Rhonda L. Bacher

scDEcrypter improves differential expression analysis in viral infection studies by accounting for uncertainty in cell-state and infection labels, identifying more biologically relevant genes and pathways.

genesetGPT is an LLM-based framework that improves gene set annotation by using curated biological context and iterative prompt design to summarize heterogeneous gene sets.

The paper profiles chromatin accessibility in post-sepsis MDSCs, showing divergent epigenetic trajectories between rapid recovery and chronic critical illness, with MHC class II repression in CCI.

CN-RNN is a deep learning framework for detecting copy number variations from exome sequencing data, showing improved performance over existing methods.

The paper develops simCRISPR, a simulation framework for pooled CRISPR screens, evaluates normalization strategies for detecting gene-by-environment interactions, and shows improved results with safe-harbor normalization.

This study uses spatial transcriptomics to identify inflammation signatures in pancreas and lymph node tissues of T1D patients, highlighting LTB and other genes involved in disease pathogenesis.

This paper develops a 3D CRISPR screening system using HepG2/C3A spheroids to improve the identification of genetic determinants of chemical toxicity compared to traditional 2D methods.

This study identifies 340 genes modulating PFOS-induced cytotoxicity in liver cells using CRISPR screens and validates two genes, SLC6A9 and CPSF2, with functional and molecular evidence.

The paper identifies C18orf32 as a novel regulator of lipid metabolism involved in PFOA-induced toxicity through CRISPR screening and transcriptomic analysis.

The paper identifies common loss-of-function genes and develops a method to integrate them into cell-based models to assess population variability in chemical susceptibility.

The paper introduces scLANE, an interpretable method for trajectory differential expression in single-cell RNA-seq, validated on simulations and applied to biological datasets.

This study identifies a biphasic epigenetic and transcriptional response in macrophages after burn injury, revealing locus-specific chromatin remodeling and immune dysfunction.

This study maps epigenetic changes in MDSCs after sepsis, revealing age- and sex-specific chromatin remodeling linked to immune dysfunction.

This study identifies aberrant immune regulation and stem-like CD8+ T cells in pancreatic lymph nodes during type 1 diabetes development using mass cytometry and single-cell RNA sequencing.

This study shows that blocking CD226 reduces diabetes in NOD mice by enhancing Treg function and reducing effector T cell activity.

The paper introduces Escort, a framework to evaluate dataset suitability for trajectory inference and quantify trajectory properties, reducing analysis uncertainty through data-driven assessments.

This study uses multi-omics profiling to show that low-dose ionizing radiation in Arabidopsis triggers genetic and epigenetic responses mediated by the ethylene signaling pathway.

This study shows that equalizing cDNA library concentrations before pooling improves gene detection rates and reduces technical artifacts in single-cell RNA-seq data.

The paper introduces Dino, a normalization method for single-cell RNA-seq data that improves downstream analysis by normalizing the entire gene expression distribution.

The paper introduces methylscaper, an R/Shiny app for visualizing DNA methylation and nucleosome occupancy in single-molecule and single-cell data, showing its ability to identify biologically relevant chromatin features.

This study evaluates the reproducibility of spatial ordering analysis across different single-cell RNA-seq protocols, finding high reproducibility despite differences in sequencing depth and computational methods.

The paper discusses experimental design for large-scale omic studies, emphasizing the importance of balancing omics batches and accounting for experimental factors to improve statistical power.

Trendy uses segmented regression to analyze gene expression dynamics in ordered experiments, identifying gene-specific patterns and overall transcriptome changes.

The paper uses single-cell RNA-seq to study stress response heterogeneity in yeast, finding regulatory variation and decoupled transcription-factor activation.

SCnorm provides a quantile-regression based normalization method for scRNA-seq data to reduce artifacts and improve downstream analysis accuracy.