dimensionality reduction

Dimensionality Reduction for Cyclic Compositional Data

clrcycle is a dimensionality reduction method for cyclic data that captures phase and magnitude in biological systems, showing effectiveness in circadian and cell-cycle analysis.

Whole-brain normative modeling across multiple functional connectivity metrics in MEG

This paper introduces M/EEG-FUNCOIN, a normative modeling approach for whole-brain functional connectivity in MEG data, showing improved characterization of age and sex-related patterns and detection of connectivity deviations linked to cognitive ability.

Joint reconstruction of multi-contrast relaxation-diffusion imaging

This paper introduces DIFFUSE, a framework for joint k-q-TE image reconstruction in diffusion MRI, showing improved error reduction and multi-contrast reconstruction.

ALHENA, an H&E encoder distilled from transcriptomic foundation models for multi-scale slide analysis

ALHENA improves transcriptomic signature prediction and cell-type separability using H&E images alone, without requiring spatial transcriptomics data.

Cellular and mitochondrial correlates of regional glucose metabolism in the human cortex

The paper investigates the cellular and mitochondrial factors influencing regional glucose metabolism in the human cortex using multimodal data integration.

Disentangling evoked and induced activity from single-trial neurophysiological time series

This paper introduces EOIS, a framework to separate evoked, ongoing, and induced neural activity from single-trial data, enabling component-resolved analysis and applications in biomarker refinement and neuromodulation.

Streamline CNV - A Handy Nextflow Pipeline for Chromosomal Copy Number Variation Analysis

Streamline CNV is a Nextflow pipeline for CNV analysis that simplifies processing, visualization, and interpretation of copy number data across various sequencing types and species.

Aberrant cortical reorganization and astrogliosis underlie memory deficit post-traumatic brain injury: a longitudinal multiscale multimodal study

This study uses multiscale multimodal data integration in a mouse model of traumatic brain injury to identify biomarkers of memory deficits and network dedifferentiation.

Vascularized Organoids Emerge from a Heart-Foregut-like Intermediate Recapitulating Early Human Development

This study shows that heart/foregut-like structures form from human pluripotent stem cells, developing into vascularized cardiac organoids with multiple cell types.

SpacePath enables pathway and molecular-family analysis beyond annotation ambiguity in spatial metabolomics and lipidomics

SpacePath improves spatial metabolomics analysis by handling molecular ambiguity and identifying novel lipid families in spatial datasets.

Cell-level integration of gene expression and B cell receptor data captures complementary B cell information

The paper integrates gene expression and B cell receptor data to capture complementary B cell information, revealing substructure supported by both transcriptional and receptor features.

Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior

This paper introduces Rotational Component Analysis (RCA) to detect low-dimensional rotational dynamics in neural data and shows these dynamics constrain cue-evoked behavior and learning across species.

T2G: fast and accurate transcriptome to genome BAM liftover for short and long read RNA sequencing

T2G converts transcriptome-aligned BAM files to genome coordinates, improving specificity and splice junction accuracy across RNA sequencing technologies.

CPMsi: a data-driven Computational Pipeline for fast, large-scale Mass Spectrometry Imaging analysis

CPMsi is a computational pipeline that efficiently processes and integrates large-scale MSI data, enabling faster analysis and discovery of molecular patterns across tissue sections.

The Role of Environmental Information and the Limitations of Predictability with Transformer-based Time-Series Models for complex Astrocyte Trajectories

This study uses a Transformer-based model to predict astrocyte migration during mouse brain development, finding that environmental information and multimodal modeling improve prediction accuracy.

CnidoSite: A Multi-Omics Database for Cnidarians

CnidoSite provides a centralized multi-omics database for cnidarians, integrating genomic, transcriptomic, proteomic, and phenotypic data to study thermal tolerance and environmental adaptation.

A community benchmark enables high-resolution, cross-lab quantification of mouse social behavior

The paper introduces a benchmark dataset and tool for cross-lab quantification of mouse social behavior, achieving high-resolution and reproducible classification.

Detecting movement preparation with HYDRE: a hybrid model of temporal and spectral brain signals

This study introduces HYDRE, a hybrid model combining temporal, spectral, and Riemannian features to decode motor preparation from brain signals, achieving above-chance decoding up to 1400ms before movement.

U-Chrom: a unified framework for single-cell and spatial 3D genome data

U-Chrom provides a unified framework for analyzing single-cell and spatial 3D genome data, showing consistent cell-type differences across assays.

StainAI-Web: An Interactive Platform for Whole-Slide Microglial Morphology Analysis

StainAI-Web provides an interactive platform for automated microglial morphology analysis of whole-slide histology images, enabling efficient and reproducible quantification and visualization.

MorphCell learns interpretable shape representations of 3D cellular morphology

MorphCell learns interpretable 3D cellular morphology representations from point clouds, transferring across imaging modalities and biological contexts with improved classification performance.

scOmnom: scalable end-to-end single-cell RNA-seq analysis with auditable resolution selection

scOmnom provides a scalable, semi-automated workflow for single-cell RNA-seq analysis, enabling auditable clustering-resolution selection and annotation refinement across large datasets.

NeuroPilot: Longitudinal intravital neural imaging across scales and modalities through an agentic platform

NeuroPilot converts heterogeneous intravital neural recordings into stable population activity maps, enabling behavioral decoding and structured neural dynamics analysis across behavioral states.

Variable sparsity non-negative matrix and tensor factorization algorithms and their applications in cancer genomics

This paper introduces variable-sparsity matrix and tensor factorization algorithms for cancer genomics to improve biological interpretability and modeling of cancer subtypes and pathways.

Stain Normalisation Does Not Consistently Improve Matched-Location Retrieval with Pathology Foundation Models

This study tests if stain normalization improves retrieval with pathology foundation models, finding no consistent benefit and potential harm from Macenko normalization.

Latent-Factor Stability Is Not Sufficient: Evaluating SABLE-MO in Simulated and Cancer Multi-Omics Data

The paper evaluates SABLE-MO's performance in identifying shared factors in multi-omics data, finding that stricter retention reduces errors but does not eliminate them.

Fast Generalized Longitudinal Functional Principal Components Analysis with Applications to Population-Level Neuronal Dynamics

This paper introduces a fast generalized LFPCA method for analyzing longitudinal functional data, showing it is faster and scalable while applied to neuronal firing rate data under DBS.

Scale-resolved correlation functions unify spatial omics across platforms and resolutions

SCOTE quantifies spatial relationships across all length scales in spatial omics data, revealing biological insights while distinguishing platform artifacts.

Enhanced stroke volume and divergence of the human left ventricle

The paper compares left ventricular phenotypes across humans, chimpanzees, and macaques to identify structural and functional traits associated with higher stroke volume in humans.

The trade-off between fractal morphology and function of Drosophila neurons

The paper analyzes the fractal morphology of Drosophila neurons and finds that fractal dimensions correlate with functional classes, showing specialization in morphology for distinct functions.