Single-cell and spatial transcriptomics

An automated platform for spatial functional modeling and fingerprint analysis of tissue molecular landscapes

The paper introduces SFinx, a framework for spatial functional analysis of transcriptomic data, revealing spatially varying pathway interactions in lupus nephritis.

Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models

This paper benchmarks differential expression classification in single-cell perturbation models, finding that simple baselines often outperform deep learning models.

RETROFIT: REFERENCE-FREE DECONVOLUTION OF CELL-TYPE MIXTURES IN SPATIAL TRANSCRIPTOMICS

RETROFIT deconvolves cell-type mixtures in spatial transcriptomics without requiring references, outperforming existing methods and matching reference-based approaches when references are imperfect.

An Integrated Multi-omics Single Cell Atlas of the Human RPE and Choroid

The paper constructs a multi-omics single-cell atlas of human RPE and choroid, identifying cell types, aging patterns, and genetic risk factors for AMD.

GatorPrism: Prototype-Conditioned Routing across Coalition Graph Experts for Spatial Multi-Omics Integration

GatorPrism integrates spatial multi-omics data by balancing cross-omics consensus with modality-specific signals, achieving strong performance across clustering metrics and revealing tissue structure organization.

Deconvolution-derived cell-type expression targets for personal genome sequence-to-expression prediction

The paper uses deconvolution of bulk RNA-seq to generate cell-type-specific targets for predicting gene expression from personal genome sequences, finding that sequence-derived models outperform genotype-feature baselines.

Statistical tests for bivariate spatial association across multi-omics data with disjoint coordinates

Autocorrelation-aware bivariate Moran's I, bivariate Gaussian process tests and generalized additive models (R package sbivar) test cross-modality colocalisation on disjoint coordinates; many existing permutation tests inflate false positives.

scSAID: A Comprehensive Cross-Species Single-Cell Skin Atlas Reveals Species-Specific Responses to Psoriasis

scSAID provides a cross-species single-cell skin atlas to study psoriasis, revealing species-specific molecular signatures and identifying a novel drug target.

Morphomechanically-Informed Spatial Curvature Sequencing in Prostate Cancer

This paper introduces CurvSeq and CurvSee to analyze prostate cancer morphology and molecular profiles, identifying cancer-associated genes and morphomechanical niches linked to progression.

cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution

cellGeometry deconvolutes bulk RNA-Seq using geometric methods, offering faster and more accurate results than existing approaches.

A multimodal interrogation of Broca's area in the pediatric and adult human brain

The paper provides a high-resolution molecular and cellular analysis of Broca's area and motor cortex across developmental stages, identifying distinct cellular signatures and vulnerabilities to neurodevelopmental disorders.

Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features

This study constructs a pan-cancer single-cell multi-omic atlas to uncover gene regulatory principles in cancer cell states and environmental features across diverse cancer types.

Bacterial surface display enables lysis-independent joint host-pathogen single-cell profiling

This paper introduces a lysis-independent method for joint host-pathogen single-cell profiling using bacterial surface display, enabling analysis of both host and pathogen features in infected cells.

SpatialJEPA: JEPA-inspired graph-context distillation for spatially aware multiomics integration

SpatialJEPA transfers spatial context from spatial multiomics data to dissociated non-spatial data through graph-context distillation, improving alignment and recovery of spatially organized programs in mouse brain multiomics.

Learning Minimal Gene Programs for Disease-Aligned Representations

This paper introduces SLMC to select minimal gene programs for disease-aligned representations in single-cell RNA-seq data, finding greedy methods outperform LASSO with small gene budgets.

IntraTalker - Modelling Intracellular Signalling in1 Cellular Crosstalk

IntraTalker+CrossTalkeR models intracellular and intercellular signaling in single-cell data to uncover cell-state changes and novel signaling roles.

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomics

This paper introduces Phenoverse, a deep learning framework that learns disease state representations from single-cell transcriptomic data, enabling disease prediction and severity assessment using binary labels.

Quantitative Description of C. elegans mRNA Landscapes From High Coverage Single Cell Transcriptomes

The paper develops a method to analyze high-coverage single-cell RNA-seq data to identify gene expression patterns in C. elegans cells without dimensional reduction.

CITE-seq of murine bone marrow plasmacytoid dendritic cells and haematopoietic progenitor cells in sepsis

The paper provides CITE-seq data of bone marrow cells from septic and sham mice to study transcriptional and surface marker changes in sepsis.

Easy-Multiome enables joint profiling of gene expression and chromatin accessibility in single cells

Easy-Multiome enables joint profiling of gene expression and chromatin accessibility in plant single cells with high efficiency and minimal workflow changes.

Organism-scale annotation with Pan-human Azimuth

Pan-human Azimuth maps diverse human cells into a unified hierarchical typology, enabling cross-tissue comparisons and spatial transcriptomic annotation.

Spatial Total RNA Sequencing of Formalin-Fixed Paraffin Embedded Tissues by spRandom-seq

This paper introduces spRandom-seq, a spatial RNA sequencing method for FFPE tissues, which captures both host and microbial RNAs with higher efficiency than oligo(dT)-based methods.

Global StationaryOT: Trajectory inference for aging time courses of single-cell snapshots

This paper introduces a method to infer cell trajectories from aging time courses, improving accuracy in noisy data by leveraging temporal information.

scRepresenter: a workflow for computing, integrating and benchmarking cellular representations in single-cell transcriptomics

scRepresenter provides a workflow to compute, integrate, and benchmark cellular representations from scRNA-seq data using different methods for complex disease analysis.

multiScaleAC: Cell-Cell interaction with Moran's I as a function of kernel bandwidth

The paper introduces multiScaleAC, a method to analyze cell-cell interactions across spatial scales using a modified Moran's I with varying kernel bandwidths, showing improved statistical power and reduced bias.

FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient Representations.

FloREN improves patient-level sample stratification and biomarker discovery by modeling single-cell data as an interpretable graph transformer network for immune regulatory analysis.

Deep learning representations of human Immune Health for precision immunology

The paper develops MAESTRO, a self-supervised deep learning framework to transform immune profiles into embeddings capturing immune health, enabling clinical prediction and precision immunotherapy.

Constructing a full-length single-nucleus transcriptomic atlas of the obese mouse by UURNA-seq

This paper introduces UURNA-seq, a high-throughput single-nucleus RNA sequencing method, and constructs a comprehensive obesity atlas revealing transcriptional changes and metabolic pathway dysregulation.

Flexible and high-throughput simultaneous profiling of gene expression and chromatin accessibility in single cells

The paper introduces easySHARE-seq, a method for simultaneous single-cell ATAC-seq and RNA-seq, showing improved transcript recovery and scalability in murine liver nuclei.

A Label-Free Multi-Metric Pipeline for Benchmarking Single-Cell RNA-Sequencing Clustering and Testing the Reproducibility of Cell-Type Heterogeneity

The paper introduces a label-free pipeline to benchmark single-cell RNA-seq clustering and assesses the reproducibility of cell-type heterogeneity across different embeddings and resampling methods.