Single-cell and spatial transcriptomics

ClustSIGNAL identifies cell types and subtypes using an adaptive smoothing approach for scalable spatial omics clustering

ClustSIGNAL improves spatial transcriptomics clustering by adapting smoothing based on neighbourhood complexity, enabling scalable analysis of large datasets.

PerturbBridge: Conditional Latent Schr\"{o}dinger Bridge for Single-Cell Perturbation Prediction

PerturbBridge predicts single-cell perturbation responses by modeling population-level distribution shifts using a conditional latent Schrödinger Bridge framework, achieving state-of-the-art differential-expression recovery.

OmicsResonance: An LLM-assisted cloud ecosystem for interactiveand reproducible single-cell transcriptomic analysis

OmicsResonance provides a code-free web platform for scRNA-seq analysis, integrating LLM-assisted tools and interactive features to enable reproducible and transparent single-cell research.

EISCA and EISTA: Full-Spectrum Pipelines for Single-Cell and Spatial Transcriptomics Analysis

EISCA and EISTA are pipelines for single-cell and spatial transcriptomics analysis that provide standardized, modular workflows for data processing and downstream analysis.

ResolVI - addressing noise and bias in spatial transcriptomics

ResolVI improves spatial transcriptomics by correcting molecule misassignment and batch effects, enhancing cell state distinction and cross-dataset integration.

MoTRUST: adaptive semantic protection for mosaic single-cell integration and molecular recovery

MoTRUST integrates mosaic single-cell multi-omics data and recovers missing molecular information, achieving top performance in integration and recovery tasks.

singIST: an R/Bioconductor library and Quarto dashboard for automated single-cell comparative transcriptomics analysis ofdisease models and humans

singIST compares disease model scRNA-seq data to human references to assess recapitulation at superpathway, cell type, and gene levels.

A Counterfactual Framework for Directional Cell-Cell Interaction Analysis in Spatial Transcriptomics

This paper introduces a counterfactual framework to analyze directional cell-cell interactions in spatial transcriptomics, identifying significant asymmetric influences between tumor, immune, and stromal compartments in cholangiocarcinoma tissue.

NicheScribe: consistent and generalizable annotation of stromal cells in the murine bone marrow niche

The paper develops NicheScribe, a method for consistently annotating stromal cells in the murine bone marrow niche using scRNA-seq data, enabling comparative analysis across different conditions.

CMAC: A deep learning framework for absolute cardiomyocyte transcriptional age estimation

CMAC estimates absolute transcriptional age of cardiomyocytes using deep learning, achieving high accuracy across multiple datasets and platforms.

Ultra-fast and scalable high-resolution full-length single-cell RNA sequencing using CHART-seq

CHART-seq enables ultra-fast, scalable full-length single-cell RNA sequencing with improved gene detection and isoform annotation compared to existing methods.

FlowMap: Geometry Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories

FlowMap jointly models cellular states and dynamics in a geometric framework, recovering interpretable trajectories and developmental patterns from single-cell RNA sequencing data.

Cell-Lens: Advancing LLM Reasoning at Single-Cell Resolution

Cell-Lens improves LLM reasoning on single-cell data by using a structured representation that captures more biological content than gene lists alone.

Fast, flexible analysis of differences in cellular composition with crumblr

Crumblr is a statistical method for analyzing changes in cell type composition across studies, showing increased power over existing methods in simulations and applied to multiple single-cell RNA-seq datasets.

Speciesformer learns conserved cellular states for cross-species generative virtual cell modeling

Speciesformer learns conserved cellular states across species to enable cross-species generative modeling of virtual cell states and their responses to interventions.

Layer-to-cell-class correspondence is gene-specific for pyroptosis-related transcription in human skin

Epidermal NLRP1, PYCARD and CASP4 versus dermal GSDMD polarity in systemic sclerosis skin is reproduced by same-donor cell-class pseudobulk for PYCARD and GSDMD, but rarely NLRP1.

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

RKMR identifies predictive marker panels in spatial omics data using a scalable framework that integrates kernel modeling and spatial dependence.

Spatial Transcriptomics Reveal Developmental Dynamics of the Human Cerebral Cortex and Striatum

This study uses spatial transcriptomics and snRNA-seq to identify 25 forebrain regions and characterize dynamic changes in the human fetal cortex and striatum during late first and early second trimesters.

High-Dimensional Multi-omic Mapping of Post-Mortem Human Brain Using Iterative Indirect Immunofluorescence Imaging on Xenium-Processed Tissues

The paper develops a post-processing workflow combining Xenium spatial technology with 4i imaging to enable multi-omic mapping of post-mortem human brain tissue, focusing on pathological microenvironments.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

The paper introduces dreamlet, an R package for efficient differential expression analysis of large-scale single-cell transcriptomics data, showing improved speed and memory usage compared to existing methods.

Single-nucleus transcriptome-wide association study of human brain disorders

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

An integrated pipeline to count individual transcripts with single-cell resolution

HCR3.0 RNA-FISH linked to RS-FISH spot detection labels transcripts in C. elegans embryos, larvae and adults, and yields per-neuron counts of short insulin-like peptide mRNAs in L2 larvae.

Single-Nucleus Atlas of Cell-Type Specific Genetic Regulation in the Human Brain

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

OT-knn: a neighborhood-aware optimal transport framework for aligning spatial transcriptomics data

OT-knn aligns spatial transcriptomics data by integrating local neighborhood information within an optimal transport framework, achieving accurate alignment across diverse datasets and conditions.

mc-ASTRA maps population-level variation to interpretable multicellular tissue organization processes

mc-ASTRA integrates molecular, spatial, and sample-level data to create interpretable maps of tissue-state variability and multicellular processes.

An Integrated Atlas of the Human Kidney Spanning Health and Diseases

The paper integrates multiple single-cell and single-nucleus RNA-sequencing datasets to create a comprehensive Human Kidney Cell Atlas, identifying cell types, states, and disease-associated populations.

Time-resolved single-cell state tracing exposes bidirectional state dynamics during differentiation

The paper uses single-cell state tracing to show bidirectional differentiation dynamics and rare transcriptome conversions across lineage boundaries in both in vivo and in vitro settings.

Fishash: A contingency table approach to Perturb-seq guide assignment

Fishash assigns perturbations to cells in Perturb-seq data using Fisher's Exact Test, improving accuracy and speed over existing methods.

MINT infers the latent single-cell spatial transcriptome from paired histology

MINT infers cell-resolved spatial transcriptomes from paired histology and Slide-tags data, improving alignment and expression recovery in lung adenocarcinoma.

The Separable Organization of Immune Transcriptional Responses

The paper identifies a separable organization of immune transcriptional responses across cell types using single-cell data from peripheral blood mononuclear cells exposed to cytokines.