Single-cell and spatial transcriptomics

STAT is a multi-agent framework that improves spatial transcriptomics analysis by enabling interactive, transparent, and efficient multi-task analysis across different data resolutions.

geneSync harmonizes gene symbols across RNA-seq datasets to improve integration quality by resolving nomenclature inconsistencies.

ACE-OF-Clust improves single-cell clustering by aligning results across methods and identifying informative features in scRNA-seq and spatial transcriptomics data.

MUSE integrates multi-omics data across species to align cellular states while preserving species-specific features and regulatory module conservation.

The paper constructs a single-cell peripheral immune atlas for Black and White women across breast disease states, identifying race-associated immune differences linked to chronic inflammation and immune aging in Black patients.

Phoenix predicts spatially resolved single-cell gene expression from histology, identifying new biomarkers and validating them across cancers and treatment regimens.

The paper introduces a statistical destriping method for VisiumHD spatial transcriptomics data using nuclear counts modeling, improving stripe-factor estimation and preserving biological signal.

The paper evaluates five single-cell RNA sequencing pipelines using Omnibenchmark, highlighting differences in parameter implementation across tools.

cellNexus enhances Human Cell Atlas data by adding quality control, metadata, normalization, and analysis layers to enable multi-study analyses and large-scale biological discovery.

The paper identifies POSTN-expressing cancer-associated fibroblasts (CAFs) linked to chemoradiotherapy resistance in rectal cancer using spatial multi-omics data.

This paper introduces CWDR, a dimensionality reduction method that improves visualization of cell types by incorporating lineage information, reducing overlap between clusters in high-dimensional data.

MilliMap provides an interactive framework for spatial omics analysis that integrates statistical computation and spatial exploration within a single environment.

The paper introduces clone2vec, a machine learning method for analyzing lineage-resolved single-cell data, revealing clonal patterns and continuous variation in development and disease.

The paper introduces an information-theoretic framework to define cell types by optimizing spatial information from molecular data in a whole-brain mouse spatial transcriptomic dataset.

spatiAlytica enables non-programmer biologists to perform interactive spatial omics analysis using natural language and a viewer-centric agentic system.

This paper introduces a standards framework for integrating single-cell and spatial omics data into brain cell atlases, enabling interoperable and reusable knowledge-ready resources.

This study evaluates CBMAP for scRNA-seq visualization, showing that clustering strategies significantly affect embedding quality and that Secuer improves global structure preservation.

The paper decodes BMP/TGFβ signaling from single-cell transcriptomes by identifying a low-dimensional transcriptional code and training a model to infer ligand concentrations.

The paper fine-tunes two scFMs on cancer subtypes and shows they outperform LightGBM on out-of-domain subtypes, with context-sensitive adaptation supported by interpretability analysis.

TRU-FACT aligns in vivo imaging and spatial biology datasets at cellular resolution to link cellular dynamics with molecular attributes.

SpatialBench evaluates spatial transcriptomics platforms using a mouse spleen model, finding consistent biological signals but varying resolution across technologies.

SubCellSpace identifies and characterizes subcellular mRNA localization patterns in spatial transcriptomics data using a computational framework.

Stitch-seq enables high-throughput profiling of CRISPR perturbations and gene/protein expression in millions of cells without cell barcoding or extensive sequencing.

rhapsodist processes BD Rhapsody scRNA-seq data using a Snakemake workflow, enabling cross-aligner comparison and generating count tables in SingleCellExperiment format.

TESSERA is a method for analyzing multi-sample spatial transcriptomics data, enabling differential expression analysis while accounting for spatial correlation and sample-specific factors.

This paper develops a high-quality, scalable workflow for whole-cell single-cell RNA sequencing of adult cardiomyocytes and non-myocytes using in-cell ligation and split-pool barcoding, improving data quality and throughput.

CHAMPOLLION improves multi-omics integration by using regularized optimal transport to align unpaired cells and reveal cross-modal relationships, outperforming existing methods with fewer paired cells.

SpatialQuery identifies multicellular spatial patterns and characterizes their molecular programs in spatial omics data, revealing cross-germ-layer signaling and disease-specific niches.

Helenus separates malignant and microenvironment gene expression from bulk RNA-seq to improve biomarker interpretation in cancer.

Spartan identifies spatial domains and variable genes by integrating spatial topology and activation signals in spatial transcriptomics data.