Marcus Alvarez

The paper introduces ember, an entropy-based framework to assess gene specificity in single-cell RNA sequencing data, revealing unexpected patterns in gene expression across tissues and mouse strains.

The paper introduces ambimux, a method to estimate and demultiplex ambient molecule contamination in single-cell RNA+ATAC multiome data, improving the detection of biologically meaningful signals.

The paper characterizes the epigenomic and 3D genomic landscape of human subcutaneous adipose tissue at the cell-type level and links epigenomic variation to abdominal obesity risk.

This study uses longitudinal single-cell adipose transcriptomics to identify genes and variants associated with weight loss after bariatric surgery and shows that BMI polygenic risk scores predict weight loss outcomes.

The paper evaluates demultiplexing methods for joint snRNA/snATAC experiments under ambient contamination and introduces a new metric to assess their performance.

The paper integrates single-cell omics with biobank data to show SREBF1 risk variants affect adipocyte gene expression in trans, impacting abdominal obesity.

AutoComplete improves phenotype imputation in biobank data, enhancing GWAS power and genetic discovery.

This study identifies 12,111 SNPs associated with height from 5.4 million individuals, explaining most common SNP-based heritability and showing ancestry-specific prediction accuracy.

The paper introduces DIEM, a semi-supervised machine learning classifier to remove debris-contaminated droplets in snRNA-seq, improving clustering quality across three datasets.

Bisque estimates cell composition in bulk RNA-seq using single-cell data, improving accuracy and efficiency over existing methods.

The paper integrates gene expression and GWAS data to identify novel genes associated with obesity-related traits.