Integration of differential expression and network structure for ‘omics data analysis

Schena, 1995, Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science, 270, 467, 10.1126/science.270.5235.467 Ritchie, 2015, Limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43, e47, 10.1093/nar/gkv007 Robinson, 2010, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, 26, 139, 10.1093/bioinformatics/btp616 Lui, 2015, DECODE: an integrated differential co-expression and differential expression analysis of gene expression data, BMC Bioinf., 16, 182, 10.1186/s12859-015-0582-4 Chalise, 2012, Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation analysis, OMICS, 16, 363, 10.1089/omi.2011.0126 Ghoshal, 2013, Uncovering the role of elementary processes in network evolution, Sci. Rep., 3, 2920, 10.1038/srep02920 Clarke, 2008, The properties of high-dimensional data spaces: implications for exploring gene and protein expression data, Nat. Rev. Cancer, 8, 37, 10.1038/nrc2294 He, 2019, A statistical test for differential network analysis based on inference of Gaussian graphical model, Sci. Rep., 9 Ji, 2017, JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data, Bioinformatics, 33, 3080, 10.1093/bioinformatics/btx360 Ha, 2015, DINGO: differential network analysis in genomics, Bioinformatics, 31, 3413, 10.1093/bioinformatics/btv406 Zhao, 2014, Direct estimation of differential networks, Biometrika, 101, 253, 10.1093/biomet/asu009 Zhang, 2009, Differential dependency network analysis to identify condition-specific topological changes in biological networks, Bioinformatics, 25, 526, 10.1093/bioinformatics/btn660 Fuller, 2007, Weighted gene coexpression network analysis strategies applied to mouse weight, Mamm. Genome, 18, 463, 10.1007/s00335-007-9043-3 Hood, 2004, Systems biology and new technologies enable predictive and preventative medicine, Science, 306, 640, 10.1126/science.1104635 van Dam, 2018, Gene co-expression analysis for functional classification and gene-disease predictions, Briefings Bioinf., 19, 575 Zuo, 2016, INDEED: integrated differential expression and differential network analysis of omic data for biomarker discovery, Methods, 111, 12, 10.1016/j.ymeth.2016.08.015 Cun, 2013, Network and data integration for biomarker signature discovery via network smoothed T-statistics, PLoS One, 8, 10.1371/journal.pone.0073074 Page L, 1998 Di Poto, 2017, Metabolomic characterization of hepatocellular carcinoma in patients with liver cirrhosis for biomarker discovery, Cancer Epidemiol. Biomarkers Prev., 26, 675, 10.1158/1055-9965.EPI-16-0366 van de Vijver, 2002, A gene-expression signature as a predictor of survival in breast cancer, N. Engl. J. Med., 347, 1999, 10.1056/NEJMoa021967 Lauritzen, 1996 Friedman, 2008, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, 9, 432, 10.1093/biostatistics/kxm045 Siska, 2016, The discordant method: a novel approach for differential correlation, Bioinformatics, 32, 690, 10.1093/bioinformatics/btv633 McKenzie, 2016, DGCA: A comprehensive R package for differential gene correlation analysis, BMC Syst. Biol., 10, 106, 10.1186/s12918-016-0349-1 Fukushima, 2013, DiffCorr: an R package to analyze and visualize differential correlations in biological networks, Gene, 518, 209, 10.1016/j.gene.2012.11.028 Tony Cai, 2011, A constrained ℓ₁ minimization approach to sparse precision matrix estimation, J. Am. Stat. Assoc., 106, 594, 10.1198/jasa.2011.tm10155 Yuan, 2010, High dimensional inverse covariance matrix estimation via linear programming, J. Mach. Learn. Res., 11, 2261 Cai, 2010, Optimal rates of convergence for covariance matrix estimation, Ann. Stat., 38, 2118, 10.1214/09-AOS752 Peter, 2008, Regularized estimation of large covariance matrices, Ann. Stat., 36, 199 Li, 2018, INDEED: R package for network based differential expression analysis, Proceedings (IEEE Int Conf Bioinformatics Biomed), 2018, 2709 Morrison, 2005, GeneRank: using search engine technology for the analysis of microarray experiments, BMC Bioinf., 6, 233, 10.1186/1471-2105-6-233 Winter, 2012, Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes, PLoS Comput. Biol., 8, 10.1371/journal.pcbi.1002511 Zhao, 2012, The huge package for high-dimensional undirected graph estimation in R, J. Mach. Learn. Res., 13, 1059 Liesenfeld, 2013, Review of mass spectrometry-based metabolomics in cancer research, Cancer Epidemiol. Biomarkers Prev., 22, 2182, 10.1158/1055-9965.EPI-13-0584 Nezami Ranjbar, 2015, GC-MS based plasma metabolomics for identification of candidate biomarkers for hepatocellular carcinoma in Egyptian cohort, PLoS One, 10, 10.1371/journal.pone.0127299 Cancer Genome Atlas Research, 2008, Comprehensive genomic characterization defines human glioblastoma genes and core pathways, Nature, 455, 1061, 10.1038/nature07385 Wang, 2014, Similarity network fusion for aggregating data types on a genomic scale, Nat. Methods, 11, 333, 10.1038/nmeth.2810 Nebert, 2000, Extreme discordant phenotype methodology: an intuitive approach to clinical pharmacogenetics, Eur. J. Pharmacol., 410, 107, 10.1016/S0014-2999(00)00809-8 Jeanmougin, 2010, Should we abandon the t-test in the analysis of gene expression microarray data: a comparison of variance modeling strategies, PLoS One, 5, 10.1371/journal.pone.0012336 Cancer Genome Atlas, 2012, Comprehensive molecular portraits of human breast tumours, Nature, 490, 61, 10.1038/nature11412 Verhaak, 2010, Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1, Cancer Cell, 17, 98, 10.1016/j.ccr.2009.12.020 Quan, 2017, Suppression of p53-inducible gene 3 is significant for glioblastoma progression and predicts poor patient prognosis, Tumour Biol, 39, 10.1177/1010428317694572 Murphy, 2013, Activation of executioner caspases is a predictor of progression-free survival in glioblastoma patients: a systems medicine approach, Cell Death Dis., 4, e629, 10.1038/cddis.2013.157 Park, 2021, Crk and CrkL as therapeutic targets for cancer treatment, Cells, 10, 10.3390/cells10040739 Kalluri, 2016, Effect of D609 on the expression of GADD45beta protein: potential inhibitory role in the growth of glioblastoma cancer stem like cells, Eur. J. Pharmacol., 791, 510, 10.1016/j.ejphar.2016.09.026 Ji, 2021, miR497/MIR497HG inhibits glioma cell proliferation by targeting CCNE1 and the miR588/TUSC1 axis, Oncol. Rep., 46, 10.3892/or.2021.8206 Wang, 2021, GADD45B is a potential diagnostic and therapeutic target gene in chemotherapy-resistant prostate cancer, Front. Cell Dev. Biol., 9 Hollern, 2019, E2F1 drives breast cancer metastasis by regulating the target gene FGF13 and altering cell migration, Sci. Rep., 9, 10.1038/s41598-019-47218-0 Vijay, 2019, GSK3beta regulates epithelial-mesenchymal transition and cancer stem cell properties in triple-negative breast cancer, Breast Cancer Res., 21, 37, 10.1186/s13058-019-1125-0 Liu, 2021, Overexpression of Lrp5 enhanced the anti-breast cancer effects of osteocytes in bone, Bone Res, 9, 32, 10.1038/s41413-021-00152-2 Hollern, 2014, The E2F transcription factors regulate tumor development and metastasis in a mouse model of metastatic breast cancer, Mol. Cell Biol., 34, 3229, 10.1128/MCB.00737-14 Zhu, 2014, Challenging role of Wnt5a and its signaling pathway in cancer metastasis (Review), Exp. Ther. Med., 8, 3, 10.3892/etm.2014.1676 Song, 2018, FGF18 enhances migration and the epithelial-mesenchymal transition in breast cancer by regulating akt/GSK3beta/beta-catenin signaling, Cell. Physiol. Biochem., 49, 1019, 10.1159/000493286 Vandermoere, 2005, The antiapoptotic effect of fibroblast growth factor-2 is mediated through nuclear factor-kappaB activation induced via interaction between Akt and IkappaB kinase-beta in breast cancer cells, Oncogene, 24, 5482, 10.1038/sj.onc.1208713 Fowler, 2020, Progesterone receptor gene variants in metastatic estrogen receptor positive breast cancer, Horm Cancer, 11, 63, 10.1007/s12672-020-00377-3 Fisler, 2018, Elucidating feed-forward apoptosis signatures in breast cancer datasets: higher FOS expression associated with a better outcome, Oncol. Lett., 16, 2757 Tashiro, 2003, Regulation of FGF receptor-2 expression by transcription factor E2F-1, Oncogene, 22, 5630, 10.1038/sj.onc.1206636 Rahmatallah, 2014, Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets, Bioinformatics, 30, 360, 10.1093/bioinformatics/btt687 Amar, 2013, Dissection of regulatory networks that are altered in disease via differential co-expression, PLoS Comput. Biol., 9, 10.1371/journal.pcbi.1002955 Tesson, 2010, DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules, BMC Bioinf., 11, 497, 10.1186/1471-2105-11-497 Watson, 2006, CoXpress: differential co-expression in gene expression data, BMC Bioinf., 7, 509, 10.1186/1471-2105-7-509 Yang, 2020, Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data, Briefings Bioinf., 21, 1058, 10.1093/bib/bbz049 Yang, 2020, NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data, Nucleic Acids Res., 48, W436, 10.1093/nar/gkaa258 Fu, 2022, Optimization of metabolomic data processing using NOREVA, Nat. Protoc., 17, 129, 10.1038/s41596-021-00636-9 Li, 2022, POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability, Briefings Bioinf., 23, 10.1093/bib/bbac040 Tang, 2019, Simultaneous improvement in the precision, accuracy, and robustness of label-free proteome quantification by optimizing data manipulation chains, Mol. Cell. Proteomics, 18, 1683, 10.1074/mcp.RA118.001169 Chalise, 2014, Integrative clustering methods for high-dimensional molecular data, Transl. Cancer Res., 3, 202