Single-cell and spatial transcriptomics

Smoothie denoises spatial transcriptomics data and constructs co-expression networks to detect gene modules and annotate functions across tissues and conditions.

This study evaluates foundation models for single-cell data analysis across eight tasks, finding that task-specific methods may outperform them in some cases and proposing guidelines for improving FM performance.

The paper generates a single-cell transcriptomic dataset of murine embryoid bodies under hypoxia and normoxia to study oxygen's impact on lineage specification and cellular heterogeneity during early development.

This paper presents a single-cell and spatial proteotranscriptomic atlas of human oral tissues, identifying fibroblasts as key immunoregulatory hubs and revealing changes in disease states.

The paper introduces UCASpatial, an ultra-precision spatial transcriptomics deconvolution algorithm, to improve the identification of immune cell subpopulations and their functional roles in tissue microenvironments.

The paper analyzes human endothelial cell heterogeneity across tissues using scRNA-seq data and identifies tissue-specific cell-cell communication patterns.

The paper quantifies cell type populations across 3D anatomical structures using single-cell transcriptomics and proteomics data from the Human Reference Atlas.

This paper develops a zero-inflated hierarchical generalized transformation model to improve cell-type deconvolution in spatial transcriptomics data from oral squamous cell carcinomas.

DAESC+ is a software package for single-cell allele-specific expression analysis, offering improved accuracy and speed compared to existing methods.

This paper introduces a spatial omics method that combines high spatial resolution with genome-wide profiling through optical barcoding and sequencing.

TUSCAN improves tumor region identification in spatial transcriptomics by integrating copy number variation profiles with histopathological images.

The paper maps cis-regulatory elements in the macaque cortex using snATAC-seq and links them to disease-related genes and risk SNPs.

CellVoyager autonomously analyzes scRNA-seq data to generate new hypotheses and findings, outperforming existing models and producing scientifically interesting results.

SCPRO-VI integrates multi-omic single-cell data using a variational graph auto-encoder and biologically guided metrics, improving subcluster identification.

singIST quantifies disease model similarity to human conditions at single-cell resolution using pathway and cell type-level measures, demonstrating its utility in atopic dermatitis models.

MultiGAI corrects batch effects in single-cell multi-omics data while preserving biological signals, showing effectiveness in integration and correction tasks.

CTFacTomo reconstructs 3D spatial gene expressions from RNA tomography data using tensor factorization and regularization, outperforming benchmarks on zebrafish and mouse datasets.

The paper introduces a whole-embryo spatial transcriptomics method and generates a comprehensive gene expression and chromatin accessibility atlas in zebrafish embryos.

The paper introduces STHELAR, a multi-tissue dataset integrating spatial transcriptomics and H&E images for cell type annotation in cancer research.

Connectome-seq maps neuronal connectivity at single-synapse resolution using barcoding and sequencing, revealing molecular markers in connected neurons.

The paper integrates six single-cell datasets to create a high-resolution atlas of HPV-negative HNSCC, revealing new cell populations and associations between transcriptional signatures and clinical outcomes.

The paper uses scRNA-seq to analyze the response of human bronchial epithelial cell types to CdCl2, revealing cell-type-specific transcriptional changes.

The paper develops CSI-TME to identify prognostic cell state interactions in IDH-mutant gliomas, revealing a pro-tumor network associated with immune checkpoint therapy response.

scMILD identifies condition-associated cells using sample-level labels and reveals biological transitions in disease datasets.

The paper applies Pareto optimality to single-cell RNA sequencing data to identify cellular archetypes and their functions, finding that this principle explains transcriptomic variation across cell types.

This study creates a single-cell immune atlas of pig lymphoid organs using scRNA-seq, identifying conserved immune cell populations between pigs and humans.

This study introduces DeepTX, a deep learning framework that links mechanistic models to scRNA-seq data to infer transcriptional burst kinetics in response to DNA damage.

This study evaluates dispersion metrics for estimating transcriptional variability in single-cell RNA sequencing data and finds the variance-to-mean ratio is scale-invariant and responsive to noise.

This study compares three nuclei isolation methods for brain snRNA-seq, finding protocol-dependent differences in cell type proportions, transcriptional homogeneity, and RNA contamination levels.

This paper creates a multi-omic atlas of human autonomic and sensory ganglia, identifying cell-type-specific features and disease associations in peripheral neuropathies.