Biology subjects

Rincon de la Rosa, L.

Publications and source records attributed to Rincon de la Rosa, L..

3 recordsLinked to original sources

bioRxiv2026

Morphologic intratumoral heterogeneity from routine whole-slide histopathology is prognostic for survival in primary central nervous system lymphoma: development in the LOC Network and international external validation

Background: Clinical scores incompletely capture outcomes in primary central nervous system lymphoma (PCNSL). We quantified morphologic heterogeneity in pretreatment hematoxylin and eosin (H\&E) whole slides. Patients and methods: Three independent cohorts of immunocompetent, HIV- and EBV-negative patients treated recently were analyzed: LOC 2023 (122 slides), phase III BLOCAGE-01 (245 slides; NCT02313389), and external Barcelona (BCN; 41 slides). UNI embeddings, prototype learning, spatial metrics, and elastic-net Cox regression defined ITH-C. Results: Models achieved bootstrap-corrected concordance of 0.797--0.834. Age-, sex-, and KPS-adjusted ITH-C HRs were 1.29 (95\% CI 1.01--1.64), 1.27 (1.07--1.51), and 2.13 (1.35--3.37), respectively. Adding ITH-C increased MSKCC C-index from 0.671 to 0.717, 0.560 to 0.593, and 0.588 to 0.706. Spatial transcriptomics linked ITH-C to immune programs. Conclusions: Routine H\&E encodes prognostic spatial heterogeneity in PCNSL. ITH-C complements clinical scores, supporting prospective risk stratification.

bioinformatics↗

bioRxiv2026

VelOT: kinetic-free RNA velocity inference via optimal transport, flow-field smoothing, and VAMP coarse-graining of cellular dynamics

Inferring cellular dynamics from snapshot single-cell RNA sequencing remains difficult when spliced and unspliced counts are sparse or unreliable. We present VelOT, a kinetic-free RNA velocity framework that formulates dynamics as local optimal transport on the gene-expression manifold. VelOT orders cells by diffusion pseudotime, constructs overlapping spatial-temporal windows, estimates displacement vectors with entropy-regularized transport, and smooths them with a lightweight neural flow field. A downstream VAMP-based MetaFlow module learns soft meta-states and a directed PAGA-like graph, identifying initial, terminal, branching, and cycling regimes with committor probabilities. Across four real benchmarks and three synthetic topologies, VelOT outperforms scVelo, DeepVelo, and FluxMatching in cross-boundary directionality and intra-cluster coherence while remaining computationally efficient. In adult oligodendroglioma scRNA-seq, VelOT recovers stem-like to astrocyte-like and oligodendrocyte-like differentiation axes without kinetic inputs. VelOT reframes RNA velocity within scRNA-seq as a geometry and transport problem that does not require kinetic modeling.

bioinformatics↗

bioRxiv2026

h5adify: neuro-symbolic metadata harmonizationenables scalable AnnData integration with locallarge language models

BackgroundThe rapid growth of public single-cell and spatial transcriptomics repositories has shifted the main bottleneck for atlas-scale integration from data generation to metadata heterogeneity. Even when datasets are released in the AnnData H5AD format, inconsistent column naming, partial annotations, and mixed gene identifier conventions frequently prevent reproducible merging, downstream benchmarking, and reuse in foundation model training. Automated approaches that resolve semantic inconsistency while preserving biological validity are therefore essential for scalable data reuse. ResultsWe present h5adify, a neuro-symbolic toolkit that combines deterministic biological inference with locally deployed large language models to transform heterogeneous AnnData objects into schema-normalized, integration-ready representations. The framework performs metadata field discovery, gene identifier harmonization, optional paper-aware extraction, and consensus resolution with explicit uncertainty logging. Benchmarking four open-weight model families deployed through Ollama (Gemma, Llama, Mistral, and Qwen) demonstrates that small local models achieve high semantic accuracy in metadata resolution with low hallucination rates and modest computational requirements. In controlled simulations introducing annotation noise into single-cell and Visium-like datasets, harmonization improves integration benchmarking and reduces spurious batch effects. Application to sex-annotated glioblastoma datasets recovers biologically coherent microenvironmental patterns and cell type-specific genomic differences not explained by differential expression alone. ConclusionsTogether, h5adify provides a reproducible framework for evaluating LLM-assisted biocuration and enables scalable, privacy-preserving metadata harmonization for modern single-cell atlases and foundation model pipelines. These results demonstrate that modular neuro-symbolic integration of deterministic biological inference and small local language models can effectively resolve semantic heterogeneity while remaining computationally accessible.

bioinformatics↗