SEMA

SEMA performs graphical hypothesis testing of multidimensional cancer genomics datasets using Structural Equation Modeling to analyze complex molecular relationships, including TP53 effects on cell cycle regulation, DNA repair, and signal transduction.

Key Features:

• Graphical Hypothesis Testing: Enables construction and testing of graphical models representing cancer mechanisms for hypothesis-driven analysis.
• Structural Equation Modeling (SEM): Applies Structural Equation Modeling to analyze graphical models and supports both exploratory and confirmatory analyses of relationships among genomics variables.
• Implementation Technologies: Implemented with R Shiny and JavaScript and provides integrated visual and statistical functionalities for model specification and evaluation.
• Multidimensional Data Handling: Processes large-scale, multidimensional cancer genomics datasets for modeling complex molecular interactions.

Scientific Applications:

• Gene mechanism investigation: Constructing and testing models to elucidate roles of specific genes such as TP53 in cell cycle regulation, DNA repair, and signal transduction.
• Biomarker and target discovery: Supporting identification of potential therapeutic targets and biomarkers through model-based interrogation of genomic alterations.
• Cancer genomics analysis: Enabling model-driven exploration of functional interactions within large-scale, multidimensional cancer genomics datasets.

Methodology:

SEMA analyzes graphical models using Structural Equation Modeling to perform exploratory and confirmatory statistical analyses and is implemented with R Shiny and JavaScript.