Integrated Drug Response Ecosystem

DROMA
Drug Response Omics association MAp

An integrated ecosystem for cancer drug response and multi-omics research, combining a unified SQLite resource, harmonized R analysis, reusable workflow applications, and AI- and browser-facing interfaces. Built around 21 datasets, 10,625 entries, and 58,316 preclinical drug-testing records.

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21 Datasets
10,625 Entries
58,316 Drug Tests

Key Highlights

Unified data resources, harmonized analytics, and reusable workflow applications

Unified SQLite Resource

DROMA_DB organizes 21 datasets into one harmonized database for cross-project access

Multi-Omics Support

Supports mRNA, mutation, CNV, DNA methylation, gene fusion, protein, and response layers

Cross-Model Coverage

Spans cell line, PDC, PDO, PDX, and clinical-oriented resources in one ecosystem

Workflow Layer

DROMA_Meta packages standardized multi-step biomarker discovery workflows

Harmonized Analytics

DROMA_R provides AAC handling, z-score normalization, biomarker analysis, and meta-analysis

Multiple Interfaces

Use DROMA through R, Python, MCP-compatible AI assistants, or the Shiny web interface

DROMA Ecosystem

Parallel components in one ecosystem, from database to workflow and interface layers

Data Layer

DROMA_DB

Database

Unified SQLite data resource across 21 datasets and multiple omics layers

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DROMA_Set

Data Management

R data structures and database access for single-project and cross-project analysis

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Analysis Layer

DROMA_R

Analytics

Harmonized statistical analysis, batch screening, and visualization

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DROMA_Meta

Workflow

Application-layer workflow package for standardized biomarker discovery pipelines

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User Interfaces

DROMA_Web

Web App

Interactive Shiny interface for browser-based exploration and analysis

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DROMA_MCP

AI Interface

Natural language access to DROMA datasets and analysis through MCP

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DROMA_Py

Python

Python access layer for querying and orchestrating DROMA resources

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Get Started in Minutes

Begin your drug-omics analysis journey with DROMA's comprehensive ecosystem

1

Install Components

Install DROMA.Set and DROMA.R, then add DROMA.Meta if you need workflow automation

2

Download Data

Download the harmonized DROMA database from Zenodo

3

Start Analyzing

Load a project, normalize omics and response data, then run association analysis

View Documentation
R 
# Install DROMA packages
devtools::install_github("mugpeng/DROMA_Set")
devtools::install_github("mugpeng/DROMA_R")
devtools::install_github("mugpeng/DROMA_Meta")

# Connect data
library(DROMA.Set)
gCSI <- createDromaSetFromDatabase("gCSI", "droma.sqlite")

# Start analysis
gCSI <- loadMolecularProfilesNormalized(gCSI, "mRNA", "ABCB1")
gCSI <- loadTreatmentResponseNormalized(gCSI, drugs = "Paclitaxel")
library(DROMA.R)
result <- analyzeDrugOmicPair(gCSI, "mRNA", "ABCB1", "Paclitaxel")