Biology subjects

Malagoli, G.

Publications and source records attributed to Malagoli, G..

4 recordsLinked to original sources

bioRxiv2026

XplainTDA: Explaining the topological structure of single-cell multi-omics data through perturbation

Single-cell experiments have improved our ability to study biological systems by profiling thousands of cells simultaneously. The data can be viewed as a point cloud in a proper experimental space, whose shape is defined by the gene regulatory network. Persistent homology has been proposed as a method for studying the intrinsic structure of data and has shown the potential to reveal novel insights. Existing approaches focus on detecting topological features or groups of cells, without a focus on explainability and interpretability. Here, we introduce an explainable topological data analysis framework for single-cell data (XplainTDA) that identifies molecular determinants underlying the topological structure of the data via a perturbation-based approach. Across omics and data sets, we show that the method is stable, reproducible, and interpretable, extending persistent homology from topological description to molecular interpretation.

bioinformatics↗

bioRxiv2026

Integration of single-cell multi-omic data with graph-based topic modelling

Recent advances in single-cell biology enable the profiling of multiple molecular layers, such as the transcriptome, epigenome, and surface proteins, within a single cell. Tackling the complexity of these data from different perspectives allows researchers to get the most complete insights into the biological properties of cells. Here, we propose a graph-based topic modelling method called bionSBM. Our method leverages well-known community-detection methods for multipartite graphs and the interpretability of topic modelling to cluster and explain high-dimensional, sparse, and noisy single-cell matrices. We applied our algorithm to paired single-cell multi-omics data, such as 10X Multiome, SHARE-seq, and CITE-seq. We showed that it achieves superior performance compared to state-of-the-art methods for ground-truth label retrieval, with high specificity and distinct biological interpretability.

bioinformatics↗

bioRxiv2025

Geometry aware graph attention networks to explain single-cell chromatin state and gene expression

High-throughput measurements that profile the transcriptome or the epigenome of single-cells are becoming a common way to study cell identity. These data are high dimensional, sparse and non linear. Here we present SEAGALL (Single-cell Explainable Geometry-Aware Graph Attention Learning pipeLine), a hypothesis free method to extract biologically relevant features from single-cell experiments based on geometry regularised autoencoders (GRAE) and explainable graph attention networks (GAT). We use a GRAE to embed the data into a latent space preserving the data geometry and we construct a cell-to-cell graph computing distances in the GRAE bottleneck. Exploiting the attention mechanism to dynamically learn the relevant edges, we use GATs to classify the cells and we explain the predictions of the model with XAI methods to unravel the features which are driving cell identity beyond marker genes. We apply our method to data sets from scRNA-seq, scATAC-seq and scChIP-seq experiments. SEAGALL can extract cell type specific and stable signatures which not only differ from the ones found in classical linear approaches but are less biassed by coverage and high expression.

bioinformatics↗

bioRxiv2022

Comprehensive analysis of long non-coding RNAs in breast cancer using topic modeling.

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long noncoding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

bioinformatics↗