pdacR

pdacR provides curated pancreatic ductal adenocarcinoma (PDAC) transcriptomic datasets and analytical functions for gene expression analysis to support subtype exploration, differential expression, and survival association studies.

Key Features:

• Data Curation and Quality Control: Provides annotated datasets from landmark studies and applies curation and quality-control procedures to address low-purity samples and non-adenocarcinoma cases.
• Curated Annotated Datasets: Supplies high-quality, annotated transcriptomic datasets specifically assembled for PDAC analyses.
• Clustering: Performs clustering to identify patterns and groupings within PDAC gene expression data.
• Differential Expression Analysis: Detects genes differentially expressed between conditions or PDAC subtypes.
• Survival Analysis: Associates gene expression patterns with patient survival outcomes.
• Dimensionality Reduction: Reduces dataset dimensionality to reveal underlying structures in transcriptomic data.
• Multi-dataset Analysis and Cross-study Comparison: Enables analysis across multiple datasets to compare transcriptomic signals between studies.
• Support for Subtype Models: Facilitates exploration of PDAC subtype models, including support for the basal-like/classical framework.

Scientific Applications:

• Gene Expression Analysis: Enables in-depth analysis of PDAC gene expression to investigate molecular mechanisms of tumor behavior and progression.
• Subtype Classification: Supports refinement and evaluation of PDAC subtype classifications, including basal-like versus classical distinctions, to inform biological interpretation.
• Transcriptomic Landscape Exploration: Allows comprehensive, multi-dataset investigation of the PDAC transcriptomic landscape and cross-study comparisons.

Methodology:

Uses curated transcriptomic datasets from landmark studies, applies data curation and quality control to remove low-purity and non-adenocarcinoma samples, and performs clustering, differential expression analysis, survival analysis, and dimensionality reduction.