José Alquicira-Hernández

Efficient genome-wide mapping of reproducible, context-dependent eQTLs at single-cell resolution

The paper introduces Dynema, a method for genome-wide mapping of context-dependent and independent eQTLs at single-cell resolution, showing improved detection compared to pseudobulking approaches.

The impact of sex on the immune system explored at the single-cell level

The paper examines sex differences in the immune system at the single-cell level, identifying sex-biased genes and eQTLs linked to autoimmune diseases.

Federated single-cell QTL meta-analysis reveals novel disease mechanisms

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

Protein Translation Dysregulation and Immune Cell Evasion Define Metastatic Clones in HPV-related Cancer of the Oropharynx

This study identifies mechanisms of immune evasion and metastasis in HPV-related oropharyngeal cancer using single-cell and spatial genomic data, showing dysregulated protein translation and immune pathway suppression in metastatic clones.

Single-cell atlas of the human immune system reveals sex-specific dynamics of immunosenescence

This study uses single-cell RNA sequencing to analyze immune aging in 982 donors, revealing sex-specific changes in immune cell composition and function during aging.

Identifying genetic variants that influence the abundance of cell states in single-cell data

The paper introduces GeNA, a tool to identify genetic variants influencing cell state abundance in single-cell data, finding five loci associated with immune cell state shifts.

Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal new mechanisms of genome regulation

The paper introduces MEOTIVE, a statistical framework to identify genetic effects on gene expression variability, revealing new mechanisms of genome regulation and associations with autoimmune disease risk.

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution

The paper maps cell-type-specific eQTLs for HLA genes across multiple tissues and identifies dynamic regulation of HLA-DQ genes in immune cells.

DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets

DevKidCC classifies kidney organoid cell subtypes accurately by comparing datasets to a reference model trained on human fetal kidney single-cell RNA-seq data.

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

Nebulosa recovers single cell gene expression signals by kernel density estimation

Nebulosa recovers gene expression signals in single-cell data using kernel density estimation to address drop-out and low expression issues.

No evidence that plasmablasts transdifferentiate into developing neutrophils in severe COVID-19 disease

The paper challenges a claim that plasmablasts transdifferentiate into developing neutrophils in severe COVID-19 by showing that UMAP embeddings may not reflect direct lineage relationships.

Programmatic access to bacterial regulatory networks with regutools

The paper introduces regutools, an R package that enables programmatic access to RegulonDB data for analyzing bacterial regulatory networks within the Bioconductor ecosystem.

Comprehensive benchmarking of computational deconvolution of transcriptomics data

This study benchmarks deconvolution methods for bulk transcriptomics data, showing reference marker selection and normalization impact performance, with single-cell methods performing comparably to bulk methods.

ascend: R package for analysis of single cell RNA-seq data

ascend provides an R package for streamlined single-cell RNA-seq analysis, offering functions for filtering, normalization, clustering, and visualization.

scPred: scPred: Cell type prediction at single-cell resolution

scPred is a method for predicting cell types from single-cell RNA sequencing data, achieving high accuracy in classifying cell states and subtypes across multiple tissues and disease contexts.