2018;360(6389):660–3. https://doi.org/10.1126/science.aaf2666.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Spradling KD, Lumley LA, Robison CL, Meyerhoff JL, Dillman R, James F. Transcriptional analysis of rat piriform cortex following exposure to the organophosphonate anticholinesterase sarin and induction of seizures. J Neuroinflamm. 2011;8:83–83. https://doi.org/10.1186/1742-2094-8-83.Article 
CAS 

Google Scholar 
Miller JA, Guillozet-Bongaarts A, Gibbons LE, Postupna N, Renz A, Beller AE, Sunkin SM, Ng L, Rose SE, Smith KA, Szafer A, Barber C, Bertagnolli D, Bickley K, Brouner K, Caldejon S, Chapin M, Chua ML, Coleman NM, Cudaback E, Cuhaciyan C, Dalley RA, Dee N, Desta T, Dolbeare TA, Dotson NI, Fisher M, Gaudreault N, Gee G, Gilbert TL, Goldy J, Griffin F, Habel C, Haradon Z, Hejazinia N, Hellstern LL, Horvath S, Howard K, Howard R, Johal J, Jorstad NL, Josephsen SR, Kuan CL, Lai F, Lee E, Lee F, Lemon T, Li X, Marshall DA, Melchor J, Mukherjee S, Nyhus J, Pendergraft J, Potekhina L, Rha EY, Rice S, Rosen D, Sapru A, Schantz A, Shen E, Sherfield E, Shi S, Sodt AJ, Thatra N, Tieu M, Wilson AM, Montine TJ, Larson EB, Bernard A, Crane PK, Ellenbogen RG, Keene CD, Lein E. Neuropathological and transcriptomic characteristics of the aged brain. eLife. 2017;6:31126. https://doi.org/10.7554/eLife.31126.Article 

Google Scholar 
Mo A, Nagpal S, Gettler K, Haritunians T, Giri M, Haberman Y, Karns R, Prince J, Arafat D, Hsu N-Y, Chuang L-S, Argmann C, Kasarskis A, Suarez-Farinas M, Gotman N, Mengesha E, Venkateswaran S, Rufo PA, Baker SS, Sauer CG, Markowitz J, Pfefferkorn MD, Rosh JR, Boyle BM, Mack DR, Baldassano RN, Shah S, Leleiko NS, Heyman MB, Griffiths AM, Patel AS, Noe JD, Davis Thomas S, Aronow BJ, Walters TD, McGovern DPB, Hyams JS, Kugathasan S, Cho JH, Denson LA, Gibson G. Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression. Am J Hum Genet. 2021;108(9):1765–79. https://doi.org/10.1016/j.ajhg.2021.07.013.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ratnapriya R, Sosina OA, Starostik MR, Kwicklis M, Kapphahn RJ, Fritsche LG, Walton A, Arvanitis M, Gieser L, Pietraszkiewicz A, Montezuma SR, Chew EY, Battle A, Abecasis GR, Ferrington DA, Chatterjee N, Swaroop A. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat Genet. 2019;51(4):606–10. https://doi.org/10.1038/s41588-019-0351-9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bennett BJ, Farber CR, Ghazalpour A, Pan C, Che N, Wen P, Qi HX, Mutukulu A, Siemers N, Neuhaus I, Yordanova R, Gargalovic P, Pellegrini M, Kirchgessner T, Lusis AJ. Unraveling inflammatory responses using systems genetics and gene-environment interactions in macrophages. Cell. 2012;151(3):658–70. https://doi.org/10.1016/j.cell.2012.08.043.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Song L, Langfelder P, Horvath S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinform. 2012;13(1):328. https://doi.org/10.1186/1471-2105-13-328.Article 
CAS 

Google Scholar 
Hu Y, Zhao H. CCor: a whole genome network-based similarity measure between two genes. Biometrics. 2016;72(4):1216–25. https://doi.org/10.1111/biom.12508.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Falcon S, Gentleman R. Using GOstats to test gene lists for go term association. Bioinformatics. 2006;23(2):257–8. https://doi.org/10.1093/bioinformatics/btl567.Article 
CAS 
PubMed 

Google Scholar 
Schaefer RJ, Michno J-M, Myers CL. Unraveling gene function in agricultural species using gene co-expression networks. Biochimica et Biophysica Acta Gene Regulat Mech. 2017;1860(1):53–63. https://doi.org/10.1016/j.bbagrm.2016.07.016.Article 
CAS 

Google Scholar 
Khanin R, Wit E. How scale-free are biological networks. J Comput Biol. 2006;13(3):810–8. https://doi.org/10.1089/cmb.2006.13.810.Article 
CAS 
PubMed 

Google Scholar 
Lima-Mendez G, Helden J. The powerful law of the power law and other myths in network biology. Mol Biosyst. 2009;5:1482–93. https://doi.org/10.1039/B908681A.Article 
CAS 
PubMed 

Google Scholar 
Broody AD, Clauset A. Scale-free networks are rare. Nat Commun. 2019;10(1):1017–1017. https://doi.org/10.1038/s41467-019-08746-5.Article 
CAS 

Google Scholar 
Clote P. Are RNA networks scale-free? J Math Biol. 2020;80(5):1291–321. https://doi.org/10.1007/s00285-019-01463-z.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fix E, Hodges JL. Discriminatory analysis. Nonparametric discrimination: consistency properties. Int Stat Rev. 1989;57(3):238–47.Article 

Google Scholar 
Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat. 1992;46(3):175–85.Article 

Google Scholar 
Peng C-YJ, Lee KL, Ingersoll GM. An introduction to logistic regression analysis and reporting. J Educ Res. 2002;96(1):3–14. https://doi.org/10.1080/00220670209598786.Article 

Google Scholar 
Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28(5):1–26. https://doi.org/10.18637/jss.v028.i05.Article 

Google Scholar