Isabella N. Grabski

Reconstructing developmental and disease progression with sample-level embeddings

The paper introduces scSLIDE, a framework that generates sample-level embeddings from single-cell data to analyze variation across samples in disease and development.

Mapping transcriptional responses to cellular perturbation dictionaries with RNA fingerprinting

This paper introduces RNA fingerprinting, a method to map transcriptional responses to perturbations using reference dictionaries, showing accurate single-cell assignments and broad biological applications.

Iterative, multimodal, and scalable single-cell profiling for discovery and characterization of signaling regulators

The paper presents an iterative framework to identify mTOR signaling regulators using multimodal single-cell data and validates novel regulators through experimental testing.

Bayesian Multi-Study Non-Negative Matrix Factorization for Mutational Signatures

This paper introduces a Bayesian multi-study NMF method to identify common and condition-specific mutational signatures across multiple datasets.

Significance Analysis for Clustering with Single-Cell RNA-Sequencing Data

The paper introduces a statistical method to assess the significance of clusters in single-cell RNA-seq data, improving accuracy by addressing variability and reducing false discoveries.

A probabilistic gene expression barcode for annotation of cell-types from single cell RNA-seq data

This paper introduces a probabilistic method for cell-type annotation in scRNA-seq data that outperforms existing approaches by combining gene expression across thousands of genes and accounting for batch effects.

Differentially methylated regions and methylation QTLs for teen depression and early puberty in the Fragile Families Child Wellbeing Study

The paper identifies differentially methylated regions and methylation QTLs associated with teen depression and early puberty using longitudinal DNA methylation data from the Fragile Families Child Wellbeing Study.