Biology subjects

Shojaie, A.

Publications and source records attributed to Shojaie, A..

14 recordsLinked to original sources

bioRxiv2026

Integrating Spatially Adjusted Protein Summaries for Survival Prediction in Spatial Proteomics

Recent advances in spatial proteomics, particularly imaging mass cytometry, enable the measurement of protein expression at the single-cell level while preserving a spatial context. Conventional survival analyses, however, typically rely on patient-level averages of protein intensities and therefore overlook spatial heterogeneity and tissue architecture. To address this limitation, we introduce a framework that incorporates spatial information into survival modeling by generating spatially adjusted protein summaries (SAPS). In this approach, cell-level protein intensities within each patient are modeled using spatial spline regression to capture spatial trends. From these models, we extract two complementary features: a spatially adjusted mean expression and a residual variance that reflects cell-to-cell variability unexplained by spatial effects. These summaries are then incorporated into Cox proportional hazards models in combination with clinical covariates. We further show that our estimator is asymptotically equivalent to an oracle estimator under mild regularity conditions. In simulation studies, our proposed framework achieved improved predictive performance compared to other alternative methods. The application of the method to breast cancer imaging mass cytometry data indicate that spatially adjusted summaries may enhance survival prediction and reveal biologically interpretable spatial protein patterns, suggesting high translational potential. This methodology offers an efficient means of translating complex spatial proteomics data into patient-level features, providing both improved survival prediction and new insights into the role of spatial heterogeneity in cancer outcomes. R package is available on the Comprehensive R Archive Network repository at https://cran.r-project.org/web/packages/SurvSPro/index.html

bioinformatics↗

bioRxiv2026

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset

Rare disease research and diagnosis rely on the integration of genomic and phenotypic data generated across diverse clinical sites; however, the absence of widely adopted standards for representing genomic data and associated metadata has limited data interoperability, reuse, and cross-study analysis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was established to investigate challenging rare disease cases and evaluate emerging multi-omic technologies for clinical translation. To support coordinated data integration across distributed research sites, we developed a common Consortium Data Model in partnership with domain experts to standardize the capture of participant-, family-, phenotype- and assay-level metadata, with a particular emphasis on using a modular architecture to support linking of multiple data versions from multiple omic technologies to a single individual and attribution of a genetic finding to the specific technology used for its initial discovery. Adoption of the GREGoR Data Model has enabled continued generation and public release of a harmonized, analysis-ready Consortium Dataset. The most recent release includes phenotypic, family and multi-omic data from 12,292 participants in 5,029 families. Other rare disease data sharing efforts are beginning to adopt this data model which will facilitate cross consortium analyses and empower rare disease research. This work demonstrates that a collaborative, flexible, and scalable data model can enable large-scale rare disease research, facilitate cross-center data harmonization, and enable data interoperability.

genomics↗

bioRxiv2026

Digital Atlases to Unlock the Potential of Brain Biorepository Tissues for Interdisciplinary Research

Human brain tissue preserved in biorepositories is foundational for the structural, cellular, and biomolecular research necessary for a mechanistic understanding of neurological diseases. Realizing the research potential of these valuable resources requires well-characterized research-relevant tissue that can be efficiently identified by investigators and incorporated into the conceptual and computational frameworks of interdisciplinary research. Several large-scale efforts to improve research reliability and reproducibility have sought to characterize and annotate the processes by which these samples are collected, yet limited progress has been made on standardizing spatial information for these samples. Biorepositories systematically collect brain tissue according to a brain sampling protocol (BSP) that differs between institutions, yet explicit spatial information regarding the samples may not be documented in standard operating procedures (SOPs). The amount of anatomical location details available to investigators are inconsistent across biorepositories and typically lack sufficient anatomical precision to ensure correspondence with samples from other biorepositories or research relevant brain regions specified by neuroimaging, functional, or disease-susceptibility criteria. Here, we introduce a pipeline for developing a Spatial Atlas for Mapping Protocol Locations of Ex vivo Samples (SAMPLES), which uses a neuroimaging framework to create a 3D representation of a BSP through a metrically precise digital instantiation of the procedures for brain extraction, segmentation, slicing, and sampling on a modern digital brain template. SAMPLES incorporates modern neuroinformatics conventions to create explicit 3D labels of BSP-defined samples that can be interactively visualized with freely available neuroimaging software. We illustrate the pipeline by developing an atlas for the protocol from the University of Washington BioRepository and Integrated Neuropathology laboratory (UW BRaIN SAMPLES). By providing an explicit, computable reference, SAMPLES atlases can support the efficient identification, referencing, and utilization of postmortem samples for interdisciplinary research. These capabilities enable biorepository workflows, data harmonization across biorepositories, and integration with antemortem neuroimaging.

neuroscience↗

bioRxiv2025

CausalGRN: deciphering causal gene regulatory networks from single-cell CRISPR screens

Large-scale single-cell CRISPR screens with single-cell RNA-seq (scRNA-seq) readouts provide critical data to map causal gene regulatory networks (GRNs). However, translating the complex scRNA-seq outputs into reliable causal insights remains a major analytical challenge. Here we present CausalGRN, a scalable computational framework that infers causal GRNs and predicts cellular responses to unseen perturbations. CausalGRN first mitigates pervasive spurious partial correlations in sparse scRNA-seq data through a novel adaptive thresholding correction, enabling robust inference of an undirected graph. It then orients this graph using observed perturbation outcomes. The resulting directed GRN can be used to predict the downstream effects of novel perturbations via network propagation. Across both simulations and diverse experimental datasets, CausalGRN substantially outperforms existing approaches in network reconstruction accuracy and in predicting the effects of unseen perturbations, providing a principled bridge from perturbation data to causal gene regulation.

bioinformatics↗

bioRxiv2025

Accounting for Spatial Correlation in Graphical Analysis of SpatialTranscriptomics Data

Co-expression analysis is key for understanding disease mechanisms and gene regulatory and functional relationships. In spatial transcriptomics, estimating gene correlation is challenging due to correlation among cells, which can lead to spurious associations that obscure true biological associations. To address this, we propose SpaceDecorr, a method that adjusts gene expression for technical artifacts and spatial dependencies by modeling each gene independently using a Negative Binomial Generalized Additive Model (NB-GAM) with spatial splines. Co-expression is then estimated from the Pearson residuals, yielding decorrelated expression values suitable for downstream analysis. This method targets cell-intrinsic coordination, rather than clustering genes by shared spatial patterns, and supports multi-sample analysis trough independent per-sample adjustment. Across simulations and real datasets, it consistently reduces false-positive correlations and improves the functional coherence of co-expression modules.

genomics↗

bioRxiv2024

Differential Expression Analysis for Spatially Correlated Data

Differential expression is a key application of imaging spatial transcriptomics, moving analysis beyond cell type localization to examining cell state responses to microenvironments. However, spatial data poses new challenges to differential expression: segmentation errors cause bias in fold-change estimates, and correlation among neighboring cells leads standard models to inflate statistical significance. We find that ignoring these issues can result in considerable false discoveries that greatly outnumber true findings. We present a suite of solutions to these fundamental challenges, and implement them in the R package smiDE. spatial transcriptomics, differential expression, segmentation error mitigation, spatial correlation, spatial random effects model

genomics↗

bioRxiv2024

Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling

Alzheimers disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta (A{beta}), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A{beta} biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimers process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A{beta} detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A{beta} status, and offer key insights into dynamic functional connectivity analysis in AD.

neuroscience↗

bioRxiv2022

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotypes ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimers disease.

genomics↗

bioRxiv2021

Subtype transcriptomic profiling of myeloid cells in Alzheimer Disease brain illustrates the diversity in active microglia phenotypes

Microglia contribute to Alzheimers Disease (AD) progression and are candidate therapeutic targets. Human microglia exhibit an array of transcriptional phenotypes implying that accurate manipulation of microglial function will require clarity of their molecular states and context dependent regulation. To increase the number of microglia analyzed per subject we employed fluorescence activated nuclei sorting prior to single-nucleus RNA-seq on human prefrontal cortices. We observed microglia phenotypes previously unrecognized in human brain gene expression studies and mapped their transcriptomic relationships by trajectory inference. Three clusters were enriched for endolysosomal pathways, one of which showed differential expression of AD GWAS genes in addition to genes implicated in nucleic acid detection and interferon signaling. Analysis of the "homeostatic" microglia cluster revealed a uniquely AD subcluster. Our study demonstrates the value of deeply profiling microglia to explore the biological implications of microglia transcriptomic diversity.

neuroscience↗

bioRxiv2021

Genetic effect modification of cis-acting C-reactive protein variants in cardiometabolic disease status

Mendelian randomization (MR) studies carried out among patients with a particular health condition should establish the genetic instrument influences the exposure in that subgroup, however this is normally investigated in the general population. Here, we investigated whether the genetic associations of four cis-acting C-reactive protein (CRP) variants differed between participants with and without three cardiometabolic conditions: obesity, type 2 diabetes, and cardiovascular disease. Associations of cis-genetic variants with CRP differed between obese and non-obese individuals. A multivariable analysis suggested strong independent associations of the gene-by-body mass index (BMI) interaction on CRP (P<1.18x10-8 for the CRP variants). Applying MR, we observed strong causal effect of BMI on CRP (P=2.14x10-65). In summary, our study indicates that genetic associations with CRP differ across disease sub-groups, with evidence to suggest that BMI is an effect modifier. MR studies of disease progression should report on the genetic instrument-exposure association in the disease subgroup under investigation.

genetics↗

bioRxiv2021

Cortical network structure mediates response to stimulation: an optogenetic study in non-human primates

As aberrant network-level functional connectivity underlies a variety of neural disorders, the ability to induce targeted functional reorganization would be a profound development towards therapies for neural disorders. Brain stimulation has been shown to alter large-scale network-wide functional connectivity, but the mapping from stimulation to the modification is unclear. Here, we leverage advances in neural interfaces, interpretable machine learning, and graph theory to arrive at a model which accurately predicts stimulation-induced network-wide functional reorganization. The model jointly considers the stimulation protocol and the cortical network structure, departing from the standard approach which only considers the stimulation protocol. We validate our approach in the primary sensorimotor cortex of non-human primates using paired optogenetic stimulation through a large-scale optogenetic interface. We observe that the stimulation protocol only predicts a small portion of the induced functional connectivity changes while the network structure predicts much more, indicating that the underlying network is the primary mediator of the response to stimulation. We extract the relationships linking the stimulation and network characteristics to the functional connectivity changes and observe that the mappings diverge over frequency bands and successive stimulations. Finally, we uncover shared processes governing real-time and longer-term effects of stimulation. Our framework represents a paradigm shift for targeted neural stimulation and can be used to interrogate, improve, and develop stimulation-based interventions for neural disorders. TeaserBrain stimulation rewires the brain, but the pre-existing network structure of the brain controls the rewiring.

neuroscience↗

bioRxiv2021

REHE: Fast Variance Components Estimation for Linear Mixed Models

AO_SCPLOWBSTRACTC_SCPLOWLinear mixed models are widely used in ecological and biological applications, especially in genetic studies. Reliable estimation of variance components is crucial for using linear mixed models. However, standard methods, such as the restricted maximum likelihood (REML), are computationally inefficient and may be unstable with small samples. Other commonly used methods, such as the Haseman-Elston (HE) regression, may yield negative estimates of variances. Utilizing regularized estimation strategies, we propose the restricted Haseman-Elston (REHE) regression and REHE with resampling (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide non-negative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.

genetics↗

bioRxiv2019

A comparative study of topology-based pathway enrichment analysis methods

BackgroundPathway enrichment analysis is extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples.\n\nResultsThe findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment.\n\nConclusionThe analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

systems biology↗

bioRxiv2019

The GMD-biplot and its application to microbiome data

Exploratory analysis of human microbiome data is often based on dimension-reduced graphical displays derived from similarities based on non-Euclidean distances, such as UniFrac or Bray-Curtis. However, a display of this type, often referred to as the principal coordinate analysis (PCoA) plot, does not reveal which taxa are related to the observed clustering because the configuration of samples is not based on a coordinate system in which both the samples and variables can be represented. The reason is that the PCoA plot is based on the eigen-decomposition of a similarity matrix and not the singular value decomposition (SVD) of the sample-by-abundance matrix. We propose a novel biplot that is based on an extension of the SVD, called the generalized matrix decomposition (GMD), which involves an arbitrary matrix of similarities and the original matrix of variable measures, such as taxon abundances. As in a traditional biplot, points represent the samples and arrows represent the variables. The proposed GMD-biplot is illustrated by analyzing multiple real and simulated data sets which demonstrate that the GMD-biplot provides improved clustering capability and a more meaningful relationship between the arrows and the points.

bioinformatics↗