Kyle C. McGovern

Scale-Aware Compositional Inference Improves Reproducibility and Uncovers Convergent Aging Programs in Spatial Transcriptomics

This paper introduces a scale-aware framework for spatial differential expression that improves reproducibility and identifies convergent aging programs in aged mouse brains.

Scale Reliant Mixed Effects Models Enhance Microbiome Data Analysis

This paper introduces SR-MEM, a mixed-effects model for microbiome data that accounts for compositional data issues, achieving better false discovery rate control and power than existing methods.

Brain-wide connectivity and novelty response of the dorsal endopiriform nucleus in mice

This study maps the connectivity and function of the dorsal endopiriform nucleus in mice, revealing its role in olfaction and response to novel stimuli through Oxtr-expressing neurons.

Replacing Normalizations with Interval AssumptionsImproves the Rigor and Robustness of DifferentialExpression and Differential Abundance Analyses

The paper introduces interval assumptions to replace normalization in differential expression and abundance analyses, reducing false positives and negatives through more realistic and flexible assumptions.

Addressing Erroneous Scale Assumptions in Microbe and Gene Set Enrichment Analysis

The paper introduces a sensitivity analysis framework to address false positives in microbe and gene set enrichment analysis caused by erroneous scale assumptions.