IOAT

IOAT performs statistical analysis and visualization of multi-omics and clinical data to support biomarker identification and cancer subtype determination.

Key Features:

• Data Compatibility: Targets CSV-formatted clinical information and high-dimensional multi-omics datasets.
• Data Preprocessing: Provides preprocessing functionalities to prepare raw omics data for downstream analysis.
• Feature Selection: Implements feature selection to identify the most relevant variables in datasets.
• Risk Assessment and Clustering: Includes risk assessment and clustering algorithms for subgroup identification within cohorts.
• Survival Analysis: Supports survival analysis to evaluate prognostic factors and patient outcomes.
• Cancer Subtype Determination: Integrates clinical data with omics information to define cancer subtypes relevant to precision oncology.
• Biomarker Identification: Assists in identifying biomarkers related to tumor staging and gene associations.
• Methodological Flexibility: Provides multiple analytical methods and models selectable for different datasets and questions.

Scientific Applications:

• Cancer Genomics: Enables integrative analyses of multi-omics and clinical parameters in cancer research.
• TCGA Lung Cancer: Has been applied to lung cancer datasets from The Cancer Genome Atlas (TCGA).
• Precision Oncology: Supports discovery of cancer subtypes and biomarkers to inform precision oncology approaches.
• Tumor Staging and Gene Associations: Facilitates analysis of biomarker associations with tumor staging and disease progression.

Methodology:

Processes CSV inputs with data preprocessing, feature selection, clustering algorithms, risk assessment, survival analysis, and integration of clinical and multi-omics data.