Synplex

Synplex simulates multiplex immunofluorescence images to generate synthetic tissue datasets for studying spatial interactions in the tumor microenvironment.

Key Features:

• Simulator of multiplexed immunofluorescence images: Generates synthetic multiplex immunofluorescence images from user-defined parameters to model tissue-level marker expression and morphology.
• Cell phenotypes: Allows specification of cell phenotypes based on marker expression levels and morphological characteristics.
• Cellular neighborhoods: Models spatial associations between different cell phenotypes to represent cellular neighborhoods within tissues.
• Interactions between cellular neighborhoods: Simulates interactions among cellular neighborhoods to capture dynamic spatial interplay in the tumor microenvironment.
• Validation with synthetic tissues: Produces synthetic tissues that reflect real cancer cohorts and replicate underlying differences in tumor microenvironments.
• Data augmentation for machine learning: Provides synthetic datasets for augmenting training data and evaluating machine learning models.
• In silico biomarker selection: Enables selection and assessment of clinically relevant biomarkers associated with disease progression or therapeutic response.

Scientific Applications:

• Cancer microenvironment modeling: Modeling and analysis of tumor microenvironment spatial organization using multiplex immunofluorescence-like synthetic images.
• Spatial interaction analysis: Exploring complex spatial interactions and neighborhood-level effects among cell phenotypes.
• Machine learning development and evaluation: Augmenting training datasets and validating predictive models for image analysis and biomarker discovery.
• In silico biomarker assessment: Assessing candidate biomarkers for association with disease progression or treatment response.

Methodology:

Computational simulation of multiplex immunofluorescence by generating synthetic tissues from user-defined parameters for cell phenotypes, spatial associations, and neighborhood interactions.