surviveR: a flexible shiny application for patient survival analysis
Abstract Kaplan–Meier (KM) survival analyses based on complex patient categorization due to the burgeoning volumes of genomic, molecular and phenotypic data, are an increasingly important aspect of the biomedical researcher’s toolkit. Commercial statistics and graphing packages for such analyses are...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-48894-9 |
| _version_ | 1827581674756505600 |
|---|---|
| author | Tamas Sessler Gerard P. Quinn Mark Wappett Emily Rogan David Sharkey Baharak Ahmaderaghi Mark Lawler Daniel B. Longley Simon S. McDade |
| author_facet | Tamas Sessler Gerard P. Quinn Mark Wappett Emily Rogan David Sharkey Baharak Ahmaderaghi Mark Lawler Daniel B. Longley Simon S. McDade |
| author_sort | Tamas Sessler |
| collection | DOAJ |
| description | Abstract Kaplan–Meier (KM) survival analyses based on complex patient categorization due to the burgeoning volumes of genomic, molecular and phenotypic data, are an increasingly important aspect of the biomedical researcher’s toolkit. Commercial statistics and graphing packages for such analyses are functionally limited, whereas open-source tools have a high barrier-to-entry in terms of understanding of methodologies and computational expertise. We developed surviveR to address this unmet need for a survival analysis tool that can enable users with limited computational expertise to conduct routine but complex analyses. surviveR is a cloud-based Shiny application, that addresses our identified unmet need for an easy-to-use web-based tool that can plot and analyse survival based datasets. Integrated customization options allows a user with limited computational expertise to easily filter patients to enable custom cohort generation, automatically calculate log-rank test and Cox hazard ratios. Continuous datasets can be integrated, such as RNA or protein expression measurements which can be then used as categories for survival plotting. We further demonstrate the utility through exemplifying its application to a clinically relevant colorectal cancer patient dataset. surviveR is a cloud-based web application available at https://generatr.qub.ac.uk/app/surviveR , that can be used by non-experts users to perform complex custom survival analysis. |
| first_indexed | 2024-03-08T22:39:47Z |
| format | Article |
| id | doaj.art-cfca7324fc9a45e0aa3aa715d09fa8ec |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T22:39:47Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-cfca7324fc9a45e0aa3aa715d09fa8ec2023-12-17T12:15:49ZengNature PortfolioScientific Reports2045-23222023-12-011311610.1038/s41598-023-48894-9surviveR: a flexible shiny application for patient survival analysisTamas Sessler0Gerard P. Quinn1Mark Wappett2Emily Rogan3David Sharkey4Baharak Ahmaderaghi5Mark Lawler6Daniel B. Longley7Simon S. McDade8Patrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastPatrick G. Johnston Centre for Cancer Research, Queen’s University BelfastAbstract Kaplan–Meier (KM) survival analyses based on complex patient categorization due to the burgeoning volumes of genomic, molecular and phenotypic data, are an increasingly important aspect of the biomedical researcher’s toolkit. Commercial statistics and graphing packages for such analyses are functionally limited, whereas open-source tools have a high barrier-to-entry in terms of understanding of methodologies and computational expertise. We developed surviveR to address this unmet need for a survival analysis tool that can enable users with limited computational expertise to conduct routine but complex analyses. surviveR is a cloud-based Shiny application, that addresses our identified unmet need for an easy-to-use web-based tool that can plot and analyse survival based datasets. Integrated customization options allows a user with limited computational expertise to easily filter patients to enable custom cohort generation, automatically calculate log-rank test and Cox hazard ratios. Continuous datasets can be integrated, such as RNA or protein expression measurements which can be then used as categories for survival plotting. We further demonstrate the utility through exemplifying its application to a clinically relevant colorectal cancer patient dataset. surviveR is a cloud-based web application available at https://generatr.qub.ac.uk/app/surviveR , that can be used by non-experts users to perform complex custom survival analysis.https://doi.org/10.1038/s41598-023-48894-9 |
| spellingShingle | Tamas Sessler Gerard P. Quinn Mark Wappett Emily Rogan David Sharkey Baharak Ahmaderaghi Mark Lawler Daniel B. Longley Simon S. McDade surviveR: a flexible shiny application for patient survival analysis Scientific Reports |
| title | surviveR: a flexible shiny application for patient survival analysis |
| title_full | surviveR: a flexible shiny application for patient survival analysis |
| title_fullStr | surviveR: a flexible shiny application for patient survival analysis |
| title_full_unstemmed | surviveR: a flexible shiny application for patient survival analysis |
| title_short | surviveR: a flexible shiny application for patient survival analysis |
| title_sort | surviver a flexible shiny application for patient survival analysis |
| url | https://doi.org/10.1038/s41598-023-48894-9 |
| work_keys_str_mv | AT tamassessler surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT gerardpquinn surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT markwappett surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT emilyrogan surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT davidsharkey surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT baharakahmaderaghi surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT marklawler surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT danielblongley surviveraflexibleshinyapplicationforpatientsurvivalanalysis AT simonsmcdade surviveraflexibleshinyapplicationforpatientsurvivalanalysis |