Single-cell and spatial transcriptomics

CIPHER: An end-to-end framework for designing optimized aggregated spatial transcriptomics experiments

CIPHER optimizes aggregate spatial transcriptomics experiments by jointly designing gene signatures and cell embeddings, improving decoding accuracy and experimental reliability.

ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics

ChatSpatial enables reproducible spatial transcriptomics analysis by using an LLM to select and orchestrate tools with schema enforcement, improving parameter validity and workflow consistency.

SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology

SciCore-Omics is a tri-modal foundation model that integrates histology, spatial transcriptomics, and language to improve spatial biology analysis and interpretation.

UnBlender: validating individual analyses in respiratory bulk RNA-seq cell type deconvolution

UnBlender enables cell type deconvolution in respiratory bulk RNA-seq data and provides routine validation of deconvolution accuracy.

TissueFormer: Extending single-cell foundation models to predict population-level phenotypes

TissueFormer predicts population-level phenotypes from single-cell data, outperforming existing methods in predicting COVID-19 severity and cortical area identity.

SpatioCell: Deep Integration of Histology and Spatial Transcriptomics for Profiling the Cellular Microenvironment at Single-Cell Level

SpatioCell integrates histology and spatial transcriptomics to extract single-cell resolution cell type and expression information from ST data, revealing microenvironment features in triple negative breast cancer.

CellClick: an interactive platform for adjustable and accurate cell type annotation in single-cell and spatial omics data

CellClick is an interactive platform that improves cell type annotation accuracy in single-cell and spatial omics data through automated and user-guided tools.

Information Geometry of Intracellular Compartment Coupling Reveals Transcriptomic State Transitions in Single Cells

The paper introduces CCE, a framework to analyze transcriptomic coupling between intracellular compartments, revealing state transitions in single cells through information-geometric metrics.

Topology-aware reconstruction of cellular state landscapes from microscopy using self-supervised learning

The paper introduces SI-SimCLR, a self-supervised learning framework for reconstructing cellular phenotypic landscapes from microscopy data without segmentation or annotation, revealing substate organization and connections in astrocytes.

Reference-Informed Spatial Domain Detection Using Weak Supervision for Spatial Transcriptomics

This paper introduces GraphScrDom, a contrastive learning model for spatial domain detection in spatial transcriptomics using weak supervision and reference data, showing strong performance across platforms and resolutions.

HyperNiche: Learning Heterophilic Cellular Niches with Hypergraph Neural Networks

HyperNiche improves clustering and biological interpretability of spatial transcriptomics data by modeling higher-order cellular niches with hypergraph neural networks.

RAD: A Read-structure Agnostic Demultiplexer for Single-Cell Long-Read Sequencing and Analysis

RAD is a demultiplexer for single-cell long-read sequencing that improves speed and sensitivity over existing methods while supporting complex read structures.

Multiscale Hyperbolic Embedding Reveals Hierarchical Structure in Complex Biological Systems

The paper introduces MuH-MDS, a hyperbolic embedding method that scales to large datasets and improves hierarchical structure analysis in biological data.

Alignment-free integration of single-nucleus ATAC-seq across species with sPYce

sPYce integrates single-nucleus ATAC-seq data across species using k-mer histograms, revealing conserved and divergent regulatory programs without requiring genome alignments.

Ultra-efficient High Resolution 3D Reconstruction of Spatial Omics Data with Neural Transcriptomic Field

This paper introduces NTF, a deep learning framework for high-resolution 3D reconstruction of spatial omics data, achieving fast and scalable results across multiple species and datasets.

Human Fibroblast-Myeloid cell tissue atlas across lung, synovium, skin and heart

The paper analyzes scRNAseq data from multiple tissues to identify shared and tissue-specific myeloid and stromal cell populations in health and disease.

Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain

The paper integrates spatial transcriptomics and single-nucleus RNA-seq data from the human dACC to define molecular profiles, spatial domains, and functional insights.

CycleVI: Isolating cell cycle variation with an interpretable deep generative model

CycleVI isolates cell cycle variation in scRNA-seq data using a deep generative model, improving the resolution of biological processes and drug response signals.

Decoding Multicellular Communication Motifs from Spatial Transcriptomics with ALARMIST

ALARMIST identifies multicellular communication motifs in spatial transcriptomics data, revealing microenvironmental drivers of tumor progression in lung and brain cancers.

barbieQ: An R software package for analysing barcode count data from clonal tracking experiments

barbieQ is an R package for analysing barcode count data from clonal tracking experiments, providing statistical tests for differential barcode proportion and occurrence.

Monju: Multi-criteria clustering in single-cell omics

Monju performs multi-criteria clustering on single-cell omics data by integrating multiple clustering solutions to assess cluster stability.

Histology-informed spatial domain identification through multi-view graph convolutional networks

This paper introduces STESH, a spatial transcriptomics clustering method that integrates gene expression, spatial location, and histology, achieving superior clustering accuracy across multiple tissue types.

Development positions malignant cellular states but does notexplain their diversification

The paper develops a framework to analyze how malignant cellular states in pancreatic ductal adenocarcinoma relate to normal developmental trajectories, finding that while developmental programs help locate cancer states, they do not explain their diversification.

Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts

This study compares five RNA velocity methods across three datasets to evaluate their performance in predicting cellular states.

CytoSignal Detects Locations and Dynamics of Ligand-Receptor Signaling at Cellular Resolution from Spatial Transcriptomic Data

CytoSignal detects and quantifies ligand-receptor signaling interactions at cellular resolution from spatial transcriptomic data, enabling spatial gradient analysis and temporal dynamics prediction.

From unsupervised clustering to atlas-guided annotation in cohort-scale spatial omics with HiCAT

HiCAT improves spatial omics annotation by combining unsupervised clustering with atlas guidance, revealing molecularly defined regions and enhancing downstream analysis.

scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq

scTrends classifies and quantifies gene expression trends along pseudotime in single-cell RNA-seq datasets, revealing diverse expression patterns across biological processes.

CLOP-DiT: Structured-Metadata-Conditioned Single-Cell Latent Generation via Contrastive Language-Omics Pretraining and Diffusion Transformers

CLOP-DiT generates synthetic single-cell gene expression profiles from structured biological metadata using contrastive alignment and diffusion models, achieving high steering accuracy but limited cross-dataset variability.

reComBat-seq: Regularized negative binomial regression for batch-effect correction in underdetermined transcriptomics datasets

reComBat-seq removes batch effects in transcriptomics data while preserving count data structure and compatibility with downstream analyses.

An agent-built, verified atlas of public spatial transcriptomics shows that breadth across studies drives transfer and reproducibility

The paper presents a verified spatial transcriptomics atlas built from public data, showing that increasing study diversity improves model transfer and reproducibility.