Pau Badia-i-Mompel

The Hidden Biology of Wellbeing: A Multi-Omics Analysis Across Genomic, Epigenomic, and Transcriptomic Layers.

This study uses multi-omics data from 2,320 participants to identify latent factors capturing shared variation across genomic, epigenomic, and transcriptomic layers, finding one factor associated with wellbeing, though its significance diminishes after covariate correction.

Comparison and evaluation of methods to infer gene regulatory networks from multimodal single-cell data

The paper benchmarks methods for inferring gene regulatory networks from multimodal single-cell data and introduces a framework called GRETA to evaluate their performance and limitations.

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

This study evaluates how different transcriptomics analysis pipelines affect downstream functional enrichment results using FLOP, finding that data filtering significantly impacts gene set correlation.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

CollecTRI-derived signed regulons, spanning 1,183 transcription factors (TFs) and 45,856 TF-gene links, outperform DoRothEA, ChEA3, RegNetwork and Pathway Commons at identifying perturbed TFs in KnockTF data.

Spatial cell type mapping of multiple sclerosis lesions

This study maps cell types and their spatial distribution in multiple sclerosis lesions to understand disease progression and identify potential therapeutic targets.

decoupleR: Ensemble of computational methods to infer biological activities from omics data

decoupleR evaluates methods for inferring biological activities from omics data, finding simple linear models and consensus scores perform best in perturbation experiments.

Bioactivity descriptors for uncharacterized compounds

The paper introduces deep neural networks to infer bioactivity signatures for uncharacterized compounds, improving activity prediction across various biological endpoints.