Skip to main content
SpringerLink
Log in

Integrative Enrichment Analysis of Intra- and Inter- Tissues’ Differentially Expressed Genes Based on Perceptron

  • Conference paper
  • First Online:
Book cover Intelligent Computing Theories and Application (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11644))

Included in the following conference series:

  • 1473 Accesses

Abstract

Recent researches in biomedicine have indicated that the molecular basis of chronic complex diseases can also exist outside the disease-impacted tissues. Differentially expressed genes between different tissues can help revealing the molecular basis of chronic diseases. Therefore, it is essential to develop new computational methods for the integrative analysis of multi-tissues gene expression data, exploring the association between differentially expressed genes in different tissues and chronic disease. To analysis of intra- and inter-tissues’ differentially expressed genes, we designed an integrative enrichment analysis method based on perceptron (IEAP). Firstly, we calculated the differential expression scores of genes using fold-change approach with intra- and inter- tissues’ gene expression data. The differential expression scores are seen as features of the corresponding gene. Next, we integrated all differential expression scores of gene to get differential expression enrichment score. Finally, we ranked the genes according to the enrichment score. Top ranking genes are candidate disease-risk genes. Computational experiments shown that genes differentially expressed between striatum and liver of normal samples are more likely to be Huntington’s disease-associated genes, and the prediction precision could be further improved by IEAP. We finally obtained five disease-associated genes, two of which have been reported to be related with Huntington’s disease.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ross, C.A., et al.: Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat. Rev. Neurol. 10(4), 204 (2014)

    Article  Google Scholar 

  2. Seredenina, T., Luthicarter, R.: What have we learned from gene expression profiles in huntington’s disease? Neurobiol. Dis. 45(1), 83 (2012)

    Article  Google Scholar 

  3. Wang, X., Huang, T., Bu, G., Xu, H.: Dysregulation of protein trafficking in neurodegeneration. Mol. Neurodegener. 9(1), 31 (2014)

    Article  Google Scholar 

  4. Difiglia, M., et al.: Aggregation of huntingtin in neuronal intranuclear inclusions and dystrophic neurites in brain. Science 277(5334), 1990 (1997)

    Article  Google Scholar 

  5. Kugler, K.G., Mueller, L.A.J., Graber, A., Dehmer, M.: Integrative network biology: graph prototyping for co-expression cancer networks. PLoS ONE 6(7), 22843 (2011)

    Article  Google Scholar 

  6. Liu, Z.P.: Identifying network-based biomarkers of complex diseases from high- throughput data. Biomark. Med. 10(6), 633–650 (2016)

    Article  Google Scholar 

  7. Xulvibrunet, R., Li, H.: Co-expression networks: graph properties and topological comparisons. Bioinformatics 26(2), 205–214 (2010)

    Article  Google Scholar 

  8. Ray, M., Zhang, W.: Analysis of alzheimer’s disease severity across brain regions by topological analysis of gene co-expression networks. BMC Syst. Biol. 4(1), 136 (2010)

    Article  MathSciNet  Google Scholar 

  9. Ideker, T., Krogan, N.J.: Differential network biology. Mol. Syst. Biol. 8(1), 565 (2012)

    Google Scholar 

  10. Gwinner, F., et al.: Network-based analysis of omics data: the lean method. Bioinformatics 33(5), 701–709 (2017)

    Google Scholar 

  11. Huang, D.W., Sherman, B.T., Lempicki, R.A.: Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nat. Protoc. 4(1), 44 (2009)

    Article  Google Scholar 

  12. Bevilacqua, V., Pannarale, P., Abbrescia, M., Cava, C., Paradiso, A., Tommasi, S.: Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression. BMC Bioinformatics 13(S7), 9 (2012)

    Article  Google Scholar 

  13. Maulik, U., Mukhopadhyay, A., Chakraborty, D.: Gene-expression-based cancer subtypes prediction through feature selection and transductive SVM. IEEE Trans. Bio-Med. Eng. 60(4), 1111–1117 (2013)

    Article  Google Scholar 

  14. Jiang, X., Zhang, H., Zhang, Z., Quan, X.: Flexible non-negative matrix factorization to unravel disease-related genes. IEEE Trans. Comput. Biol. Bioinf. 1(1), 1–11 (2018)

    Google Scholar 

  15. Wang, H.Q., Zheng, C.H., Zhao, X.M.: jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data. Bioinformatics 31(4), 572 (2015)

    Article  Google Scholar 

  16. Jiang, X., Zhang, H., Duan, F., Quan, X.: Identify huntington’s disease associated genes based on restricted boltzmann machine with RNA-seq data. BMC Bioinformatics 18(1), 447 (2017)

    Article  Google Scholar 

  17. Liang, M., Li, Z., Chen, T., Zeng, J.: Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans. Comput. Biol. Bioinf. 12(4), 928–937 (2015)

    Article  Google Scholar 

  18. Battle, A., Brown, C.D., Engelhardt, B.E., Montgomery, S.B.: Genetic effects on gene expression across human tissues. Nature 550(7675), 204–213 (2017)

    Article  Google Scholar 

  19. Tan, M.H., et al.: Dynamic landscape and regulation of RNA editing in mammals. Nature 550(7675), 249–254 (2017)

    Article  Google Scholar 

  20. Tukiainen, T., et al.: Landscape of X chromosome inactivation across human tissues. Nature 550(7675), 244 (2017)

    Article  Google Scholar 

  21. Li, X., et al.: The impact of rare variation on gene expression across tissues. Nature 550(7675), 239–243 (2016)

    Article  Google Scholar 

  22. Hong, F., Breitling, R.: A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics 24(3), 374 (2008)

    Article  Google Scholar 

  23. Hanley, J.A., Mcneil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29 (1982)

    Article  Google Scholar 

  24. Langfelder, P., et al.: Integrated genomics and proteomics de ne huntingtin cag length-dependent networks in mice. Nat. Neurosci. 19(4), 623 (2016)

    Article  Google Scholar 

  25. Yamamoto, S., et al.: A drosophila genetic resource of mutants to study mechanisms underlying human genetic diseases. Cell 159(1), 200–214 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 20030, China

    Xue Jiang, Weihao Pan, Miao Chen, Weidi Wang, Weichen Song & Guan Ning Lin

  2. Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 20030, China

    Xue Jiang, Weidi Wang, Weichen Song & Guan Ning Lin

  3. Brain Science of Technology Research Center, Shanghai Jiao Tong University, Shanghai, 200030, China

    Xue Jiang, Weidi Wang, Weichen Song & Guan Ning Lin

Authors
  1. Xue Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weihao Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weidi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weichen Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guan Ning Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guan Ning Lin .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, X., Pan, W., Chen, M., Wang, W., Song, W., Lin, G.N. (2019). Integrative Enrichment Analysis of Intra- and Inter- Tissues’ Differentially Expressed Genes Based on Perceptron. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26969-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26968-5

  • Online ISBN: 978-3-030-26969-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation