Single-cell and spatial transcriptomics

SOFisher improves spatial omics experiment design by using reinforcement learning to guide FOV selection, outperforming conventional methods in capturing relevant biological features.

The paper introduces GLACIER, a method that infers spatially encoded transcriptional and signaling relationships using spatial Granger causality, applied to mouse cerebellum single-cell spatial transcriptomics data.

This study optimizes Xenium in situ sequencing for PAXgene-fixed tissues, showing comparable or better spatial RNA detection than FFPE samples.

The paper introduces N-Orbit, a mathematical model for comparing tissue microenvironments by encoding cell type composition and spatial relationships, showing improved performance over existing metrics in neighborhood discrimination and clinical prediction.

This paper introduces a method for cell trajectory inference using optimal transport with a mechanistic stochastic gene expression model, showing the Bursty model better captures gene dynamics than the Diffusive model.

DGAT imputes spatial protein expression from transcriptomics-only data, revealing cell states and tissue organization not visible from RNA alone.

This study profiles root cells from five Brassicaceae species under stress treatments, revealing cell-type-specific gene expression divergence and stress response variation across species.

This study uses scRNA-seq on uterine blood samples to analyze immune and stromal changes in endometrial cancer progression, identifying neutrophil signatures that stratify disease states.

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

This paper introduces MCI, a method to estimate absolute counts from proportional data using multivariate normal distributions, and demonstrates its effectiveness on white blood cell counts and single-cell mRNA data.

This study uses snRNA-seq to analyze age-related transcriptional changes in four brain regions across young and aged individuals, identifying region-specific age-associated genes and biological processes.

The paper introduces MetaFR, a method that links scATAC-seq open-chromatin variation to scRNA-seq gene expression, showing improved performance over existing methods.

This paper develops a sample processing pipeline for spatial transcriptomics of murine musculoskeletal tissues, achieving high-quality transcript detection across multiple tissue types using the Xenium Prime 5K platform.

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

The paper introduces TranscriptFormer, a generative model trained on 112 million cells across 12 species, demonstrating cross-species cell type classification and disease state prediction.

SCOTCH improves isoform-level gene expression characterization using long-read single-cell RNA sequencing by refining sub-exon boundaries and reducing false positives.

EssentCell identifies essential evolutionary relations in noisy single-cell data by solving a minimum-flip problem with false positive constraints.

This study introduces ReMiTT, a computational method to identify T cell migration trails in spatial transcriptomics data, revealing chemokine trends and enriched pathways associated with T cell movement in tumors.

CAPTAIN is a multimodal foundation model trained on single-cell RNA and protein data, improving multi-omics integration and uncovering protein-driven intercellular dynamics.

CRAK-Velo improves RNA velocity estimation by integrating chromatin accessibility data, enabling accurate cell-type deconvolution and insights into gene-chromatin interactions.

anndataR enables interoperability between R and Python by allowing native reading and writing of H5AD files in R, with compatibility testing and conversion to R single-cell objects.

RegFormer improves single-cell RNA sequencing analysis by integrating gene regulatory networks with Mamba-based modeling, outperforming existing models in clustering, integration, and annotation.

DiffuST denoises spatial transcriptomics data by integrating spatial features and pathology images, improving downstream analysis and biological insights.

Renoir maps ligand-target activities across spatial topologies and identifies context-specific cell-cell interactions from spatial transcriptomics data.

The paper introduces a statistical model to test functional effects in spatial transcriptomics data by analyzing gene expression patterns across space.

geneSCOPE identifies spatial gene modules and cell-cell interactions by integrating ecology-inspired statistics and network analysis in spatial transcriptomics data.

This paper introduces scDynaBar, a method using CRISPR barcoding to track temporal cellular events at single-cell resolution, showing the transient nature of the 2C-like state in mESCs.

The paper identifies off-target probe binding in 10x Genomics Xenium gene panels and develops a tool to assess its impact on spatial transcriptomic accuracy.

The paper quantifies marker fidelity and background expression of cortical cell types using single-nucleus transcriptomics data and extends canonical markers with improved subtype-specific markers.

CSFeatures identifies cell type-specific differential features in single-cell and spatial omics data by considering expression levels, smoothness, and proportion across cell populations.