Melissa J. Davis

hoodscanR identifies and analyzes cell neighborhoods in spatial transcriptomics data, revealing transcriptional differences in tumor cells across neighborhoods.

The paper introduces smartid, a method for accurately detecting markers in rare cell populations using a modified TF-IDF approach with Gaussian Mixture Model, improving cell type annotation in single-cell RNA-seq data.

The paper identifies transcriptional modules and cytokine signaling pathways that drive functional heterogeneity in T follicular helper cells across different infections.

mastR identifies markers from bulk RNA-seq data using a permutation test and signal-to-noise ratio approach, outperforming existing methods in benchmark tests.

This paper shows that temporarily blocking type I interferon during viral infection boosts CD8+ T cell memory and vaccine effectiveness by promoting TSCM cell formation.

SpaNorm improves spatial transcriptomics data normalisation by separating spatial library size effects from biological signals, enhancing domain and spatially variable gene identification.

This study shows that library size in spatial transcriptomics data confounds biological signals and recommends against normalizing it unless necessary.

vissE identifies and visualizes higher-order molecular phenotypes from functional enrichment analysis by summarizing redundancies into biological themes.

standR is a Bioconductor package that improves the analysis of Nanostring GeoMx DSP spatial transcriptomic data by addressing technical variability and enhancing statistical power.

scDECAF identifies cell types, states, and programs in single-cell data by learning gene set representations, showing effectiveness in disease and immune response contexts.

The paper uses spatial transcriptomics to analyze how CDK4/6 inhibition affects tumor response in SHH medulloblastoma by mapping cellular states and spatial organization.

This paper predicts protein-protein interactions using sequence encodings, achieving performance comparable to existing methods without complex physico-chemical features.

The paper introduces an embedding-based method for peptide identity propagation that improves missing value reduction and false discovery rate estimation in mass spectrometry data.

The paper introduces stingscore, a method that uses stably-expressed genes to score transcriptomic signatures from individual samples, improving normalization and enabling clinical applications.

The paper introduces msImpute, a low-rank approximation method for imputing missing values in mass spectrometry data, showing it performs well when data are missing at random.

The paper introduces a single-sample scoring method for gene set analysis that is stable across varying sample compositions and demonstrates improved computational efficiency compared to existing methods.

This study shows Activin-A inhibits NK cell function, promotes transdifferentiation into ILC1-like cells, and suppresses anti-tumor immunity, with therapeutic blockade improving outcomes.

The paper shows that multiple microRNAs work together to control EMT by reducing noise and enhancing gene expression changes, more accurately reflecting endogenous regulation.

The paper infers edge functions in protein-protein interaction networks using machine learning with Gene Ontology, InterPro, and Pfam annotations, achieving high classification accuracy.