Justin D. Silverman

Dietary fiber-type-specific gut microbiome preconditioning drives differential susceptibility to sporadic colon cancer development

The paper shows that inulin supplementation increases colon tumor incidence in mice by altering gut microbiome and metabolome, creating a tumor-promoting environment when exposed to carcinogens.

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.

Label-free 3D virtual histology of human formalin-fixed paraffin-embedded (FFPE) prostate needle biopsies with propagation-based phase-contrast micro-CT (PBCT)

This paper introduces a method using PBCT to create 3D virtual histology of FFPE prostate biopsies, enabling nondestructive 3D analysis of tumor architecture and distinguishing Gleason patterns.

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.

PCR Bias Impacts Microbiome Ecological Analyses

This study evaluates how PCR bias affects microbial ecology analyses and provides guidance on selecting appropriate diversity metrics and applying bias correction.

Long-range neural pathways for octopus chemotactile processing revealed from periphery-to-brain by centimeter-field microCT

The paper uses high-resolution microCT to create a 3D digital octopus, revealing neural pathways and organ structures from periphery to brain.

Not every gene is special: one simple rule to control the false discovery rate when analysing high-throughput sequencing data

This study shows that scale misspecification in differential expression analyses leads to poor false discovery rate control and provides guidance on choosing appropriate scale uncertainty.

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis

This paper shows Bayesian partially identified models outperform machine learning and normalization methods in microbiome data analysis by accounting for uncertainty in microbial load.

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.

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis

The paper introduces scale models to improve microbiome and gene expression analysis by reducing false positives and negatives compared to traditional normalizations.

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.

Explicit Scale Simulation for analysis of RNA-sequencing withALDEx2

This paper introduces scale models to improve transcriptomic analysis by reducing false positives/negatives and enhancing transparency, using the ALDEx2 package.

Quantitative Geometric Modeling of Blood Cells from X-ray Histotomograms of Whole Zebrafish Larvae

The paper develops a semi-automated method to segment and model blood cells in zebrafish larvae using X-ray histotomography, enabling quantitative geometric analysis across developmental stages and mutants.

Scalable estimation of microbial co-occurrence networks with Variational Autoencoders

This paper introduces a scalable method using Variational Autoencoders to estimate microbial co-occurrence networks from high-dimensional microbiome data.

Decoding the olfactory map: targeted transcriptomics link olfactory sensory neurons to glomeruli

The paper spatially maps olfactory receptors to glomeruli in the mouse olfactory bulb using targeted transcriptomics.

DNA extraction and host depletion methods significantly impact and potentially bias bacterial detection in a biological fluid

The paper evaluates DNA extraction and host depletion methods in bovine milk to improve bacterial detection for food safety, finding that magnetic-based methods are most effective and some methods introduce microbial detection bias.

Naught all zeros in sequence count data are the same

The paper evaluates zero-handling models in genomic and microbiome data, showing significant disagreement in identifying differentially expressed sequences and finding simple count models sufficient across processes.

Measuring and Mitigating PCR Bias in Microbiome Data

The paper measures and mitigates PCR bias in microbiome data using experimental validation and Bayesian models.

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

The paper develops a modeling framework to analyze microbiota dynamics in artificial human guts, revealing technical noise as a major source of variation and identifying sub-daily fluctuations and replicable trajectories.

Statistical Considerations in the Design and Analysis of Longitudinal Microbiome Studies

This paper reviews statistical challenges in longitudinal microbiome studies and provides guidelines for design and analysis.