Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Expander: from expression microarrays to networks and functions

Abstract

A major challenge in the analysis of gene expression microarray data is to extract meaningful biological knowledge out of the huge volume of raw data. Expander (EXPression ANalyzer and DisplayER) is an integrated software platform for the analysis of gene expression data, which is freely available for academic use. It is designed to support all the stages of microarray data analysis, from raw data normalization to inference of transcriptional regulatory networks. The microarray analysis described in this protocol starts with importing the data into Expander 5.0 and is followed by normalization and filtering. Then, clustering and network-based analyses are performed. The gene groups identified are tested for enrichment in function (based on Gene Ontology), co-regulation (using transcription factor and microRNA target predictions) or co-location. The results of each analysis step can be visualized in a number of ways. The complete protocol can be executed in ≈1 h.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Flow chart of the Expander workflow.
Figure 3: Expander views.
Figure 2: Data sheet view.
Figure 4: Clustering solution visualization.
Figure 5: Enrichment analysis results.
Figure 6: MATISSE results.

Similar content being viewed by others

Accession codes

Accessions

EMBL/GenBank/DDBJ

References

  1. Sharan, R., Maron-Katz, A. & Shamir, R. CLICK and EXPANDER: a system for clustering and visualizing gene expression data. Bioinformatics 19, 1787–1799 (2003).

    Article  CAS  PubMed  Google Scholar 

  2. Sharan, R. & Shamir, R. CLICK: a clustering algorithm with applications to gene expression analysis. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 307–316 (2000).

    CAS  PubMed  Google Scholar 

  3. Elkon, R., Linhart, C., Sharan, R., Shamir, R. & Shiloh, Y. Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome Res. 13, 773–780 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Shamir, R. et al. EXPANDER—an integrative program suite for microarray data analysis. BMC Bioinformatics 6, 232 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Tanay, A., Sharan, R., Kupiec, M. & Shamir, R. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc. Natl. Acad. Sci. USA 101, 2981–2986 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tanay, A., Sharan, R. & Shamir, R. Discovering statistically significant biclusters in gene expression data. Bioinformatics 18 (Suppl 1): S136–S144 (2002).

    Article  PubMed  Google Scholar 

  7. Elkon, R., Linhart, C., Halperin, Y., Shiloh, Y. & Shamir, R. Functional genomic delineation of TLR-induced transcriptional networks. BMC Genomics 8, 394 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Elkon, R. et al. SPIKE—a database, visualization and analysis tool of cellular signaling pathways. BMC Bioinformatics 9, 110 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Muller, F.J. et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature 455, 401–405 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ulitsky, I. & Shamir, R. Identification of functional modules using network topology and high-throughput data. BMC Syst. Biol. 1, 8 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kuehn, H., Liberzon, A., Reich, M. & Mesirov, J.P. Using GenePattern for gene expression analysis. Curr. Protoc. Bioinformatics Chapter 7 Unit 7 12 (2008).

  12. Li, C. & Wong, W.H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 98, 31–36 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Saeed, A.I. et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34, 374–378 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. Stavrum, A.K., Petersen, K., Jonassen, I. & Dysvik, B. Analysis of gene-expression data using J-Express. Curr. Protoc. Bioinformatics Chapter 7 Unit 7 3 (2008).

  15. Rustici, G. et al. Data storage and analysis in ArrayExpress and Expression Profiler. Curr. Protoc. Bioinformatics. Chapter 7 Unit 7 13 (2008).

  16. Tarraga, J. et al. GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Res. 36, W308–W314 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sturn, A., Quackenbush, J. & Trajanoski, Z. Genesis: cluster analysis of microarray data. Bioinformatics 18, 207–208 (2002).

    Article  CAS  PubMed  Google Scholar 

  18. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366–2382 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Stern, S., Dror, T., Stolovicki, E., Brenner, N. & Braun, E. Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge. Mol. Syst. Biol. 3, 106 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Rosenzweig, D. et al. Retooling Leishmania metabolism: from sand fly gut to human macrophage. FASEB J. 22, 590–602 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Oron, E. et al. Genomic analysis of COP9 signalosome function in Drosophila melanogaster reveals a role in temporal regulation of gene expression. Mol. Syst. Biol. 3, 108 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Blum, R. et al. Gene expression signature of human cancer cell lines treated with the ras inhibitor salirasib (S-farnesylthiosalicylic acid). Cancer Res. 67, 3320–3328 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Elkon, R. et al. Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis. Genome Biol. 6, R43 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Blum, R. et al. E2F1 identified by promoter and biochemical analysis as a central target of glioblastoma cell-cycle arrest in response to Ras inhibition. Int. J. Cancer 119, 527–538 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Laurent, L.C. et al. Comprehensive microRNA profiling reveals a unique human embryonic stem cell signature dominated by a single seed sequence. Stem Cells 26, 1506–1516 (2008).

    Article  CAS  PubMed  Google Scholar 

  26. Rodriguez, A. et al. Requirement of bic/microRNA-155 for normal immune function. Science 316, 608–611 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Smedley, D. et al. BioMart—biological queries made easy. BMC Genomics 10, 22 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Schadt, E.E., Li, C., Ellis, B. & Wong, W.H. Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J. Cell. Biochem. Suppl. (Suppl 37): 120–125 (2001).

  30. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Raychaudhuri, S., Stuart, J.M. & Altman, R.B. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 455–466 (2000).

  33. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96, 2907–2912 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. & Church, G.M. Systematic determination of genetic network architecture. Nat. Genet. 22, 281–285 (1999).

    Article  CAS  PubMed  Google Scholar 

  35. Tanay, A., Steinfeld, I., Kupiec, M. & Shamir, R. Integrative analysis of genome-wide experiments in the context of a large high-throughput data compendium. Mol. Syst. Biol. 1, 2005.0002 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hanisch, D., Zien, A., Zimmer, R. & Lengauer, T. Co-clustering of biological networks and gene expression data. Bioinformatics 18 (Suppl 1): S145–S154 (2002).

    Article  PubMed  Google Scholar 

  37. Ideker, T., Ozier, O., Schwikowski, B. & Siegel, A.F. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 (Suppl 1): S233–S240 (2002).

    Article  PubMed  Google Scholar 

  38. Liu, M. et al. Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genet. 3, e96 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Luscombe, N.M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Suzuki, H. et al. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat. Genet. 41, 553–562 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Farh, K.K. et al. The widespread impact of mammalian microRNAs on mRNA repression and evolution. Science 310, 1817–1821 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Lim, L.P. et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433, 769–773 (2005).

    Article  CAS  PubMed  Google Scholar 

  43. Halperin, Y., Linhart, C., Ulitsky, I. & Shamir, R. Allegro: analyzing expression and sequence in concert to discover regulatory programs. Nucleic Acids Res. 37, 1566–1579 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Friedman, R.C., Farh, K.K., Burge, C.B. & Bartel, D.P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92–105 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Grimson, A. et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91–105 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ripley, B. The R project in statistical computing. MSOR Connections. The newsletter of the LTSN Maths, Stats & OR Network 1, 23–25 (2001).

    Article  Google Scholar 

  47. Hermjakob, H. et al. IntAct: an open source molecular interaction database. Nucleic Acids Res. 32, D452–D455 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001).

    Article  CAS  PubMed  Google Scholar 

  49. Quackenbush, J. Computational analysis of microarray data. Nat. Rev. Genet. 2, 418–427 (2001).

    Article  CAS  PubMed  Google Scholar 

  50. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wingender, E. et al. TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res. 28, 316–319 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Warren, M.K. & Ralph, P. Macrophage growth factor CSF-1 stimulates human monocyte production of interferon, tumor necrosis factor, and colony stimulating activity. J. Immunol. 137, 2281–2285 (1986).

    CAS  PubMed  Google Scholar 

  53. Um, H.D., Orenstein, J.M. & Wahl, S.M. Fas mediates apoptosis in human monocytes by a reactive oxygen intermediate dependent pathway. J. Immunol. 156, 3469–3477 (1996).

    CAS  PubMed  Google Scholar 

  54. Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 289–300 (1995).

  56. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gaidatzis, D., van Nimwegen, E., Hausser, J. & Zavolan, M. Inference of miRNA targets using evolutionary conservation and pathway analysis. BMC Bioinformatics 8, 69 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Stark, A., Brennecke, J., Bushati, N., Russell, R.B. & Cohen, S.M. Animal microRNAs confer robustness to gene expression and have a significant impact on 3′UTR evolution. Cell 123, 1133–1146 (2005).

    Article  CAS  PubMed  Google Scholar 

  59. Rajewsky, N. microRNA target predictions in animals. Nat. Genet. 38 (Suppl): S8–S13 (2006).

    Article  CAS  PubMed  Google Scholar 

  60. Baek, D. et al. The impact of microRNAs on protein output. Nature 455, 64–71 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Nielsen, C.B. et al. Determinants of targeting by endogenous and exogenous microRNAs and siRNAs. RNA 13, 1894–1910 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sandberg, R., Neilson, J.R., Sarma, A., Sharp, P.A. & Burge, C.B. Proliferating cells express mRNAs with shortened 3′ untranslated regions and fewer microRNA target sites. Science 320, 1643–1647 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Matthews, L. et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37, D619–D622 (2009).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Israel Steinfeld for his role in the early development of Expander, Metsada Pasmanik-Chor for useful discussions and Akshay Krishnamurthy for commenting on an early version of the protocol. Igor Ulitsky was partially supported by the Edmond J Safra Bioinformatics Program at Tel Aviv University and by the Legacy Heritage Fund. Yosef Shiloh is a Research Professor of the Israel Cancer Research Fund. This study was supported in part by the Israel Science Foundation (Grant No. 802/08) and by the European Community's Seventh Framework Programme (Grants HEALTH-F4-2007-200767 for the APO-SYS project and HEALTH-F4-2009-223575 for the TRIREME project).

Author information

Authors and Affiliations

Authors

Contributions

R.S. conceived and led the project; A.M.-K., S.S. and D.S. developed Expander using software code contributed by R.S., A.T., C.L., R.E. and I.U.; I.U. and R.S. wrote the paper. All the authors contributed to the design of Expander, and all have read and approved the paper.

Corresponding author

Correspondence to Ron Shamir.

Ethics declarations

Competing interests

Expander is available for commercial licensing through Tel Aviv University's technology transfer company.

Supplementary information

Supplementary Data 1

BMM.txt contains a microarray dataset constructed by the Innate Immunity Systems Biology project (http://www.innateimmunity-systemsbiology.org/), in which expression profiles were recorded in murine bone marrow-derived macrophage cells (BMMs) at several time points after exposure to six agents. (TXT 15753 kb)

Supplementary Data 2

MG430_2.0_Probe2EntrezGene.txt contains a mapping of Affymetrix Murine Genome (MG) U430 2.0 probes to Entrez Gene identifiers, taken from BioMart24. (TXT 573 kb)

Supplementary Data 3

Mir155.txt contains a microarray dataset due to Rodriguez et al.22, in which five repeats of Th1 cells deficient for mir-155 are compared with five controls (obtained from the ArrayExpress database, accession number E-TABM-232). (TXT 3337 kb)

Supplementary Data 4

mouse.IntAct.sif a mouse PPI network taken from the IntAct database43, in SIF format. (TXT 35 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ulitsky, I., Maron-Katz, A., Shavit, S. et al. Expander: from expression microarrays to networks and functions. Nat Protoc 5, 303–322 (2010). https://doi.org/10.1038/nprot.2009.230

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2009.230

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics