spicyR

spicyR infers changes in the spatial localization of cell types across groups of high-parameter histological images to quantify alterations in cellular environments during disease progression.

Key Features:

• Spatial inference: Uses robust statistical methods to detect changes in spatial localization of multiple cell types across image groups.
• Validation on simulated data: Demonstrated high sensitivity and specificity in detecting spatial localization changes using simulated datasets.
• Application to imaging mass cytometry: Applied to a type 1 diabetes imaging mass cytometry dataset to identify significant alterations in cellular associations.
• High-parameter histological data support: Supports analysis of high-parameter histological techniques and imaging modalities to explore tissue environments and identify distinct cell types.
• Handling complex cellular architectures: Designed to accommodate complex cellular arrangements typical of diseased tissues.

Scientific Applications:

• Disease progression analysis: Quantifies changes in cellular interactions and spatial arrangements relevant to disease mechanisms, as illustrated in type 1 diabetes data.
• Histological data exploration: Enables comprehensive analysis of high-parameter histological datasets to characterize tissue microenvironments and cell-type associations.

Methodology:

Implements advanced statistical techniques to analyze spatial localization changes across image datasets.