GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved]
Exploratory analysis of cancer consortia data curated by the cBioPortal repository typically requires advanced programming skills and expertise to identify novel genomic prognostic markers that have the potential for both diagnostic and therapeutic exploitation. We developed GNOSIS (GeNomics explOre...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2022-09-01
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| Series: | HRB Open Research |
| Subjects: | |
| Online Access: | https://hrbopenresearch.org/articles/5-8/v2 |
| _version_ | 1811227016358264832 |
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| author | Andrew Flaus Lydia King Emma Holian Simone Coughlan Aaron Golden |
| author_facet | Andrew Flaus Lydia King Emma Holian Simone Coughlan Aaron Golden |
| author_sort | Andrew Flaus |
| collection | DOAJ |
| description | Exploratory analysis of cancer consortia data curated by the cBioPortal repository typically requires advanced programming skills and expertise to identify novel genomic prognostic markers that have the potential for both diagnostic and therapeutic exploitation. We developed GNOSIS (GeNomics explOrer using StatistIcal and Survival analysis in R), an R Shiny App incorporating a range of R packages enabling users to efficiently explore and visualise such clinical and genomic data. GNOSIS provides an intuitive graphical user interface and multiple tab panels supporting a range of functionalities, including data upload and initial exploration, data recoding and subsetting, data visualisations, statistical analysis, mutation analysis and, in particular, survival analysis to identify prognostic markers. GNOSIS also facilitates reproducible research by providing downloadable input logs and R scripts from each session, and so offers an excellent means of supporting clinician-researchers in developing their statistical computing skills. |
| first_indexed | 2024-04-12T09:35:22Z |
| format | Article |
| id | doaj.art-2bb81b6d705046e1b5c3b925a959e786 |
| institution | Directory Open Access Journal |
| issn | 2515-4826 |
| language | English |
| last_indexed | 2024-04-12T09:35:22Z |
| publishDate | 2022-09-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | HRB Open Research |
| spelling | doaj.art-2bb81b6d705046e1b5c3b925a959e7862022-12-22T03:38:15ZengF1000 Research LtdHRB Open Research2515-48262022-09-01514866GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved]Andrew Flaus0Lydia King1https://orcid.org/0000-0002-0696-9811Emma Holian2Simone Coughlan3Aaron Golden4https://orcid.org/0000-0001-8208-4292Centre for Chromosome Biology, School of Natural Sciences, National University of Ireland, Galway, H91 TK33, IrelandSFI Centre for Genomics Data Science, National University of Ireland, Galway, H91 TK33, IrelandSchool of Mathematical & Statistical Sciences, National University of Ireland, Galway, H91 TK33, IrelandSFI Centre for Genomics Data Science, National University of Ireland, Galway, H91 TK33, IrelandSchool of Mathematical & Statistical Sciences, National University of Ireland, Galway, H91 TK33, IrelandExploratory analysis of cancer consortia data curated by the cBioPortal repository typically requires advanced programming skills and expertise to identify novel genomic prognostic markers that have the potential for both diagnostic and therapeutic exploitation. We developed GNOSIS (GeNomics explOrer using StatistIcal and Survival analysis in R), an R Shiny App incorporating a range of R packages enabling users to efficiently explore and visualise such clinical and genomic data. GNOSIS provides an intuitive graphical user interface and multiple tab panels supporting a range of functionalities, including data upload and initial exploration, data recoding and subsetting, data visualisations, statistical analysis, mutation analysis and, in particular, survival analysis to identify prognostic markers. GNOSIS also facilitates reproducible research by providing downloadable input logs and R scripts from each session, and so offers an excellent means of supporting clinician-researchers in developing their statistical computing skills.https://hrbopenresearch.org/articles/5-8/v2Cancer Genomics cBioPortal Precision Oncology Statistical Analysis Survival Analysis Data Explorationeng |
| spellingShingle | Andrew Flaus Lydia King Emma Holian Simone Coughlan Aaron Golden GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved] HRB Open Research Cancer Genomics cBioPortal Precision Oncology Statistical Analysis Survival Analysis Data Exploration eng |
| title | GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved] |
| title_full | GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved] |
| title_fullStr | GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved] |
| title_full_unstemmed | GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved] |
| title_short | GNOSIS: an R Shiny app supporting cancer genomics survival analysis with cBioPortal [version 2; peer review: 2 approved] |
| title_sort | gnosis an r shiny app supporting cancer genomics survival analysis with cbioportal version 2 peer review 2 approved |
| topic | Cancer Genomics cBioPortal Precision Oncology Statistical Analysis Survival Analysis Data Exploration eng |
| url | https://hrbopenresearch.org/articles/5-8/v2 |
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