Brooke L. Fridley

BiNVaST improves cell typing across spatial transcriptomic platforms by generating synthetic data that preserves niche boundaries and integrates molecular and spatial information.

spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, identifying spatial domains and differential metabolites in mouse cancer data.

The paper introduces multiScaleAC, a method to analyze cell-cell interactions across spatial scales using a modified Moran's I with varying kernel bandwidths, showing improved statistical power and reduced bias.

The paper develops a functional data analysis framework to model spatial clustering in ovarian cancer, identifying prognostic T cell patterns beyond immune cell abundance.

iModMix identifies novel multi-omics associations by constructing data-driven modules without relying on biological annotations.

The paper identifies a transcriptional transition state in ccRCC cells that drives sarcomatoid transformation and enhances immunotherapy response via macrophage and T-cell interactions.

The paper provides exact analytical solutions for the mean and variance of the Nearest Neighbor G(r) spatial metric under complete spatial randomness, avoiding computationally expensive permutations.

spatialGE simplifies spatial transcriptomics analysis by providing a user-friendly web interface for non-programmers, enabling tasks like quality control, domain detection, and spatial analysis.

This study applies spatial mixed models to differential gene expression analysis in spatial transcriptomics, showing they reduce type-I error and improve model fit compared to non-spatial methods.

This study analyzes somatic mutational features in colorectal tumors from ancestrally diverse populations, finding ethnic and genetic ancestry associations with mutation patterns.

scSpatialSIM simulates spatial single-cell molecular data to evaluate spatial clustering methods, finding Ripleys K(r) best detects clustering across scenarios.

curatedPCaData harmonizes prostate cancer datasets to enable meta-analysis and clinical insights across multiple cohorts.

The paper introduces spatialGE, a software tool for quantifying and visualizing tumor microenvironment heterogeneity using spatial transcriptomics data.

The paper introduces a method to identify differential super enhancers by considering internal dynamics, revealing novel structural alterations with distinct regulatory impacts and improving identification compared to existing methods.

This paper applies wide deep learning to cell type classification, showing improved accuracy when models are trained on one cancer type and tested on another.

This study uses a crowdsourcing approach to stratify ALS patients, identifying four distinct clusters with unique predictors and improving outcome prediction.