Abstract
The main purpose of pathway or gene set analysis methods is to provide mechanistic insight into the large amount of data produced in high-throughput studies. These tools were developed for gene expression analyses, but they have been rapidly adopted by other high-throughput techniques, becoming one of the foremost tools of omics research.
Currently, according to different biological questions and data, we can choose among a vast plethora of methods and databases. Here we use two published examples of RNAseq datasets to approach multiple analyses of gene sets, networks and pathways using freely available and frequently updated software. Finally, we conclude this chapter by presenting a survival pathway analysis of a multiomics dataset. During this overview of different methods, we focus on visualization, which is a fundamental but challenging step in this computational field.
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Acknowledgments
This work was supported by the “My First AIRC grant” to Enrica Calura (MFAG 2019, Grant N. 23522), the IG grant to Chiara Romualdi (Grant N. IG 21837) both provided by Italian Association for Cancer Research (AIRC) and the European Molecular Biology Organization (EMBO) Short-Term Fellowship [8517] to Paolo Martini.
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Calura, E., Martini, P. (2021). Summarizing RNA-Seq Data or Differentially Expressed Genes Using Gene Set, Network, or Pathway Analysis. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 2284. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1307-8_9
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DOI: https://doi.org/10.1007/978-1-0716-1307-8_9
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