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Robustness of Pathway Enrichment Analysis to Transcriptome-Wide Gene Expression Platform

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Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020) (PACBB 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1240))

Abstract

Progress in technology and increasing knowledge of molecular biology resulted in a variety of transcriptomic expression profiling platforms. Bioinformatics supports this development with various computational techniques like, for example, pathway enrichment analysis. The presented study shows a comprehensive comparison of different transcriptomic platforms and methods of signalling pathway enrichment analysis. All analyses were performed on carefully chosen datasets with control after the most common confounding factors (disease phenotype, experimental design and sample size). It was shown that the most significant differences are observed between enrichment methods themselves rather than transcriptomic profiling platforms. Out of tested methods, CERNO and its combination with SPIA show the highest robustness to the molecular biology technique while ORA enrichment method shows little affection to tested platforms.

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References

  1. Dziuda, D.M.: Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data. Wiley, Hoboken (2010)

    Book  Google Scholar 

  2. Schena, M., et al.: Microarrays: biotechnology’s discovery platform for functional genomics. Trends Biotechnol. 16(7), 301–306 (1988)

    Article  Google Scholar 

  3. Zhang, Z.H., et al.: A comparative study of techniques for differential expression analysis on RNA-Seq data. PloS one 9(8), e103207 (2014)

    Google Scholar 

  4. Robertson, G., et al.: De novo assembly and analysis of RNA-seq data. Nat. Methods 7, 909–912 (2010)

    Article  Google Scholar 

  5. Zhang, W.: Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol. 16(1), 133 (2015)

    Article  Google Scholar 

  6. Anders, S., Huber, W.: Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

    Article  Google Scholar 

  7. Malone, J.H., Brian, O.: Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol. 9(1), 34 (2011)

    Article  Google Scholar 

  8. Khatri, P., Sirota, M., Butte A.J.: Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8(2), e1002375 (2012)

    Google Scholar 

  9. Huang, D.W., et al.: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37(1), 1–13 (2009)

    Article  Google Scholar 

  10. Khatri, P., Draghici, S., Ostermeier, G.C., Krawetz, S.A.: Profiling gene expression using onto-express. Genomics 79(2), 266–270 (2002)

    Article  Google Scholar 

  11. Hung, J.H., et al.: Identification of functional modules that correlate with phenotypic difference: the influence of network topology. Genome Biol. 11(2), R23 (2010)

    Article  Google Scholar 

  12. Ihnatova, I., Popovici, V., Budinska, E.: A critical comparison of topology-based pathway analysis methods. PloS one 13(1), e0191154 (2018)

    Google Scholar 

  13. Maciejewski, H.: Gene set analysis methods: statistical models and methodological differences. Briefings Bioinform. 15(4), 504–5018 (2014)

    Article  Google Scholar 

  14. Zyla, J., et al.: Ranking metrics in gene set enrichment analysis: do they matter? BMC Bioinform. 18(1), 256 (2017)

    Article  Google Scholar 

  15. Hung, J.H., et al.: Gene set enrichment analysis: performance evaluation and usage guidelines. Briefings Bioinform. 13(3), 281–291 (2012)

    Article  Google Scholar 

  16. Tarca, A.L., Bhatti, G., Romero, R.: A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PloS one 8(11), e79217 (2013)

    Google Scholar 

  17. Geistlinger, L., et al.: Toward a gold standard for benchmarking gene set enrichment analysis. Brief. Bioinform., bbz158 (2020). https://doi.org/10.1093/bib/bbz158

  18. Tarca, A.L., Draghici, S., Bhatti, G., Romero, R.: Down-weighting overlapping genes improves gene set analysis. BMC Bioinform. 13(1), 136 (2012)

    Article  Google Scholar 

  19. Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., Smyth, G.K.: Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Rese. 43(7), e47 (2015). https://doi.org/10.1093/nar/gkv007

    Article  Google Scholar 

  20. McCarthy, D.J., Chen, Y., Smyth, G.K.: Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40(10), 4288–4297 (2012)

    Article  Google Scholar 

  21. Kanehisa, F.M., et al.: KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017)

    Article  Google Scholar 

  22. Zyla, J., et al.: Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms. Bioinformatics 35(24), 5146–5154 (2019)

    Article  Google Scholar 

  23. Maleki, F., et al.: Size matters: how sample size affects the reproducibility and specificity of gene set analysis. Hum. Genomics 13(1), 42 (2019)

    Article  Google Scholar 

  24. Tarca, A.L., et al.: A novel signaling pathway impact analysis. Bioinformatics 25(1), 75–82 (2009)

    Article  Google Scholar 

  25. Whitlock, M.C.: Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach. J. Evol. Biol. 18(5), 1368–1373 (2005)

    Article  Google Scholar 

  26. Becht, E., et al.: Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37(1), 38 (2019)

    Article  Google Scholar 

  27. Navab, R., et al.: Prognostic gene-expression signature of carcinoma-associated fibroblasts in non-small cell lung cancer. PNAS 108(17), 7160–7165 (2011)

    Article  Google Scholar 

  28. Tang, Q., et al.: Hub genes and key pathways of non-small lung cancer identified using bioinformatics. Oncol. Lett. 16(2), 2344–2354 (2018)

    Google Scholar 

  29. Shi, W.Y., et al.: Gene expression analysis of lung cancer. Eur. Rev. Med. Pharmacol. Sci. 18(2), 217–228 (2014)

    Google Scholar 

  30. Bottomly, D., et al.: Evaluating gene expression in C57BL/6 J and DBA/2 J mouse striatum using RNA-Seq and microarrays. PloS one 6(3), e17820 (2011)

    Google Scholar 

  31. Zhao, S., et al.: Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PloS one 9(1), e78644 (2014)

    Google Scholar 

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Acknowledgements

This work was co-financed by SUT grant for maintaining and developing research potential (JZ), National Science Centre Poland, grant BITIMS 2015/19/B/ST6/01736 (JP) and European Union grant under the European Social Fund, project no. POWR.03.02.00-00-I029 (KL). All calculations were carried out using GeCONiI infrastructure funded by NCBiR project no. POIG.02.03.01-24-099/13.

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Authors and Affiliations

  1. Department of Data Science and Engineering, The Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Joanna Zyla, Kinga Leszczorz & Joanna Polanska

Authors
  1. Joanna Zyla
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  2. Kinga Leszczorz
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  3. Joanna Polanska
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Corresponding author

Correspondence to Joanna Zyla .

Editor information

Editors and Affiliations

  1. Enhanced Regenerative Medicine, Istituto Italiano di Tecnologia, Genoa, Genova, Italy

    Gabriella Panuccio

  2. Department de Informática, Universidade do Minho, Braga, Portugal

    Miguel Rocha

  3. Computer Science Department, University of Vigo, Vigo, Spain

    Florentino Fdez-Riverola

  4. Institute for Artificial Intelligence and Big Data (AIBIG), Universiti Malaysia Kelantan, Kampus Kota, Kota Bharu, Malaysia

    Mohd Saberi Mohamad

  5. Biotechnology, Intelligent Systems and Educational Technology (BISITE) Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    Roberto Casado-Vara

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Zyla, J., Leszczorz, K., Polanska, J. (2021). Robustness of Pathway Enrichment Analysis to Transcriptome-Wide Gene Expression Platform. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_18

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