45 citations · 60 across the 11 of their papers we have counts for
11 papers
MoSAIC: Multi-Resolution Spatial Regression Analysis of Cellular Colocalizations in Cancer Imaging
Jessica Aldous, Michele Peruzzi, Maria Masotti +4
Hierarchical multiplex imaging approaches generate spatially resolved single-cell measurements across multiple, spatially organized fields of view (FOVs) within patient tumor speci…
Partial correlation networks of Gaussian processes
Michele Peruzzi
In Gaussian graphical models, conditional independence and partial correlations are natural inferential targets for understanding direct relationships in multivariate data. No comp…
Joint Modeling of Spatial Dependencies Across Multiple Subjects in Multiplexed Tissue Imaging
Joel Eliason, Arvind Rao, Timothy L Frankel +1
The tumor microenvironment (TME) is a spatially heterogeneous ecosystem where cellular interactions shape tumor progression and response to therapy. Multiplexed imaging technologie…
Inside-out cross-covariance for spatial multivariate data
Michele Peruzzi
As the spatial features of multivariate data are increasingly central in researchers' applied problems, there is a growing demand for novel spatially-aware methods that are flexibl…
Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes
Yichen Zhu, Michele Peruzzi, Cheng Li +1
In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable computational complexity…
Spatial meshing for general Bayesian multivariate models
Michele Peruzzi, David B. Dunson
Quantifying spatial and/or temporal associations in multivariate geolocated data of different types is achievable via spatial random effects in a Bayesian hierarchical model, but s…