Biology subjects

Acharyya, S.

Publications and source records attributed to Acharyya, S..

10 recordsLinked to original sources

bioRxiv2026

SCG: Spatially Co-Expressed Gene Identification through Spatially Varying Networks

Spatial transcriptomics has enabled the advancement of gene expression analysis, yet spatial co-expression remains understudied. We introduce spatial covariance regression (SCR), a scalable Bayesian factor-model-based framework for estimation of spatially-resolved gene co-expression networks across tissue domains. These networks provide the spatial map of gene-gene correlations and enable the identification of spatially co-expressed genes (SCGs), which serve as potential prognostic biomarkers and therapeutic targets.

genomics↗

bioRxiv2026

Matched pancreatic cancer liver metastatic model system reveals cancer cell-dependent organotropism and site-specific tumor microenvironment reflective of human disease

Background: Pancreatic ductal adenocarcinoma (PDAC) mortality is driven largely by liver metastatic disease; however, the malignant cell states and tumor microenvironmental features of PDAC liver metastases remain obscure. Methods: We developed a transplant model system of matched pancreatic and liver tumors to study PDAC metastatic progression. Using this model, we identified murine PDAC cell lines with distinct liver metastatic capacities and performed multiomic profiling of matched primary pancreatic and metastatic liver tumors. Transcriptional programs associated with high and low liver tropism were defined and evaluated across tumor models and independent human PDAC datasets. Spatial and tumor-immune interaction analyses were used to characterize microenvironmental niches, immune composition, and cellular relationships within primary and metastatic tumors. Results: A high-liver-tropic transcriptional program was enriched in high liver-tropic cell lines and malignant cells within liver metastases, conserved across human PDAC datasets, and associated with inferior patient survival. High- and low-liver-tropic tumor states occupied distinct liver microenvironmental niches and exhibited different tumor-immune communication networks, accompanied by local and systemic changes in immune composition. Liver metastases also displayed features of enhanced immunosuppression, including increased proximity of CD4+ and CD8+ T cells to tumor cells and greater spatial association between regulatory T cells and exhausted CD8+ T cells. Conclusions: These findings identify conserved PDAC cell states associated with differential liver metastatic capacity and demonstrate that liver metastasis is accompanied by spatial and immunologic remodeling of the tumor microenvironment. Together, the study provides a framework for understanding how tumor-intrinsic metastatic programs interact with site-specific immune ecosystems in PDAC.

cancer biology↗

bioRxiv2026

Multi-resolution Spatial Graphical Regression Models for Hierarchical Spatial Transcriptomics Data

Advances in spatial transcriptomics (ST) technologies enable systematic molecular characterization of tumor microenvironment, tumor gradients and gene regulatory networks. Cancer progression is known to vary along pathological gradients, yet existing network approaches for gene network inference typically ignore hierarchical spatial organization across the tumor. We develop a Bayesian multi-resolution spatial graphical regression (mSGR) framework to infer spatially varying gene networks from multi-resolution ST data. The proposed model allows precision matrices to vary across hierarchically structured spatial domains, capturing both local and global organization within the tumor. To identify spatially varying regulatory relationships, we introduce a spatially structured edge selection strategy that borrows strength across regions according to spatial proximity and pathological gradients, while Gaussian-process priors flexibly model spatial variation in edge strengths. Scalable inference is achieved through an augmented mean-field variational Bayes algorithm with node-wise parallel regressions, enabling efficient estimation in high-dimensional settings. Simulation studies demonstrate improved recovery of network structures compared with competing approaches. Applying mSGR to multi-resolution ST data from kidney cancer reveals stronger regulatory connectivity in transitional regions of epithelial-mesenchymal transition pathway and identifies hub genes along the tumor gradient, illustrating how spatially resolved network analysis can provide key insights into tumor microenvironment organization.

genomics↗

bioRxiv2025

Spatial analysis reveals a novel inflammatory tumor transition state which promotes a macrophage-driven induction of sarcomatoid renal cell carcinoma

Sarcomatoid renal cell carcinoma (sRCC) is an aggressive transdifferentiation of epithelioid clear cell RCC (ccRCC) tumors that shows heightened response to immunotherapy. The underlying biology leading to sarcomatoid transformation and mechanisms contributing to immunotherapy response are not well understood. Novel single cell spatial techniques were used in ccRCC and sRCC tumors from 40 patients to understand the spatial sRCC transformation and corresponding immune changes. A transcriptional transition state in epithelioid ccRCC cells along a continuum to mesenchymal sRCC was identified which expresses high levels of pro-inflammatory cytokines and an immune infiltrate. In vitro studies demonstrated that M2-like macrophages, recruited to the tumor by the transition state, induce full transition to the sarcomatoid state. A combination of increased PD-L1 expression and T-cells recruited by the transition state was observed, consistent with the increased immunotherapy response. This study enriches our understanding of the mechanisms leading to development and immune responsiveness of sRCC paving the way for novel approaches to diminish RCC progression.

cancer biology↗

bioRxiv2025

Spatially varying graph estimation for spatial transcriptomics cancer data

Modern spatial transcriptomic profiling techniques facilitate spatially resolved, high-dimensional assessment of cellular gene transcription across the tumor domain. The characterization of spatially varying gene networks enables the discovery of heterogeneous regulatory patterns and biological mechanisms underlying cancer etiology. We propose a spatial Graphical Regression (sGR) model to infer spatially varying graphs for high-resolution multivariate spatial data. Unlike existing graphical models, sGR explicitly incorporates spatial information to infer non-linear conditional dependencies through Gaussian processes. It conducts sparse estimation and selection of spatially varying edges, at both spatial and sub-spatial levels. Extensive simulation studies illustrate the profitability of sGR for spatial graph structural recovery and estimation accuracy. Our methods are motivated by and applied to two spatial transcriptomics data sets in breast and prostate cancer, to investigate spatially varying gene connectivity patterns across the tumor micro-environment. Our findings reveal several novel spatial interactions between genes related to immune activation and carcinogenesis regulation such as CD19 in breast cancer and ARHGAP family in prostate cancer. We also provide a modular software package for fitting and visualization of spatially varying graphs.

genomics↗

bioRxiv2024

CAFE: An Integrated Web App for High-Dimensional Analysis and Visualization in Spectral Flow Cytometry

Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analyzing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFE as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging and annotation. Using CAFE, we demonstrated analysis of a human PBMC dataset of 350,000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all. CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe.

systems biology↗

bioRxiv2023

Probabilistic Graphical Modeling under Heterogeneity

Probabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad applicability to cases where sample-specific heterogeneity exists e.g. tumor heterogeneity. We propose a flexible Bayesian approach called Graphical Regression (GraphR) which (a) allows direct incorporation of intrinsic factors of sample heterogeneity at different scales through a regression-based formulation, (b) enables sparse network estimation at a sample-specific level, (c) allows identification and uncertainty quantification of potential effects of heterogeneity on network structures, and (d) is computationally efficient through the use of variational Bayes algorithms. We illustrate the comparative efficiency of GraphR against existing methods in terms of graph structure recovery and computational cost across multiple realistic simulation settings. We use GraphR to analyze four diverse multi-omics and spatial transcriptomics datasets to study inter- and intra-sample molecular networks and delineate biological discoveries that otherwise cannot be revealed by existing approaches. We have developed a GraphR R-package along with an accompanying Shiny App that provides comprehensive analysis and dynamic visualization functions.

genomics↗

bioRxiv2022

A monotone single index model for missing-at-random longitudinal proportion data

Beta distributions are commonly used to model proportion valued response variables, commonly encountered in longitudinal studies. In this article, we develop semi-parametric Beta regression models for proportion valued responses, where the aggregate covariate effect is summarized and flexibly modeled, using a interpretable monotone time-varying single index transform of a linear combination of the potential covariates. We utilize the potential of single index models, which are effective dimension reduction tools and accommodate link function misspecification in generalized linear mixed models. Our Bayesian methodology incorporates the missing-at-random feature of the proportion response, and utilize Hamiltonian Monte Carlo sampling to conduct inference. We explore finite-sample frequentist properties of our estimates, and assess the robustness via detailed simulation studies. Finally, we illustrate our methodology via application to a motivating longitudinal dataset on obesity research recording proportion body fat.

developmental biology↗

bioRxiv2021

SpaceX: Gene Co-expression Network Estimation for Spatial Transcriptomics

MotivationThe analysis of spatially-resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. ResultsWe develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data. Availability and implementationThe SpaceX R-package is available at github.com/bayesrx/SpaceX. Contactxzhousph@umich.edu, veerab@umich.edu Supplementary informationSupplementary data are available at bookdown.org/satwik91/SpaceX_supplementary/.

bioinformatics↗

bioRxiv2021

A Bayesian Precision Medicine Framework for Calibrating Individualized Therapeutic Indices in Cancer

The development and clinical implementation of evidence-based precision medicine strategies has become a realistic possibility, primarily due to the rapid accumulation of large-scale genomics and pharmacological data from diverse model systems: patients, cell-lines and drug perturbation studies. We introduce a novel Bayesian modeling framework called the individualized theRapeutic index (iRx) model to integrate high-throughput pharmacogenomic data across model systems. Our iRx model achieves three main goals: first, it exploits the conserved biology between patients and cell-lines to calibrate therapeutic response of drugs in patients; second, it finds optimal cell line avatars as proxies for patient(s); and finally, it identifies key genomic drivers explaining cell line-patient similarities. This is achieved through a semi-supervised learning approach, that conflates (unsupervised) sparse latent factor models with (supervised) penalized regression techniques. We propose a unified and tractable Bayesian model for estimation, and inference is conducted via efficient posterior sampling schemes. We illustrate and validate our approach using two existing clinical trial datasets in multiple myeloma and breast cancer studies. We show that our iRx model improves prediction accuracy compared to naive alternative approaches, and it consistently outperforms existing methods in literature in both in multiple simulation scenarios as well as real clinical examples.

genomics↗