Biology subjects

Picart-Armada, S.

Publications and source records attributed to Picart-Armada, S..

6 recordsLinked to original sources

bioRxiv2026

singIST: an R/Bioconductor library and Quarto dashboard for automated single-cell comparative transcriptomics analysis ofdisease models and humans

Preclinical disease models often diverge from human pathophysiology at single-cell resolution, complicating model selection and limiting translational value. We present singIST, an R/Bioconductor package for quantitative and explainable comparison of disease model scRNA-seq data against a human reference. For each superpathway, singIST fits an adaptive sparse multi-block PLS-DA model on human pseudobulk expression, integrated one-to-one orthology and cell type mapping, and translates model fold changes into the human expression space to compute signed recapitulation at the superpathway, cell type, and gene levels. To streamline interpretation and reporting, we provide singIST Visualizer, a companion Quarto/Shiny dashboard that loads singIST outputs and offers interactive exploration with export ready plots and tables, avoiding manual figure coding across many superpathways and models. We demonstrate the workflow. We illustrate an end-to-end workflow on an oxazolone mouse model against a human atopic dermatitis reference for two representative pathways: Dendritic Cells in regulating Th1/Th2 Development [BIOCARTA] and Cytokine-cytokine receptor interaction [KEGG]. singIST is distributed under the MIT License via Bioconductor, and the Visualizer is available on GitHub.

bioinformatics↗

bioRxiv2024

singIST: an integrative method for comparative single-cell trancriptomics between disease models and humans

MotivationDisease models serve as fundamental tools in drug discovery and early-stage drug development. However, these models are not a perfect reflection of human disease, and selecting a suitable model can be challenging. Existing computational approaches for molecular validation of pathophysiological resemblance to human conditions at single-cell resolution remain limited. Although quantitative computational methods exist to inform this selection, they are very limited at the single-cell resolution, which can be critical for model selection. Quantifying the resemblance of disease models to the human condition with single-cell technologies in an explainable, integrative, and generalizable manner remains a significant challenge. ResultsWe present singIST, a computational method for comparative single-cell transcriptomics analysis between disease models and human conditions. singIST provides explainable quantitative measures on disease model similarity to human condition at both pathway and cell type levels, highlighting the importance of each gene in the latter. These measures account for orthology, cell type presence in the disease model, cell type and gene importance in human condition, and gene changes in the disease model measured as fold change. This is achieved within a unifying framework that controls for the intrinsic complexities of single-cell data. We tested our method using three well-characterized murine models of moderate-to-severe Atopic Dermatitis, demonstrating its ability to recapitulate established biological knowledge while generating novel hypothesis through pathway-level analysis. Availability and implementationR library at https://github.com/DataScienceRD-Almirall/singIST and implementation code at https://github.com/DataScienceRD-Almirall/singIST_paper_results Author SummaryIn this study, we set out a method to improve how researchers evaluate disease models - tools that are essential for understanding human illness and developing new treatments. A major limitation of current methods is that they overlook the fine-scale differences between disease models and human conditions at the single-cell level. Yet, many diseases are driven by changes that occur in specific cell types. To address this, we developed singIST, a method that allows us to compare disease models to human diseases using single-cell gene expression data. What makes singIST unique is its ability to provide clear, interpretable measures of similarity between a disease model and the human condition, specifically at the level of cell types and groups of functionally related genes. We demonstrated the method using three well-characterized mouse models of atopic dermatitis and showed that singIST could both reflect known disease biology and uncover new insights into disease mechanisms. Further, we developed statistical tests to validate the trained models. Our approach is flexible and broadly applicable, making it a valuable tool for improving the selection and understanding of disease models in biomedical research.

bioinformatics↗

bioRxiv2024

In Silico Treatment: a computational framework for animal model selection and drug assessment

The translation of findings from animal models to human disease is a fundamental part in the field of drug development. However, only a small proportion of promising preclinical results in animals translate to human pathophysiology. This underscores the necessity for novel data analysis strategies to accurately evaluate the most suitable animal model for a specific purpose, ensuring cross-species translatability. To address this need, we present In Silico Treatment (IST), a computational method to assess translation of disease-related molecular expression patterns between animal models and humans. By simulating changes observed in animals onto humans, IST provides a holistic picture of how well animal models recapitulate key aspects of human disease, or how treatments transform pathogenic expression patterns to healthy ones. Furthermore, IST highlights particular genes that influence molecular features of pathogenesis or drug mode of action. We demonstrate the potential of IST with three applications using bulk transcriptomics data. First, we assessed two mouse models for idiopathic pulmonary fibrosis (IPF): one involving injury with intra-tubular Bleomycin exposure, and the other Adeno-associated-virus-induced, TGF{beta}1-mediated tissue transformation (AAV6.2-TGF{beta}1). Both models exhibited gene expression patterns resembling extracellular matrix derangement in human IPF, whereas differences in VEGF-driven vascularization were observed. Second, we confirmed known features of non-alcoholic steatohepatitis (NASH) mouse models, including choline-deficient, l-amino acid-defined diet (CDAA), carbon tetrachloride hepatotoxicity injury (CCl4) and bile duct ligation surgery (BDL). Overall, the three mouse models recapitulated expression changes related to fibrosis in human NASH, whereas model-specific differences were found in lipid metabolism, inflammation, and apoptosis. Third, we reproduced the strong anti-fibrotic signature and induction of the PPAR signaling observed in the Elafibranor experimental treatment for NASH in the CDAA model. We validated the contribution of known disease-related genes to the findings made with IST in the IPF and NASH applications. The complete data integration IST framework, including an interactive app to integrate and compare datasets, is made available as an open-source R package. Author summaryPreclinical testing plays a pivotal role in the drug development process, serving as a crucial evaluation phase before a new drug can be tested on humans in clinical trials. The drug must undergo a rigorous evaluation in in vivo and in vitro preclinical studies to assess its safety and efficacy. However, positive outcomes in preclinical animal models do not always translate to positive results in humans, mainly due to biological differences. Therefore, selecting an animal model that closely mirrors human disease traits and detecting and accounting for model limitations is of paramount importance. Over the last decade, the availability of gene expression data in both animals and humans has substantially increased. Gene expression states and perturbations are routinely employed as a proxy to predict and understand changes in disease states. Here, we developed In Silico Treatment, a computational method designed to overlay the gene expression changes observed in animals onto humans, quantifying the change in human disease status. We applied this method to mouse models for idiopathic pulmonary fibrosis and non-alcoholic steatohepatitis, two severe fibrotic diseases. We successfully identified known features of the disease models and provide a granular gene-level rationale behind our predictions. Consequently, our method shows promise as an effective approach to improve animal model selection and thus clinical translation.

bioinformatics↗

bioRxiv2020

MultiPaths: a Python framework for analyzing multi-layer biological networks using diffusion algorithms

SummaryHigh-throughput screening yields vast amounts of biological data which can be highly challenging to interpret. In response, knowledge-driven approaches emerged as possible solutions to analyze large datasets by leveraging prior knowledge of biomolecular interactions represented in the form of biological networks. Nonetheless, given their size and complexity, their manual investigation quickly becomes impractical. Thus, computational approaches, such as diffusion algorithms, are often employed to interpret and contextualize the results of high-throughput experiments. Here, we present MultiPaths, a framework consisting of two independent Python packages for network analysis. While the first package, DiffuPy, comprises numerous state-of-the-art diffusion algorithms applicable to any generic network, the second, DiffuPath, enables the application of these algorithms on multi-layer biological networks. To facilitate its usability, the framework includes a command line interface, reproducible examples, and documentation. To demonstrate the framework, we conducted several diffusion experiments on three independent multi-omics datasets over disparate networks generated from pathway databases, thus, highlighting the ability of multi-layer networks to integrate multiple modalities. Finally, the results of these experiments demonstrate how the generation of harmonized networks from disparate databases can improve predictive performance with respect to individual resources. AvailabilityDiffuPy and DiffuPath are publicly available under the Apache License 2.0 at https://github.com/multipaths. Contactsergi.picart@upc.edu and daniel.domingo.fernandez@scai.fraunhofer.de

bioinformatics↗

bioRxiv2020

The effect of statistical normalisation on network propagation scores

MotivationNetwork diffusion and label propagation are fundamental tools in computational biology, with applications like gene-disease association, protein function prediction and module discovery. More recently, several publications have introduced a permutation analysis after the propagation process, due to concerns that network topology can bias diffusion scores. This opens the question of the statistical properties and the presence of bias of such diffusion processes in each of its applications. In this work, we characterised some common null models behind the permutation analysis and the statistical properties of the diffusion scores. We benchmarked seven diffusion scores on three case studies: synthetic signals on a yeast interactome, simulated differential gene expression on a protein-protein interaction network and prospective gene set prediction on another interaction network. For clarity, all the datasets were based on binary labels, but we also present theoretical results for quantitative labels. ResultsDiffusion scores starting from binary labels were affected by the label codification, and exhibited a problem-dependent topological bias that could be removed by the statistical normalisation. Parametric and non-parametric normalisation addressed both points by being codification-independent and by equalising the bias. We identified and quantified two sources of bias -mean value and variance- that yielded performance differences when normalising the scores. We provided closed formulae for both and showed how the null covariance is related to the spectral properties of the graph. Despite none of the proposed scores systematically outperformed the others, normalisation was preferred when the sought positive labels were not aligned with the bias. We conclude that the decision on bias removal should be problem and data-driven, i.e. based on a quantitative analysis of the bias and its relation to the positive entities. AvailabilityThe code is publicly available at https://github.com/b2slab/diffuBench Contactsergi.picart@upc.edu

bioinformatics↗

bioRxiv2018

Benchmarking network propagation methods for disease gene identification

BackgroundIn-silico identification of potential disease genes has become an essential aspect of drug target discovery. Recent studies suggest that one powerful way to identify successful targets is through the use of genetic and genomic information. Given a known disease gene, leveraging intermolecular connections via networks and pathways seems a natural way to identify other genes and proteins that are involved in similar biological processes, and that can therefore be analysed as additional targets.\n\nResultsHere, we systematically tested the ability of 12 varied network-based algorithms to identify target genes and cross-validated these using gene-disease data from Open Targets on 22 common diseases. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. We also compared several cross-validation schemes and showed that different choices had a remarkable impact on the performance estimates. When seeding biological networks with known drug targets, we found that machine learning and diffusion-based methods are able to find novel targets, showing around 2-4 true hits in the top 20 suggestions. Seeding the networks with genes associated to disease by genetics resulted in poorer performance, below 1 true hit on average. We also observed that the use of a larger network, although noisier, improved overall performance.\n\nConclusionsWe conclude that machine learning and diffusion-based prioritisers are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large effect of several factors on prediction performance, especially the validation strategy, input biological network, and definition of seed disease genes.

genetics↗