Unknown

Dataset Information

0

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.


ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

SUBMITTER: Torre-Healy LA 

PROVIDER: S-EPMC9918476 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

altmetric image

Publications

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.

Torre-Healy Luke A LA   Kawalerski Ryan R RR   Oh Ki K   Chrastecka Lucie L   Peng Xianlu L XL   Aguirre Andrew J AJ   Rashid Naim U NU   Yeh Jen Jen JJ   Moffitt Richard A RA  

Communications biology 20230210 1


Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls  ...[more]

Similar Datasets

| S-EPMC6853918 | biostudies-literature
| S-EPMC6296757 | biostudies-literature
| S-EPMC4849535 | biostudies-literature
2019-10-03 | GSE125380 | GEO
| S-EPMC11217054 | biostudies-literature
| S-EPMC7458899 | biostudies-literature
| PRJNA432344 | ENA
| S-EPMC8675915 | biostudies-literature
| S-EPMC6819395 | biostudies-literature
| S-EPMC7074188 | biostudies-literature