1. Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks

In this century, a high-throughput technology is being harnessed in various applications to solve a diverse range of biological problems and to explore biological phenomena. Next-generation sequencers (NGS) can be used for measuring and monitoring thousands of small molecules simultaneously [1–4] and large genomic sequences can be acquired quickly and routinely. RNA sequencing with NGS (RNA-seq) measures nearly every transcript of cellular systems (i.e., transcriptome) [5–7]. The term omics refers to the comprehensive analysis of biological systems and approaches including genomics, transcriptomics, and metabolomics that have become a promising way to inspect complex network interactions in cellular systems. To understand the organizing principle of cellular functions at different levels, an integrative approach with large-scale omics data including genomics, transcriptomics, proteomics, and metabolomics, is required [8–10]. Although it means different things to different scientists, systems biology [11] is the study of the behavior of complex biological processes using integrated approaches and a collection of omics-based data sets, quantitative measurements of the behavior of interacting cellular components, and mathematical/computational models to predict and describe complex dynamic behaviors.

[1]  R. Steuer,et al.  Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation. , 2006, Bio Systems.

[2]  Christina Kendziorski,et al.  R/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression , 2012, Bioinform..

[3]  Liang Chen,et al.  A statistical method for identifying differential gene-gene co-expression patterns , 2004, Bioinform..

[4]  S. Kanaya,et al.  Characterizing gene coexpression modules in Oryza sativa based on a graph-clustering approach , 2009 .

[5]  Ker-Chau Li,et al.  Genome-wide coexpression dynamics: Theory and application , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[6]  M. Stitt.  Systems-integration of plant metabolism: means, motive and opportunity. , 2013, Current opinion in plant biology.

[7]  Korbinian Strimmer,et al.  fdrtool: a versatile R package for estimating local and tail area-based false discovery rates , 2008, Bioinform..

[8]  A. G. de la Fuente.  From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. , 2010, Trends in genetics : TIG.

[9]  A. Fernie,et al.  Co-expression and co-responses: within and beyond transcription , 2012, Front. Plant Sci..

[10]  Mohammad Asim,et al.  Differential C3NET reveals disease networks of direct physical interactions , 2011, BMC Bioinformatics.

[11]  Hui Yu,et al.  Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs , 2011, BMC Bioinformatics.

[12]  Monica Chagoyen,et al.  Tools for the functional interpretation of metabolomic experiments , 2013, Briefings Bioinform..

[13]  Kazuki Saito,et al.  Transcriptome data modeling for targeted plant metabolic engineering. , 2013, Current opinion in biotechnology.

[14]  Gary D Bader,et al.  A travel guide to Cytoscape plugins , 2012, Nature Methods.

[15]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[16]  M. Hirai,et al.  Decoding genes with coexpression networks and metabolomics - 'majority report by precogs'. , 2008, Trends in plant science.

[17]  Jonathan D. G. Jones,et al.  Evidence for Network Evolution in an Arabidopsis Interactome Map , 2011, Science.

[18]  E. Marcotte,et al.  Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana , 2010, Nature Biotechnology.

[19]  Thomas Werner,et al.  Next generation sequencing in functional genomics , 2010, Briefings Bioinform..

[20]  Takayuki Tohge,et al.  Metabolomics reveals comprehensive reprogramming involving two independent metabolic responses of Arabidopsis to UV-B light. , 2011, The Plant journal : for cell and molecular biology.

[21]  Qingqiu Gong,et al.  An Arabidopsis gene network based on the graphical Gaussian model. , 2007, Genome research.

[22]  Zhongming Zhao,et al.  DCGL v2.0: An R Package for Unveiling Differential Regulation from Differential Co-expression , 2013, PloS one.

[23]  Staffan Persson,et al.  Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. , 2009, Plant, cell & environment.

[24]  Atul J. Butte,et al.  Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges , 2012, PLoS Comput. Biol..

[25]  Atsushi Fukushima,et al.  A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches. , 2014, Journal of experimental botany.

[26]  A. Fukushima.  DiffCorr: an R package to analyze and visualize differential correlations in biological networks. , 2013, Gene.

[27]  Cole Trapnell,et al.  Computational methods for transcriptome annotation and quantification using RNA-seq , 2011, Nature Methods.

[28]  Rainer Spang,et al.  Finding disease specific alterations in the co-expression of genes , 2004, ISMB/ECCB.

[29]  Masanori Arita,et al.  Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis , 2010, BMC Bioinformatics.

[30]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[31]  Francesca Chiaromonte,et al.  Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis , 2007, Genome Biology.

[32]  A. Fukushima,et al.  Current challenges and future potential of tomato breeding using omics approaches , 2013, Breeding science.

[33]  Marie E. Bolger,et al.  Plant genome sequencing - applications for crop improvement. , 2014, Current opinion in biotechnology.

[34]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[35]  Sean R. Davis,et al.  GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor , 2007, Bioinform..

[36]  R. Lister,et al.  Next is now: new technologies for sequencing of genomes, transcriptomes, and beyond. , 2009, Current opinion in plant biology.

[37]  Julie A. Dickerson,et al.  Arabidopsis gene co-expression network and its functional modules , 2009, BMC Bioinformatics.

[38]  A. Fukushima,et al.  Exploring Tomato Gene Functions Based on Coexpression Modules Using Graph Clustering and Differential Coexpression Approaches1[C][W][OA] , 2012, Plant Physiology.

[39]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[40]  Y. Higashi,et al.  Network analysis for gene discovery in plant-specialized metabolism. , 2013, Plant, cell & environment.

[41]  Chandan K. Reddy,et al.  Ranking differential Hubs in gene Co-Expression Networks , 2012, J. Bioinform. Comput. Biol..

[42]  Zoran Nikoloski,et al.  Network analysis: tackling complex data to study plant metabolism. , 2013, Trends in biotechnology.

[43]  O. Fiehn,et al.  Differential metabolic networks unravel the effects of silent plant phenotypes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[44]  Eve Syrkin Wurtele,et al.  Regulon organization of Arabidopsis , 2008, BMC Plant Biology.

[45]  Kazuki Saito,et al.  Integrated omics approaches in plant systems biology. , 2009, Current opinion in chemical biology.

[46]  N. Friedman,et al.  Comprehensive comparative analysis of strand-specific RNA sequencing methods , 2010, Nature Methods.

[47]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[48]  Robert Gentleman,et al.  Using GOstats to test gene lists for GO term association , 2007, Bioinform..

[49]  H. Kitano.  Systems Biology: A Brief Overview , 2002, Science.

[50]  Ju Han Kim,et al.  Identifying set-wise differential co-expression in gene expression microarray data , 2009, BMC Bioinformatics.

[51]  Stefan R. Henz,et al.  A gene expression map of Arabidopsis thaliana development , 2005, Nature Genetics.

[52]  Michael Kaufmann,et al.  yFiles - Visualization and Automatic Layout of Graphs , 2001, Graph Drawing Software.

[53]  Masanori Arita,et al.  Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach , 2011, BMC Systems Biology.

[54]  Yoshiyuki Ogata,et al.  Approaches for extracting practical information from gene co-expression networks in plant biology. , 2007, Plant & cell physiology.

[55]  Michael Watson,et al.  CoXpress: differential co-expression in gene expression data , 2006, BMC Bioinformatics.

[56]  T. Ideker,et al.  Differential network biology , 2012, Molecular systems biology.

[57]  Xiaoli Li,et al.  Computational approaches for detecting protein complexes from protein interaction networks: a survey , 2010, BMC Genomics.

[58]  Joachim Selbig,et al.  pcaMethods - a bioconductor package providing PCA methods for incomplete data , 2007, Bioinform..

[59]  C. Shelton,et al.  Annotating Genes of Known and Unknown Function by Large-Scale Coexpression Analysis1[W][OA] , 2008, Plant Physiology.

[60]  A. Fernie,et al.  Systems analysis of metabolic phenotypes: what have we learnt? , 2014, Trends in plant science.

[61]  Kevin V. Solomon,et al.  Extracting data from the muck: deriving biological insight from complex microbial communities and non-model organisms with next generation sequencing. , 2014, Current opinion in biotechnology.

[62]  Hui Yu,et al.  Bioinformatics Applications Note Gene Expression Dcgl: an R Package for Identifying Differentially Coexpressed Genes and Links from Gene Expression Microarray Data , 2022 .

[63]  Peter Langfelder,et al.  Eigengene networks for studying the relationships between co-expression modules , 2007, BMC Systems Biology.

[64]  Sangsoo Kim,et al.  Gene expression Differential coexpression analysis using microarray data and its application to human cancer , 2005 .

[65]  Anton J. Enright,et al.  An efficient algorithm for large-scale detection of protein families. , 2002, Nucleic acids research.

[66]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[67]  Rainer Breitling,et al.  DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules , 2010, BMC Bioinformatics.

[68]  Christina Kendziorski,et al.  An Empirical Bayesian Approach for Identifying Differential Coexpression in High‐Throughput Experiments , 2012, Biometrics.

[69]  Makoto Kobayashi,et al.  Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana , 2007, BMC Systems Biology.

[70]  Paul Pavlidis,et al.  A methodology for the analysis of differential coexpression across the human lifespan , 2009, BMC Bioinformatics.

[71]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[72]  Koji Tsuda,et al.  ROS-DET: robust detector of switching mechanisms in gene expression , 2011, Nucleic acids research.

[73]  Shigehiko Kanaya,et al.  Development and implementation of an algorithm for detection of protein complexes in large interaction networks , 2006, BMC Bioinformatics.

[74]  Weixiong Zhang,et al.  Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks , 2010, BMC Systems Biology.

[75]  Taru Tukiainen,et al.  A Differential Network Approach to Exploring Differences between Biological States: An Application to Prediabetes , 2011, PloS one.

[76]  Zhiping Weng,et al.  Gene set enrichment analysis: performance evaluation and usage guidelines , 2012, Briefings Bioinform..

[77]  Ross Lazarus,et al.  Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes , 2011, BMC Systems Biology.

[78]  Hiroshi Mamitsuka,et al.  Detecting Differentially Coexpressed Genes from Labeled Expression Data: A Brief Review , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[79]  Hyun Uk Kim,et al.  Data integration and analysis of biological networks. , 2010, Current opinion in biotechnology.