Biology subjects

Mattila, C.

Publications and source records attributed to Mattila, C..

4 recordsLinked to original sources

bioRxiv2026

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest,XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.

bioinformatics↗

bioRxiv2026

FusedFCR: A Fused Forward Continuation-Ratio model for marker selection along cell-fate trajectories

Time-course single-cell RNA sequencing (scRNA-seq) data collected across ordered stages provide population-level snapshots of differentiation, disease progression, and aging. Supervised pseudotime methods use observed stage labels to reconstruct continuous progression but generally do not identify marker genes associated with changes from one stage to the next. Unsupervised pseudotime-based marker selection methods infer latent trajectories directly from expression data and identify trajectory-associated genes, but do not explicitly link these associations to the observed stages. We propose FusedFCR, a regularized forward continuation-ratio model that represents cellular progression through a sequence of conditional transitions across ordered stages. FusedFCR combines a lasso penalty for gene selection with a fusion penalty that encourages similar effects across adjacent transitions while allowing transient and direction-changing associations. The resulting transition-specific coefficients support interpretable gene selection and a continuous pseudotime-like projection anchored to the observed developmental stages. In simulations, FusedFCR accurately recovers gene-effect trajectories and improved predictive performance relative to alternative methods. Applied to one mouse and three human datasets (mouse pancreatic beta-cells, human extravillous trophoblast, human induced pluripotent stem cell derived astrocytes, and human endometrial cells during the secretory phase), FusedFCR identifies biologically interpretable genes associated with distinct developmental transitions. Gene set enrichment analysis further reveals stage-specific pathway activity consistent with known developmental biology, while held-out stage-classification accuracy was competitive or superior across both datasets. Together, these results show that FusedFCR complements pseudotemporal ordering by identifying which molecular programs change and when those changes emerge along the developmental trajectory. An accompanying R package is available on GitHub.

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bioRxiv2025

CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data

High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which ignore gene-gene dependencies and nonlinear effects, while multivariate machine-learning (ML) methods often lack principled feature selection and uncertainty quantification. The Bayesian kernel machine regression (BKMR) framework offers an appealing alternative because it (a) captures non-linear gene-outcome relationships and higher-order interactions, and (b) enables automatic relevance determination (ARD) through sparsity-inducing priors. However, we show that the traditional latent Gaussian process (GP) formulation of BKMR is inadequate for discrete outcomes (e.g., cell-type labels), leading to biased inference and unstable variable selection. We propose a copula-based Bayesian kernel machine regression (CBKMR) model that uses outcome-appropriate discrete marginals while a Gaussian copula captures kernel-induced dependence across observations. To ensure scalability to modern single-cell datasets, we further introduce a nearest-neighbor GP-based variant, NNCBKMR, which reduces computational complexity from O(N 3) to nearly linear in N. Simulation studies show that CBKMR more accurately captures nonlinear effects and yields stronger marker-selection performance than BKMR and top ensemble ML methods (e.g., random forests, XGBoost). Applications to multiple scRNA-seq datasets demonstrate that CBKMR identifies concise marker panels that align closely with expert-annotated gene signatures while providingposterior uncertainty for principled decision-making.

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bioRxiv2025

GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics

Spatially structured, overdispersed count data with high-dimensional predictors are increasingly observed across studies from population-level epidemiology to cellular-level spatial omics. Feature selection is critical to identify influential predictors, such as key risk factors or biomarkers. Few Bayesian studies have assessed negative binomial regression (NBR) models with standard variable selection priors, like the mixture spike-and-slab (SS) or continuous horseshoe (HS), but mostly under aspatial settings. Features often form groups; for instance, in population surveys, caloric intake and physical activity may fall under "Diet & Exercise", while cigarette use and smoking laws belong to "Smoking". We propose a flexible NBR model that accommodates spatial autocorrelation and introduces a novel group-structured prior by hybridizing SS and HS shrinkage. The models performance with different priors is evaluated in terms of specificity, precision, and computational cost under challenging scenarios, including "large p, small n" cases. We further apply the model to CDC state-level cancer data, comprising demographic, screening, and behavioral covariates, to identify key drivers and population-level risk factors, and to a melanoma spatial omics dataset for predictive modeling expression of gene. An efficient R package is provided on GitHub.

bioinformatics↗