Biology subjects

Lopes, M. B.

Publications and source records attributed to Lopes, M. B..

7 recordsLinked to original sources

bioRxiv2026

Regularized logistic regression-based gene selection for functional analysis of heterogeneous scRNA-seq macrophage populations in muscle aging and regeneration.

Background: Single-cell RNA sequencing enables the characterization of heterogeneous cellular responses across experimental conditions. However, feature-selection methods often prioritize genes associated with global condition differences while overlooking subgroup-specific transcriptional structure. This limitation is particularly relevant in complex tissues, where distinct cell states may contribute differently to disease, aging, or regeneration. Regularized logistic regression provides an interpretable framework for sparse gene selection, but standard penalties do not explicitly account for differences in gene-gene correlation patterns across annotated subgroups. Results: We introduce DisTwiner, a subgroup-aware extension of the twiner correlation-based regularization framework for binary classification problems with multiple annotated groups. DisTwiner computes pairwise angular scores between feature-correlation profiles across subgroup labels and incorporates the resulting feature-specific weights into an Elastic Net-regularized logistic regression model. We evaluated DisTwiner using simulated data and a single-cell RNA-seq dataset of macrophage populations isolated from young and aged regenerating murine skeletal muscle. In simulated data, alternative aggregation and transformation strategies showed comparable classification performance, with minimum-based exponential weighting producing the greatest dispersion of feature penalties. In the macrophage dataset, DisTwiner achieved classification performance comparable to standard Elastic Net logistic regression and higher than Random Forest under the evaluated setting. Although predictive accuracy was similar to Elastic Net, DisTwiner selected genes with more heterogeneous fold-change and correlation patterns across macrophage subpopulations. Downstream enrichment analyses showed that DisTwiner-selected features captured biologically relevant aging-associated pathways, including interferon-response signatures in specific macrophage populations, and could serve as an additional filtering layer after differential expression analysis. Conclusions: DisTwiner extends correlation-based regularization to binary classification settings with multiple annotated subgroups, enabling feature selection that accounts for subgroup-specific correlation structure. Applied to single-cell macrophage data, the method preserved competitive classification performance while prioritizing genes with cell-population-dependent expression patterns, supporting its use for interpretable subgroup-aware feature selection in high-dimensional omics data.

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bioRxiv2025

Performance Assessment of an Unsupervised Variable Selection Approach for Biomarker Discovery and Glioblastoma Subtyping

High-dimensional omics data often contain more variables than observations, which negatively impacts the performance of classical data analysis methods. Dimensionality reduction is typically addressed through variable selection strategies that incorporate a penalty term into the model. While effective for selecting task-specific variables, this approach may not be optimal when the goal is to preserve the dataset structure and the overall biological information for multiple downstream analyses. In such cases, a priori unsupervised variable selection is preferable. In this study, we evaluate several unsupervised variable selection approaches to derive a representative subset of the original dataset. Building on the performance assessment results, we introduce TRIM-IT, a novel tool for unsupervised variable selection, clustering, survival analysis, and differential gene expression analysis. Applied to glioblastoma (GBM) data, TRIM-IT identified three clusters that correlate with tumor histology, exhibit distinct survival curves, and display unique molecular profiles with genes potentially serving as biomarkers. The tool is available for reproduction and adaptation to other studies.

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bioRxiv2024

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

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bioRxiv2024

A Novel Tool for Multi-Omics Network Integration and Visualization: A Study of Glioma Heterogeneity

Gliomas are highly heterogeneous tumors with generally poor prognoses. Leveraging multi-omics data and network analysis holds great promise in uncovering crucial signatures and molecular relationships that elucidate glioma heterogeneity. However, the complexity of the problem and the high dimensionality of the data increase the challenges of integrating information across various biological levels. In this study, we developed a framework comprising two steps for variable selection based on sparse network estimation from various omics. Subsequently, we introduced MINGLE (Multi-omics Integrated Network for GraphicaL Exploration), a novel methodology designed to merge distinct multi-omics information into a single network, enabling the identification of underlying relations through an innovative integrated visualization. Applying this method to glioma data, with patients grouped according to the newest glioma classification guidelines, led to the selection of variables as potential candidates for novel glioma-type-specific biomarkers.

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bioRxiv2023

Integration of Multi-omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers

Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGG) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. On the other hand, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A and HEPN1. Overall, this classification method allowed to discriminate the different TCGA glioma patients with very high performance, while seeking for common information across multiple data types, ultimately enabling the understanding of essential mechanisms driving glioma heterogeneity and unveiling potential therapeutic targets.

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bioRxiv2023

Updating TCGA glioma classification through integration of molecular profiling data following the 2016 and 2021 WHO guidelines

The understanding of glioma disease has been evolving drastically with dedicated research into the genetic and molecular profiling of glioma tumour tissue. Molecular biomarkers have gained progressive and substantial importance in providing diagnostic information, leading to groundbreaking changes in the tumour classification system, criteria and taxonomy standardised by the 2016 and 2021 editions of the World Health Organization Classification of Tumours of the Central Nervous Systems guidelines (WHO-2016 and WHO-2021, respectively). Some of the insights into glioma disease derived from extensive research on open-source multi-omics databases, such as the Cancer Genome Atlas (TCGA). However, given the substantial changes in glioma classification, retrospective databases may harbour outdated diagnostic annotations, suboptimal for further research. Here we propose two methods for updating the tumour classification of TCGA glioma samples in accordance with WHO-2016 and WHO-2021 guidelines through the integration of curated molecular profiling information. Our methods allowed for the diagnostic update of 98% and 87% of evaluated TCGA glioma cases according to WHO-2016 and -2021, respectively, and highlighted changes in patient-specific diagnosis across both guidelines editions. Our reclassification pipelines are provided in software R, facilitating direct reproduction or tailoring upon new releases of WHO guidelines.

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bioRxiv2018

Sparse network-based regularization for the analysis of patientomics high-dimensional survival data

Data availability by modern sequencing technologies represents a major challenge in oncological survival analysis, as the increasing amount of molecular data hampers the generation of models that are both accurate and interpretable. To tackle this problem, this work evaluates the introduction of graph centrality measures in classical sparse survival models such as the elastic net.\n\nWe explore the use of network information as part of the regularization applied to the inverse problem, obtained both by external knowledge on the features evaluated and the data themselves. A sparse solution is obtained either promoting features that are isolated from the network or, alternatively, hubs, i.e., features that are highly connected within the network.\n\nWe show that introducing the degree information of the features when inferring survival models consistently improves the model predictive performance in breast invasive carcinoma (BRCA) transcriptomic TCGA data while enhancing model interpretability. Preliminary clinical validation is performed using the Cancer Hallmarks Analytics Tool API and the String database.\n\nThese case studies are included in the recently released glmSparseNet R package1, a flexible tool to explore the potential of sparse network-based regularizers in generalized linear models for the analysis of omics data.

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