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IOAT: an interactive tool for statistical analysis of omics data and clinical data.
Lanlan Wu1, Fei Liu2, Hongmin Cai3
1Department of Software Engineering, South China University of Technology, Guangzhou, China.
BMC Bioinformatics
|June 16, 2021
Summary
A new desktop tool, IOAT, allows non-technical users to analyze private multi-omics and clinical data securely. It offers flexible data processing and model selection for cancer research and precision oncology.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- High-throughput sequencing generates vast multi-omics data.
- Existing tools lack suitability for private data, non-technical users, and integrated clinical information.
- Limited flexibility in data processing and model selection hinders comprehensive analysis.
Purpose of the Study:
- Develop an interactive desktop tool (IOAT) for secure analysis of private multi-omics and clinical data.
- Empower non-technical users to perform complex data analysis without coding.
- Facilitate flexible data processing and model selection for personalized cancer research.
Main Methods:
- IOAT is a local desktop application targeting CSV formatted data.
- Integrates multi-omics data with clinical information.
- Provides modules for data preprocessing, feature selection, risk assessment, clustering, and survival analysis.
Main Results:
- IOAT enables secure and convenient analysis of private multi-omics data.
- Users can flexibly combine methods for model selection, risk assessment, and cancer subtype identification.
- Identifies genes associated with tumor staging, supporting precision oncology development.
- Demonstrated capabilities on TCGA lung cancer data.
Conclusions:
- IOAT offers a comprehensive solution for multi-omics data integration and analysis.
- Enables rapid cancer genome data analysis for subtype discovery and biomarker identification.
- Enhances data security and accessibility for cancer biologists and biomedicine researchers.
- Available for free download, promoting easier and safer data analysis.
Keywords:
Cancer subtypesClinical dataFeature selectionMulti-omic clusteringMulti-omics data integrationRisk assessmentSafetySurvival analysisMore Related Videos
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