Single-cell and spatial transcriptomics

PreTSA efficiently models temporal and spatial gene expression patterns in large single-cell and spatial transcriptomics datasets, matching state-of-the-art results with reduced computational time.

SpatialESD improves spatial domain detection in spatial transcriptomics by integrating multiple methods and capturing both direct and indirect relationships between clusters.

This paper compares statistical methods for differential gene expression in spatial transcriptomics, finding that the GEE framework better controls false positives than the Wilcoxon test.

scChat is a large language model co-pilot for scRNA-seq analysis that integrates contextual reasoning to improve hypothesis generation and experimental design.

This study uses single-cell RNA sequencing to analyze how sex and age affect gene expression and cellular interactions in healthy human liver cells.

Seq-Scope-X improves spatial transcriptomics resolution by expanding tissues, revealing detailed nuclear and cytoplasmic transcriptome differences in liver cells and enabling proteomic analysis in other tissues.

This paper introduces a tool to detect overlapping cells and segmentation errors in 3D spatial transcriptomics data.

stDyer-image improves clustering of spatial transcriptomics and proteomics data by integrating morphological images directly into the clustering process.

This study benchmarks 27 computational tools for transferring cell type labels from scRNA-seq to scATAC-seq data across human and mouse tissues, finding Bridge and GLUE as top performers.

This study uses single-cell transcriptomics to analyze immune aging, finding that acute COVID and HIV+ART both increase naive CD8 T cell transcriptomic age.

This paper introduces UVAE to integrate unpaired clinical flow cytometry data, improving cell type signal and clustering by correcting batch effects.

Castl identifies spatially variable genes by integrating multiple methods to reduce variability and control false discovery rates across datasets.

scLong is a billion-parameter foundation model that captures long-range gene context in scRNA-seq by modeling all genes and integrating gene knowledge, outperforming existing models in various tasks.

FLASH-MM improves single-cell differential expression analysis by using a fast linear mixed-effects model that is accurate and efficient.

This study uses single-cell RNA-seq to analyze X-ray irradiation effects on Drosophila wing discs, revealing regional and intra-regional gene expression heterogeneity.

This paper introduces scPCA, a dimensionality reduction framework that integrates cellular heterogeneity and condition effects in scRNA-seq data, revealing transcriptional changes across conditions and cell types.

spRefine denoises and imputes spatial transcriptomics data using a reference-free deep learning framework, improving data integration and revealing new aging-related biological insights.

BiCLUM integrates unpaired single-cell multi-omics data by aligning cell and feature spaces through bilateral contrastive learning, outperforming existing methods in visualization and quantitative metrics.

This study compares in vivo and in vitro midbrain development to assess their similarity to human fetal tissue.

The paper introduces smoppix, a nonparametric method for analyzing single-molecule spatial omics data, identifying localization patterns across tissues and cells.

This paper introduces scPSS, a method to quantify pathological shifts in single-cell data by comparing diseased cells to healthy references, showing it can predict individual health conditions.

The paper develops a classifier, DeCAF, to identify permissive and restraining cancer-associated fibroblast subtypes from bulk RNAseq data and validates its clinical relevance across multiple cancer types.

This paper introduces a workflow for integrating spatial transcriptomics and metabolomics data to analyze glioma tissue, identifying novel biological features at the tumor's infiltrating edge.

Tensor-cell2cell v2 jointly analyzes protein- and metabolite-mediated cell-cell communication over time, revealing coordinated dynamics in brain organoid development.

This paper uses a gene expression cartography algorithm to create a 3D atlas of the Drosophila optic lobe, identifying spatially compartmentalized cell types and gene patterns.

RNA2seg is a cell segmentation model for spatial transcriptomics trained on MERFISH and CosMx data, showing superior performance in zero-shot and few-shot settings.

scELMo uses Large Language Models to analyze single-cell data for clustering, batch correction, and cell-type annotation without retraining, outperforming existing methods.

The paper creates a single-cell atlas of sex-differentially expressed genes in human hypothalamus regions, revealing molecular sex differences and their links to neurodevelopmental disorders.

The paper shows that EMT paths are influenced by cell cycle phase coupling and highlights the limitations of low-dimensional representations in capturing transition dynamics.

stGCL integrates multi-modal spatial transcriptomics data using graph contrastive learning to improve clustering and spatial pattern analysis.