Phillip B. Nicol

Spurious correlation inflates performance in single-cell perturbation prediction

The paper shows that performance metrics in single-cell perturbation prediction are inflated by reusing control cells, leading to overestimation of method effectiveness.

An image-based transcriptomics atlas reveals the regional and microbiota-dependent molecular, cellular, and spatial structure of the murine gut

This paper creates a gut transcriptomics atlas using MERFISH to show how microbiota influences cell types, spatial organization, and molecular responses in the murine gut.

Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models

The paper introduces scGBM, a method for dimensionality reduction in scRNA-seq data using a Poisson bilinear model, which better captures biological information and quantifies uncertainty.

Identifying spatially variable genes by projecting to morphologically relevant curves

The paper introduces a method to identify spatially variable genes by projecting spatial transcriptomics data onto morphologically relevant curves, improving detection accuracy and biological interpretation.

Robust identification of perturbed cell types in single-cell RNA-seq data

The paper introduces scDist, a tool for detecting perturbed cell types in single-cell RNA-seq data, showing it outperforms existing methods in identifying transcriptomic changes in disease contexts.

Oncogenetic Network Estimation with Disjunctive Bayesian Networks

This paper introduces a Bayesian network model for oncogenetic network estimation, showing its ability to infer progression networks from synthetic and real data.

SITH: an R package for visualizing and analyzing a spatial model of intratumor heterogeneity

SITH is an R package that models tumor growth and mutation spatially, generates synthetic sequencing data, and provides 3D visualizations of intratumor heterogeneity.