Christina M. Kendziorski

SMART, a multi-agent framework, transfers findings from single-cell RNA-sequencing cancer studies to other cancer types, identifying cross-cancer prognostic signatures and generating new hypotheses.

The paper uses spatial multi-omics and single-cell transcriptomics to identify senescence-associated programs in colon adenoma to cancer progression, finding GDF15 mediates senescence-stemness coupling and immune exclusion.

AutoZyme optimizes bioinformatics software by autonomously identifying and applying code changes to improve runtime without significant memory use.

CASSIA automates and improves cell type annotation in single-cell RNA-sequencing data with interpretability and accuracy.

SpatialView is an interactive web tool for visualizing multiple spatial transcriptomics experiments, enhancing data exploration and analysis.

SpotClean addresses spot swapping in spatial transcriptomics data to improve downstream analysis sensitivity and precision.

SpatialCorr identifies gene sets with spatially varying correlation structure in spatial transcriptomics data, revealing insights not found with existing methods.

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 tests two models of beta cell dedifferentiation in diabetes using lineage tracing and RNA-seq in mice, showing dedifferentiation is not a reversal of development but varies by diabetes type.

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 an empirical Bayesian mixture model to detect distributional expression changes in single-cell RNA-seq data, showing improved sensitivity over standard approaches.

This study shows that co-differentiating human and mouse pluripotent stem cells accelerates human neural differentiation, with gene expression changes correlating with in utero development.

CHARTS provides a web tool to analyze and compare tumor subpopulations across public scRNA-seq cancer datasets.

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.

EBSeq improves computational efficiency for multi-group differential expression analysis by updating algorithms and pruning techniques.

CB2 improves cell identification in 10x Genomics data by leveraging cell-to-cell correlations to distinguish real cells from background barcodes.

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.