Se Jin Song

This study uses spatial transcriptomics to show that bladder carcinoma in situ has spatially structured cellular and metabolic heterogeneity within a single patient.

This study identifies distinct microbial and metabolic features in non-lesional acne skin using multi-omics analyses, showing molecular dysbiosis extends beyond visible lesions.

The paper analyzes gut microbiome data from three countries to show diet-microbiota associations vary by location and strain.

The authors reanalyze their pan-cancer microbiome findings using decontaminated data to address concerns about contamination, batch effects, and machine learning approaches.

Greengenes2 aligns 16S rRNA and shotgun metagenomic data using a whole-genome phylogeny, showing agreement in principal coordinates, taxonomy, and phenotype effect size.

EMPress provides an interactive tool for visualizing phylogenetic trees alongside multi-omic data, enabling exploratory analyses of microbiome and metabolome datasets.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.