Barbara Elizabeth Engelhardt

The paper introduces smNSF, a method that integrates spatial and cell-type information to uncover interpretable spatial gene patterns in spatial transcriptomics data.

This paper develops a machine learning pipeline to discover novel antibacterial compounds with distinct mechanisms of action, finding that training data bias limits the discovery of mechanistically novel antibiotics.

This paper develops a vision transformer framework to classify CMT2A disease phenotypes in iPSC motor neurons using kymographs, showing improved accuracy over traditional metrics.

The paper examines DNA methylation changes linked to early life adversity in the FFCWS cohort, finding specific, functional genomic regions associated with mental health disorders.

The paper introduces NNMF, a spatially-aware method for analyzing spatial transcriptomics data, identifying multicellular patterns and spatially-variable genes in large datasets.

The paper introduces k-spaces, a probabilistic framework for simultaneous dimension reduction, clustering, and latent space learning, demonstrating its application in genomic settings.

SPARK generates simulated nascent RNA sequencing data to benchmark computational tools for analyzing transcriptional dynamics.

The paper identifies gene expression patterns and regulatory elements associated with HMO biosynthesis in lactocytes using scRNA-seq and HMO concentration data.

This paper introduces a segmentation-free method to analyze live-cell imaging data of T cell-cancer cell interactions, revealing how T cell modifications affect aggregate formation dynamics.

The paper introduces NNNSF, a scalable variant of NSF using VNNGPs, reducing computational complexity and improving reconstruction loss on spatial and temporal transcriptomics data.

This study uses explainable AI to analyze live-cell imaging data and identify behavioral changes in TCR T cells with specific genetic knockouts.

The paper develops tools for analyzing T cell behavior from live-cell imaging to improve T cell therapies by studying the effects of genetic modifications on T cell-cancer cell interactions.

This work optimizes experimental design for spatial genomics studies by proposing structured batch experimental design approaches for atlas construction and region localization.

This paper introduces a TBLDA framework to identify eQTLs across tissues using raw count data with a one-to-many sample-to-genotype mapping.

This paper introduces scTBLDA, a method for identifying eQTLs in single-cell RNA-seq data without relying on cell-type labels, and applies it to find cis- and trans-eQTLs in lupus patients.

The paper links histology and molecular states across human tissues by analyzing paired bulk gene expression and histology data from the GTEx Consortium, revealing shared sources of variation and specific correlations with donor characteristics.

The paper introduces a Poisson reduced-rank regression model for association mapping in scRNA-seq and other sequencing data, showing it captures gene expression-covariate associations effectively.

This paper introduces a probabilistic model to align spatial genomics and histology data across different scales and technologies, enabling accurate downstream analysis.

The paper introduces a new spatial covariance function for fMRI data called the brain kernel, which captures non-stationary spatial correlations and improves decoding and factor analysis.

The paper develops hierarchical Gaussian processes and mixture of experts models to analyze EHR data from COVID-19 patients, showing improved predictive performance for clinical markers like albumin.

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.

The paper develops ImageCCA to link histological image features with gene expression, identifying gene-morphology associations in TCGA and GTEx data.

scGene-Fit selects optimal gene markers for cell type discrimination in single-cell RNA-seq data by optimizing label recovery with compressive classification methods.

The paper introduces BETS, a statistical framework for inferring gene regulatory networks from transcriptional time series data, and validates it using glucocorticoid response data and external sources.

This study uses Mendelian randomization to examine if ACE inhibitors or cardiometabolic risk factors affect lung ACE2 and TMPRSS2 expression or plasma ACE2 levels, finding no overall association.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.