Elior Rahmani

A unified model for cell-type resolution genomics from heterogeneous omics data

This paper introduces Unico, a method for deconvolving bulk genomic data into cell-type-resolved tensors, showing improved performance across gene expression and DNA methylation data.

Integrated ambient modeling and genetic demultiplexing of single-cell RNA+ATAC multiome experiments with Ambimux

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.

Single-cell DNA methylome and 3D genome atlas of the human subcutaneous adipose tissue

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.

Genome-wide association study and genomic risk prediction of age-related macular degeneration in Israel

This study performs a GWAS and evaluates polygenic risk scores for AMD in Israeli populations, finding consistent associations with known loci and demonstrating predictive power of international PRS.

Highly parameterized polygenic scores tend to overfit to population stratification via random effects

The paper shows that highly parameterized polygenic scores overfit to population stratification by including random variants, reducing their generalizability.

Identification of critical cell-types using genetic modules: A case study of neurodevelopmental disorders

The paper introduces MoToCC, a method to identify critical cell-types associated with neurodevelopmental disorders using genetic modules and single-cell data.

Phenotypic subtyping via contrastive learning

This paper introduces PACA, a contrastive learning method for subphenotype subtyping, which outperforms existing methods by capturing weak subphenotypic variation in complex diseases.

Identifying systematic variation at the single-cell level by leveraging low-resolution population-level data

The paper introduces Keris, a framework that uses population-level bulk data to infer single-cell gene expression changes, enabling hypothesis generation without extensive single-cell data collection.

Calling differential DNA methylation at cell-type resolution: addressing misconceptions and best practices

The paper benchmarks two methods for detecting cell-type-specific differential DNA methylation, finding TCA superior to CellDMC and discussing best practices for cell-type resolution analysis.

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

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

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

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

Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology

The paper demonstrates a method to infer cell-type-specific DNA methylation from bulk data, revealing novel associations with leukocyte composition and rheumatoid arthritis.

Distinguishing biological from technical sources of variation by leveraging multiple methylation datasets

The paper introduces CONFINED, a method to distinguish biological from technical variation in methylation data using multiple datasets, showing improved robustness and replicability.

A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference

This paper introduces a Bayesian method to estimate cell type composition from DNA methylation without requiring a methylation reference, showing it can identify individual cell types rather than just linear combinations.

RL-SKAT: An exact and efficient score test for heritability and set tests

RL-SKAT improves the accuracy of score tests for heritability and set-based genetic association by calculating exact p-values, avoiding the inflation or deflation of p-values seen in traditional methods.

Genome-wide methylation data mirror ancestry information

This paper shows that DNA methylation data can capture ancestry information and introduces a method to infer ancestry from methylation data without genetic data.