Michael A. Newton

This study identifies TXNIP as a downstream effector of EPAC signaling in primary melanoma, showing its role in redox regulation and melanoma progression through mTORC1 and HIF-1 pathways.

SOAAR improves cell-cell interaction inference in spatial transcriptomics by accounting for spatial autocorrelation, reducing false positives and identifying consistent immune signatures in cancer patients.

This study shows that invasive cancer can develop and regress within weeks after papillomavirus infection in mice, highlighting the rapid progression and immune dependence of tumor regression.

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.

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.

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

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

scDD identifies differential expression patterns in single-cell RNA-seq by modeling complex distribution differences, outperforming existing methods in detecting subtle gene expression changes.