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2019•BioinformaticsOpen access

gep2pep: a bioconductor package for the creation and analysis of pathway-based expression profiles

Francesco Napolitano, Diego Carrella, Xin D. Gao, Diego di Bernardo

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Abstract

SUMMARY: Pathway-based expression profiles allow for high-level interpretation of transcriptomic data and systematic comparison of dysregulated cellular programs. We have previously demonstrated the efficacy of pathway-based approaches with two different applications: the Drug Set Enrichment Analysis and the Gene2drug analysis. Here we present a software tool that allows to easily convert gene-based profiles to pathway-based profiles and analyze them within the popular R framework. We also provide pre-computed profiles derived from the original Connectivity Map and its next generation release, i.e. the LINCS database. AVAILABILITY AND IMPLEMENTATION: the tool is implemented as the R/Bioconductor package gep2pep and can be freely downloaded from https://bioconductor.org/packages/gep2pep. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://dsea.tigem.it/lincs.

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SUMMARY: Pathway-based expression profiles allow for high-level interpretation of transcriptomic data and systematic comparison of dysregulated cellular programs. We have previously demonstrated the efficacy of pathway-based approaches with two different applications: the Drug Set Enrichment Analysis and the Gene2drug analysis. Here we present a software tool that allows to easily convert gene-based profiles to pathway-based profiles and analyze them within the popular R framework. We also provide pre-computed profiles derived from the original Connectivity Map and its next generation release, i.e. the LINCS database. AVAILABILITY AND IMPLEMENTATION: the tool is implemented as the R/Bioconductor package gep2pep and can be freely downloaded from https://bioconductor.org/packages/gep2pep. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://dsea.tigem.it/lincs.

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Available abstract

SUMMARY: Pathway-based expression profiles allow for high-level interpretation of transcriptomic data and systematic comparison of dysregulated cellular programs. We have previously demonstrated the efficacy of pathway-based approaches with two different applications: the Drug Set Enrichment Analysis and the Gene2drug analysis. Here we present a software tool that allows to easily convert gene-based profiles to pathway-based profiles and analyze them within the popular R framework. We also provide pre-computed profiles derived from the original Connectivity Map and its next generation release, i.e. the LINCS database. AVAILABILITY AND IMPLEMENTATION: the tool is implemented as the R/Bioconductor package gep2pep and can be freely downloaded from https://bioconductor.org/packages/gep2pep. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://dsea.tigem.it/lincs.

Key concepts: Bioconductor, R package, Computer science, Software, Set (abstract data type), Software package, Data mining, Data set

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