Biology subjects

Micheletti, S.

Publications and source records attributed to Micheletti, S..

3 recordsLinked to original sources

bioRxiv2026

Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization

Gene regulatory networks (GRNs) provide a mechanistic framework for understanding how transcription factors coordinate gene expression to establish cellular identity and phenotype. Methods that integrate gene expression with motif-derived regulatory priors and other sources of biological information have substantially advanced gene regulatory network inference by reconstructing condition-specific regulatory architecture. These approaches estimate the evidence supporting regulatory interactions and have proven remarkably successful in a wide range of biological applications. A complementary view of regulatory networks, however, seeks to estimate the effect of those interactions on gene expression itself, providing a framework in which regulatory edges can be interpreted as activating or inhibitory influences on transcription. We developed Giraffe, a biologically informed matrix factorization framework that jointly estimates transcription factor activities and gene regulatory networks by integrating gene expression, motif-based regulatory priors, and transcription factor protein-protein interactions. Giraffe estimates signed partial regulatory effects whose magnitude and sign can be interpreted as the strength and direction of transcriptional regulation. Building directly on the biological framework established by methods such as PANDA, Giraffe provides a complementary representation of gene regulatory networks that emphasizes mechanistic interpretation while remaining scalable, flexible, and computationally efficient. Across synthetic benchmarks, six human tissues, yeast transcription factor perturbation experiments, and liver hepatocellular carcinoma, Giraffe accurately reconstructs regulatory interactions while distinguishing activating from inhibitory regulation with high accuracy. The inferred networks recover known features of tissue-specific regulation, correctly classify regulatory effects in transcription factor perturbation experiments, and identify biologically coherent changes in regulatory programs associated with liver cancer. Together, these results demonstrate that estimating the direction of transcriptional regulation provides a complementary perspective on gene regulatory networks that facilitates biological interpretation and hypothesis generation.

systems biology↗

bioRxiv2024

Reproducible processing of TCGA regulatory networks

BackgroundTechnological advances in sequencing and computation have allowed deep exploration of the molecular basis of diseases. Biological networks have proven to be a useful framework for interrogating omics data and modeling regulatory gene and protein interactions. Large collaborative projects, such as The Cancer Genome Atlas (TCGA), have provided a rich resource for building and validating new computational methods resulting in a plethora of open-source software for downloading, pre-processing, and analyzing those data. However, for an end-to-end analysis of regulatory networks a coherent and reusable workflow is essential to integrate all relevant packages into a robust pipeline. FindingsWe developed tcga-data-nf, a Nextflow workflow that allows users to reproducibly infer regulatory networks from the thousands of samples in TCGA using a single command. The workflow can be divided into three main steps: multi-omics data, such as RNA-seq and methylation, are downloaded, preprocessed, and lastly used to infer regulatory network models with the netZoo software tools. The workflow is powered by the NetworkDataCompanion R package, a standalone collection of functions for managing, mapping, and filtering TCGA data. Here we show how the pipeline can be used to study the differences between colon cancer subtypes that could be explained by epigenetic mechanisms. Lastly, we provide pre-generated networks for the 10 most common cancer types that can be readily accessed. Conclusionstcga-data-nf is a complete yet flexible and extensible framework that enables the reproducible inference and analysis of cancer regulatory networks, bridging a gap in the current universe of software tools.

bioinformatics↗

bioRxiv2023

Higher-order correction of persistent batch effects in correlation networks

Systems biology methods often rely on correlations in gene expression profiles to infer co-expression networks, commonly used as input for gene regulatory network inference or to identify functional modules of co-expressed or co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene. These adjustments have been shown to improve the fidelity of DE analysis. However, these methods do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading network inference methods that use gene co-expression to identify false, nonbiological associations, even when the input data is corrected using standard batch correction. In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. Subsequently, we introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data.

bioinformatics↗