LandScape
LandScape visualizes integrated genomic data as interactive landscape plots to compare gene- and pathway-level mutations and clinical attributes (e.g., age, gender, histology) across samples.
Key Features:
- Interactive Visualization: Dynamic, real-time visualization of summarized genetic information for exploratory analysis.
- Customizable Appearance: Built-in functions allow customization of visual elements for tailored presentation of genomic data.
- High-Quality Diagram Export: Supports export of publication-quality figures for inclusion in research outputs.
- Structured File Format and CSV Support: Accepts data in a structured file format and CSV files to represent diverse data types.
- Multi-panel Integration: Incorporates additional panels such as age, gender, and histology alongside genomic data.
- Comparative Mutation and Pathway Display: Enables comparison of genes and biological pathways mutated in cancers across datasets.
- Integration of Diverse Data Types: Combines multiple layers of genomic and sample-level data into a cohesive visualization.
- Real-time Exploration and Customization: Interactive controls permit on-the-fly exploration and adjustment of displayed data.
Scientific Applications:
- Comparative Cancer Genomics: Visual comparison of gene and pathway mutation patterns across cancer cohorts.
- Multi-layer Genomic Data Visualization: Illustration of integrated data from multiple layers or batch samples for cohort-level analyses.
- Personalized Medicine and Oncology Studies: Integration of clinical attributes (age, gender, histology) with genomic alterations to support translational investigations.
Methodology:
Integration of diverse data types via a structured file format (CSV supported) into an interactive visualization framework that enables real-time exploration and customization.
Topics
Details
- Tool Type:
- web application
- Added:
- 1/14/2020
- Last Updated:
- 12/22/2020
Operations
Publications
Jia W, Li H, Li S, Li S. LandScape: a web application for interactive genomic summary visualization. Unknown Journal. 2019. doi:10.1101/866087.
DOI: 10.1101/866087