Ricardo O. Ramirez Flores

The paper presents the Human Gut Cell Atlas v1.0, a unified reference of gut cell diversity, revealing how tissue structure influences cellular composition and enabling disease and organoid analysis.

mc-ASTRA integrates molecular, spatial, and sample-level data to create interpretable maps of tissue-state variability and multicellular processes.

ParTIpy scales archetypal analysis for biological trade-offs and integrates with single-cell workflows, enabling task inference in large datasets.

This paper constructs a transcriptional patient map of SLE using single-cell and bulk RNA-seq data, identifying conserved immune programs in blood and kidney tissue.

This paper performs a cross-organ meta-analysis of single-cell transcriptomic data to identify shared and organ-specific gene expression programs in fibrotic diseases.

ReCoN models multicellular coordination by integrating cell-cell communication and intracellular regulation to predict cellular responses to perturbations.

This study shows that reverse cardiac remodeling is a constrained multicellular state with partial recovery of metabolic programs but persistent inflammatory-mitochondrial dysfunction, mediated by lymphatic-derived Reelin.

This paper constructs a single-cell atlas of circulating immune cells to understand inflammation across diseases and develops a classifier for inflammatory diseases.

This paper integrates bulk and single-cell transcriptomic data from 25 heart failure studies to identify conserved multicellular coordination patterns in cardiac remodeling.

The paper models a macrophage-myofibroblast circuit to predict fibrosis types and shows targeting the myofibroblast autocrine loop reduces fibrosis after myocardial infarction.

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.

SOFA identifies novel sources of variation in multi-omic data by incorporating known covariates and focusing on unexplained factors.

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

The paper introduces a framework for analyzing cross-condition single-cell atlases to identify multicellular disease programs, validated across multiple datasets.

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.

LIANA+ is a framework for analyzing cell-cell communication in single-cell and spatial transcriptomics data, integrating multiple methods and supporting multi-omics data.

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

This study generates a high-resolution spatial multi-omic map of human myocardial infarction by integrating single-cell gene expression, chromatin accessibility, and spatial transcriptomic data to reveal cellular zones of injury, repair, and remodeling.

The paper compares 15 resources and 6 methods for inferring cell-cell communication from single-cell RNA data, finding significant variation in predicted interactions and biological interpretations.

MISTy is a framework for analyzing spatial omics data to identify inter-cellular signaling interactions across different spatial contexts in breast cancer datasets.

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

This study uses spatial transcriptomics to map the transcriptomic landscape of healing murine colon, identifying spatially organized transcriptional programs and their association with human diseases.