Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data

Pan, Fei; Kamath, Kiran; Zhang, Kangyu; Pulapura, Sudip; Achar, Avinash; Nunez-Iglesias, Juan; Huang, Yu; Yan, Xifeng; Han, Jiawei; Hu, Haiyan; Xu, Min; Hu, Jianjun; Zhou, Xianghong Jasmine

doi:10.1093/bioinformatics/btl163

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…GODiff (104) (http://fishgenome.org/bioinfo/godiff/index.htm version 1.2) allows investigation of functional differentiation across studies using Gene Ontology annotation. Integrative Array Analyzer (105) (http://zhoulab.usc.edu/iArrayAnalyzer.htm; version 1.1.13) provides data mining and visualization tools to combine studies for simple co-expression analysis and differential expression analysis. For visualization, UCSC Genome Browser (106) and Genome Graphs provide flexible tools to compare and explore multiple genomic studies.…”

Section: Databases and Softwarementioning

confidence: 99%

Comprehensive literature review and statistical considerations for microarray meta-analysis

Begum

Ghosh

Tseng

et al. 2012

Nucleic Acids Research

403

324

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With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

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Section: Databases and Softwarementioning

confidence: 99%

Comprehensive literature review and statistical considerations for microarray meta-analysis

Begum

Ghosh

Tseng

et al. 2012

Nucleic Acids Research

403

324

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“…There is an urgent need for methods supporting the integrative analysis of multiple biological networks. The analysis of multiple networks can be classified into two categories: (1) those studying conservations and evolvements of multiple networks between different species [3]–[8], and (2) those identifying shared network modules or variations of modules across multiple networks of the same species but under different conditions [9]–[15]. The two types of problems face different challenges.…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

et al. 2011

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The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks.

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“…5, 6, 7, 8 Data analysis has become more comprehensive as a direct result of the development of new tools and computational approaches to facilitate analysis within and across various microarray platforms. 9, 10, 11, 12, 13, 14, 15 The Food and Drug Administration(FDA)-led MicroArray Quality Control Consortium (MAQC) study 16, 17, 18, 19, 20, 21 systematically evaluated both of these key areas and concluded that microarrays are capable of generating reproducible measurements within and across different laboratories as well as across different platforms if appropriate analysis procedures were applied. 17, 19 This was a significant outcome as it showed that the same biological findings and insights were revealed, even when data were generated on different microarray platforms.…”

Section: Introductionmentioning

confidence: 99%

Consistency of predictive signature genes and classifiers generated using different microarray platforms

Fan

Chen

et al. 2010

Pharmacogenomics J

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Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80–90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

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Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data

Cited by 14 publications

References 11 publications

Comprehensive literature review and statistical considerations for microarray meta-analysis

Comprehensive literature review and statistical considerations for microarray meta-analysis

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

Consistency of predictive signature genes and classifiers generated using different microarray platforms

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