Irene Papatheodorou

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.

FlyAnatoView projects Drosophila scRNA-seq data onto anatomical diagrams to restore spatial context and enable exploration of gene expression patterns across tissues.

EISCA and EISTA are pipelines for single-cell and spatial transcriptomics analysis that provide standardized, modular workflows for data processing and downstream analysis.

scDenorm reverts normalized single-cell omics data to raw counts to improve integration and reduce biases in downstream analysis.

This paper develops a molecular trajectory-based framework to model MASLD progression using transcriptomic and proteomic data, identifying novel biomarkers and mechanistic insights.

The paper uses single-cell multi-omic lineage tracing to identify transcriptional, epigenetic, and genetic factors that predict cancer evolution and drug tolerance.

This study challenges the ortholog conjecture by analyzing scRNA-seq data across species, revealing gene-specific patterns of expression conservation and divergence.

The paper reanalyzes public pig proteomics data to map baseline protein expression across 14 organs and compares it with human orthologs.

scGOclust compares cell types across species using gene ontology to identify functional conservation and divergence in scRNA-seq data.

This paper evaluates 31 cell-type deconvolution methods across human and mouse tissues, identifies regression-based methods as effective, and introduces the CATD pipeline for reproducible analysis.

The paper compares gene expression in melanocyte regeneration and melanoma to identify signaling pathways that promote regeneration and suppress cancer traits.

This study benchmarks cross-species integration methods for single-cell RNA-seq data, finding that scANVI, scVI, and SeuratV4 balance species mixing and biology conservation.

The paper introduces a 1D coordinate model for the human gut to enable spatial integration and analysis of single-cell data across different anatomical representations.

The paper reanalyzes 24 public proteomics datasets to assess baseline protein abundance in 31 human organs and integrates the results into the Expression Atlas for combined proteomics and transcriptomics analysis.

The paper reanalyzes proteomics datasets from mouse and rat tissues to compare baseline protein expression across species and organs, finding high correlation among orthologs in brain, kidney, heart, and liver.

The paper introduces a pipeline to re-analyze public DIA proteomics datasets, integrating results into Expression Atlas to enhance data availability and utility.

The paper performs a meta-analysis of single-cell RNA data from 9 studies to identify consistent immune response patterns in COVID-19 patients, finding consistent upregulation of type I interferon and downregulation of mitochondrial genes.

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.

The paper presents the eQTL Catalogue, a resource containing uniformly processed eQTLs from 21 studies, enabling integrative analysis and identification of cell-type-specific eQTLs and disease colocalisations.

The UCSC Cell Browser allows visualization of gene expression and metadata in single-cell datasets through a web interface and Python package.

This study compiles a proteomics resource for cancer cell lines and tumour samples, finding that protein abundance in cell lines often reflects tumours but fails to capture subtype-specific variations.

The paper introduces an interactive scRNA-Seq analysis environment based on Galaxy, integrating ~70 functions for scalable and accessible single-cell data analysis.

DRscDB provides a database for comparing scRNA-seq data across species, enabling searches for orthologous genes and cell type-specific expression patterns.

The paper introduces a Galaxy-based suite for scRNA-seq analysis, offering workflows, training resources, and tools for preprocessing, normalization, and clustering across 10x and alternative platforms.

The paper provides a user guide to five online tools for exploring and visualizing PCAWG data, demonstrating their integration with external resources to enhance cancer genomics research.

This paper integrates Galaxy with Kubernetes to enable scalable, cloud-agnostic bioinformatics workflows.

The paper introduces clusterSeq, a method for identifying co-expression clusters in high-throughput sequencing data by incorporating structural elements between experimental conditions.