Drug Response Omics association MAp (DROMA, 卓玛)
DROMA is an integrated cancer drug response and multi-omics ecosystem for cross-project data organization, harmonized analysis, and reusable translational workflows.
At the ecosystem level:
- DROMA_DB provides the unified SQLite data resource.
- DROMA_Set provides the R data structures and database access layer.
- DROMA_R provides the main statistical analysis and visualization functions.
- DROMA_Meta provides workflow-level orchestration for standardized biomarker discovery pipelines.
- DROMA_MCP, DROMA_Py, and DROMA_Web provide AI, Python, and browser-facing access modes.
Official website: https://droma01.github.io/DROMA/
DROMA_DB is the unified SQLite-backed data layer of the DROMA ecosystem.
- 21 datasets across cell line, PDC, PDO, PDX, and clinical-oriented resources
- 10,625 model/sample/patient entries
- 58,316 preclinical drug-testing records
- Multi-omics coverage including mRNA, mutation, CNV, DNA methylation, gene fusion, and protein
This structure allows project-level resources to be stored in one harmonized database while remaining accessible through a shared interface.
DROMA_R is the main analysis engine for drug response and omics association studies in DROMA.
Key capabilities include:
- AAC-based drug response handling with project-level z-score normalization
- Cross-project comparison for harmonized multi-study analysis
- Phenotype-aware analysis for continuous and categorical annotations
- Drug-omics association analysis across transcriptomic and other omics layers
- Batch biomarker screening for large-scale feature discovery
- Meta-analysis across datasets with effect-size integration
- Visualization outputs including forest plots, volcano plots, scatter plots, and box plots
- Functional analysis support such as pathway-level interpretation and GSVA-style workflows
Unified SQLite database resource for DROMA datasets and annotations.
R package that builds and manages DromaSet and MultiDromaSet objects for project-level and cross-project analyses.
Python access layer for querying and handling DROMA resources in Python workflows.
R package for statistical analysis, biomarker screening, harmonized comparison, and visualization.
Workflow application package that wraps standardized multi-step biomarker discovery pipelines on top of DROMA.Set and DROMA.R.
Model Context Protocol server that exposes DROMA functionality to AI assistants through natural language interfaces.
Browser-based Shiny application for interactive exploration and analysis.
devtools::install_github("mugpeng/DROMA_Set")
devtools::install_github("mugpeng/DROMA_R")library(DROMA.Set)
gCSI <- createDromaSetFromDatabase("gCSI", "droma.sqlite")gCSI <- loadMolecularProfilesNormalized(
gCSI,
molecular_type = "mRNA",
features = "ABCB1"
)
gCSI <- loadTreatmentResponseNormalized(
gCSI,
drugs = "Paclitaxel"
)
library(DROMA.R)
result <- analyzeDrugOmicPair(gCSI, "mRNA", "ABCB1", "Paclitaxel")devtools::install_github("mugpeng/DROMA_Meta")DROMA_Meta is intended for standardized workflow execution when you want a reusable application-layer pipeline rather than lower-level exploratory analysis.
pip install droma-mcp
pip install droma-py- Zenodo record: https://zenodo.org/records/15742800
- Website: https://droma01.github.io/DROMA/
If you use DROMA in your research, please cite:
@article{li2024facilitating,
title={Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny},
author={Li, Shixiang and Peng, Yu and Chen, Miaozun and others},
journal={Communications Biology},
volume={7},
number={1},
pages={1200},
year={2024},
publisher={Nature Publishing Group},
doi={10.1038/s42003-024-06891-2}
}DROMA is licensed under the Mozilla Public License 2.0 (MPL-2.0). See LICENSE for details.