Biology subjects

Nathoo, F.

Publications and source records attributed to Nathoo, F..

6 recordsLinked to original sources

bioRxiv2026

Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes

Gaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. Covariance non-stationarity has long been recognized in spatial statistics as an important feature of spatial data, yet it has received little attention in spatial transcriptomics. We show that this omission is consequential: covariance non-stationarity is substantially evident across spatial transcriptomic datasets and alters the characterization of spatially varying genes. While typically ignored, non-stationarity of spatial covariance in gene expression may correspond to tissue heterogeneity or cell aggregates. Across 12 Visium datasets, we use approximate Bayes factors from R-INLA to compare stationary and non-stationary Mat'ern covariance functions. Evidence for covariance non-stationarity appears in 3% to 50% of genes across tissue samples. We find that gene sets associated with immune, cytokine, and other effector functions are enriched among genes favoring non-stationary spatial covariance. Covariance stationarity is therefore not a benign technical simplification in spatial transcriptomics; it is frequently violated, the violation is biologically structured, and it changes the definition and classification of spatially varying genes.

bioinformatics↗

bioRxiv2026

Neural manifold connectomics reveals multiregime functional connectivity

Neural activity is organized in low-dimensional structure, yet functional connectivity in fMRI typically represents each brain parcel by a single voxel-averaged time series. This scalar representation makes whole-brain connectivity tractable but discards potentially informative dimensions of within-parcel BOLD activity. Here, we represent each parcel by a low-dimensional temporal subspace derived from its principal-component time series and use the RV coefficient to quantify connectivity between regional subspaces. Across Human Connectome Project resting-state and working-memory data, progressively expanding these subspaces reveals reproducible connectivity regimes with distinct network and identifiability profiles. At rest, connectivity constructed from the first principal component identifies individuals more strongly than either voxel-averaged functional connectivity or higher-dimensional subspace representations. During working memory, identifiability instead peaks after secondary components are included, indicating that the distribution of individual-specific information across the regional PCA spectrum depends on cognitive state. These patterns replicate across independent samples and remain robust across multiple parcellation resolutions. Together, our findings show that within-parcel BOLD structure contains identity- and statedependent information that is obscured by scalar regional summaries. Functional connectivity may therefore be better understood as a family of related connectomes indexed by the regional subspace retained, providing a general framework for mapping interactions between low-dimensional neural representations.

neuroscience↗

bioRxiv2026

SPaCeD: Spatial Point Process Distances for Pairing the Heavy and Light Chains of B Cell Receptors from Spatial BCR-seq

B cells mediate anti-tumor immunity through B cell receptors (BCRs) composed of paired heavy and light chains. Leveraging spatial transcriptomics, we introduce SPaCeD, a framework that infers heavy-light chain pairs by integrating expression matrices with spatial distances between point patterns derived via optimal transport. Applied to ovarian and breast cancer datasets and simulation scenarios, SPaCeD improves pairing accuracy and stability compared with existing methodology (Repair), particularly for pairs with lower spatial expression.

bioinformatics↗

bioRxiv2025

A-LAVA: Detecting impact of germline variants on metabolic pathways in cancer genomes

The metabolic landscape of cancer has been widely studied, especially in the context of somatic mutations. However, the impact of inherited germline variants upon the metabolic genes interaction still remains unexplored. In this work, we present a computational pipeline named A-LAVA for the detection and analysis of germline variants that affect metabolic pathways in cancer. Our pipeline enables analysis at three different levels: SNP, gene, and pathway-based analysis. The first steps consist of detecting statistically significant SNPs through standardized GWAS pipelines and, subsequently, genes associated with metabolic traits through gene-level analysis. Then, A-LAVA performs gene set analysis (GSA) to further explore the effect of detected associations on metabolic pathways. This analysis is done through a statistical model that newly corrects for the confounding effects arising from overlapping gene sets, in addition to other corrections performed by the current best practices. Our analysis conducted on TCGA data shows that SNP and gene-level results identified key associations and that A-LAVAs GSA approach improved the overall accuracy both on synthetic and real data by correctly correcting for overlapping genes, refining significance thresholds, and reducing false positives, thus leading to more reliable metabolic pathway rankings and a more robust framework for gene set analysis. CCS CONCEPTSO_LIApplied computing [->]Bioinformatics; Metabolomics / metabonomics. C_LI

bioinformatics↗

bioRxiv2025

Prognostically favorable immune responses to ovarian cancer are distinguished by self-reactive intra-epithelial plasma cells

Tumor-infiltrating B cells (TIL-Bs) are strongly associated with patient survival; however, the underlying mechanisms are poorly understood. Using integrated single-cell and spatial biology approaches, we defined at clonal resolution the molecular phenotypes, tumor reactivity patterns, and microenvironmental locations of TIL-Bs in high-grade serous ovarian cancer (HGSC). Prognostic benefit was associated with a TIL-B-rich tumor microenvironment with marked infiltration of malignant epithelium by plasma cells (PCs). PCs spanned five molecular phenotypes; exhibited high rates of somatic hypermutation and clonal expansion; and expressed predominantly IgG1 antibodies recognizing broadly expressed nuclear, cytoplasmic, and cell surface self-antigens. Many PC-derived antibodies were polyreactive. Self- and poly-reactive TIL-Bs penetrated tumor epithelium and stroma and expressed interferon-stimulated genes, indicating strong in situ activation. The self- and poly-reactive nature of TIL-B responses, reminiscent of autoimmune disease, may provide a means for the immune system to combat tumor heterogeneity and could potentially be harnessed for more effective immunotherapy.

cancer biology↗

bioRxiv2016

Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation

Both genetic variants and brain region abnormalities are recognized to play a role in cognitive decline. We explore the association between singlenucleotide polymorphisms (SNPs) in linkage regions for Alzheimers disease and rates of decline in brain structure using data from the Alzheimers Disease Neuroimaging Initiative (ADNI).\n\nIn an initial discovery stage, we assessed the presence of linear association between the minor allele counts of 75,845 SNPs in the Alzgene linkage regions and predicted rates of change in structural MRI measurements for 56 brain regions using an RV test. In a second, refinement stage, we reduced the number of SNPs using a bootstrap-enhanced sparse canonical correlation analysis (SCCA) with a fixed tuning parameter. Each SNP was assigned an importance measure proportional to the number of times it was estimated to have a nonzero coefficient in repeated re-sampling from the ADNI-1 sample. We created refined lists of SNPs based on importance probabilities greater than 50% and 90%, respectively. In a third, validation stage, we assessed the multivariate association between these refined lists of SNPs and the rates of structural change in the independent ADNI-2 study dataset.\n\nThere was strong statistical evidence for linear association between the SNPs in the Alzgene linkage regions and the 56 imaging phenotypes in both the ADNI-1 and ADNI-2 samples (p < 0.0001). The bootstrap-enhanced SCCA identified 1,694 priority SNPs with importance probabilities > 50% and 22 SNPs with importance probabilities > 90%. The 1,694 prioritized SNPs in the ADNI-1 data were associated with imaging phenotypes in the ADNI-2 data (p = 0.0021).\n\nThis manuscript presents an analysis that addresses challenges in current imaging genetics studies such as biased sampling designs and highdimensional data with low-signal. Genes corresponding to priority SNPs having the highest contribution in the validation data have previously been implicated or hypothesized to be implicated in AD, including GCLC, IDE, and STAMBP1andFAS. We hypothesize that the effect sizes of the 1,694 SNPs in the priority set are likely small, but further investigation within this set may advance understanding of the missing heritability in late-onset Alzheimers disease. Multivariate analysis; Linkage regions; Imaging genetics; Endophenotypes; Inverse probability weighting; Variable importance probabilities

neuroscience↗