Biology subjects

Shaigan, M.

Publications and source records attributed to Shaigan, M..

4 recordsLinked to original sources

bioRxiv2026

Multimodal single-cell analysis uncovers transcription factor networks underlying T-cell aging

Aging of the immune system is associated with chronic inflammation and impaired immune function, yet the regulatory mechanisms underlying these changes remain incompletely understood. Here, we generated paired single-cell transcriptomic and chromatin accessibility profiles from peripheral blood mononuclear cells of young and old healthy donors to characterize immune aging at single-cell resolution. Using an integrative computational framework for multi-omic single-cell analysis, we detected pronounced age-associated changes in T cells, including loss of naive CD8+ T cells and expansion of differentiated memory and effector populations. Aging was accompanied by increased inflammatory signaling and reduced oxidative phosphorylation programs. Enhancer-based gene regulatory network analyses identified a reduced role of TCF7 and increased activity of inflammatory regulators, including FOSL2, in aged T cells. Integration with genetic association and eQTL datasets further supported the functional relevance of age-associated regulatory regions and their target genes.

bioinformatics↗

bioRxiv2025

PILOT-GM-VAE: Patient-Level Analysis of single cell Disease Atlas with Optimal Transport of Gaussian Mixture Variational Autoencoders

MotivationThe analysis of single cell disease atlases represents a challenge due to the presence of batch effects, low quality of disease samples, and the multi-scale nature of the data, i.e., samples are described by different cell distributions. Because of these, few computational approaches are performing sample-level disease progression analysis so far. ResultsHere, we introduce Patient-Level Analysis with Optimal Transport based on Gaussian Mixture Variational Autoencoders (PILOT-GM-VAE). PILOT-GM-VAE explores the power of GM-VAE to estimate models describing complex single cell distributions through efficient optimal transport algorithms for estimating the distance between Gaussian Mixtures. Extensive benchmarking on several single cell disease atlases and competing approaches demonstrates the performance of PILOT-GM-VAE in sample-level clustering, sample-level trajectory inference, and batch correction tasks. Moreover, we performed a case study on a breast cancer disease atlas, where PILOT-GM-VAE highlighted cellular and molecular changes associated with breast cancer disease progression. AvailabilityThe software, code, and data for benchmarking are available at https://github.com/CostaLab/PILOT-GM-VAE/tree/main

bioinformatics↗

bioRxiv2023

RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data

BackgroundMassive amounts of data are produced by combining next-generation sequencing (NGS) with complex biochemistry techniques to characterize regulatory genomics profiles, such as protein-DNA interaction and chromatin accessibility. Interpretation of such high-throughput data typically requires different computation methods. However, existing tools are usually developed for a specific task, which makes it challenging to analyze the data in an integrative manner. ResultsWe here describe the Regulatory Genomics Toolbox (RGT), a computational library for the integrative analysis of regulatory genomics data. RGT provides different functionalities to handle genomic signals and regions. Based on that, we developed several tools to perform distinct downstream analyses, including the prediction of transcription factor binding sites using ATAC-seq data, identification of differential peaks from ChIP-seq data, and detection of triple helix mediated RNA and DNA interactions, visualization, and finding an association between distinct regulatory factors. ConclusionWe present here RGT; a framework to facilitate the customization of computational methods to analyze genomic data for specific regulatory genomics problems. RGT is a comprehensive and flexible Python package for analyzing high throughput regulatory genomics data and is available at: https://github.com/CostaLab/reg-gen. The documentation is available at: https://reg-gen.readthedocs.io

bioinformatics↗

bioRxiv2022

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell and pathomics data to find sample level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics and pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell and pathomics data. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression, and tissue structures related to the disease trajectories supporting interpretation of predictions.

bioinformatics↗