Julia Wrobel

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.

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.

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

This paper introduces a method to analyze functional brain networks by treating network diagnostics as functions of parameters, enabling regression and alignment with cognitive performance measures.

GammaGateR provides a semi-automated marker gating tool for multiplexed imaging data, improving consistency and reducing manual effort while revealing biological signals.

The paper identifies neural signatures of emotion regulation using fMRI data and machine learning models, showing they can predict emotion regulation with 82.5% accuracy.

The paper introduces FunSpace, a method using spatial entropy and functional principal component analysis to assess spatial heterogeneity in tumor microenvironments and its impact on survival.

The paper introduces a platform-independent preprocessing framework for multiplex tissue imaging data to improve cross-platform consistency and reliability.

This paper develops a video segmentation pipeline to assess changes in pupil response to light after cannabis consumption and finds that acute cannabis use reduces pupil constriction and delays rebound dilation.

The paper introduces a spatial and functional analytic approach to model the impact of tumor-stromal cell interactions on survival in cancer patients using imaging data.

DenVar analyzes multiplex imaging data to assess associations between functional marker densities and clinical outcomes without thresholding.

This paper quantifies slide-to-slide variation in multiplexed immunofluorescence data and introduces normalization methods to correct it, showing that dividing by slide mean and functional data registration perform best.

The paper quantifies mouse reach kinematics using analytical frameworks and machine learning, finding conserved primate-like features with notable differences and highlighting kinematic adjustments in motor control.

This paper develops a method to reduce technical variation in multisite MRI data by aligning intensity distributions across scanners.