Single-cell and spatial transcriptomics

This study maps the temporal sequence of mouse lung regeneration after viral injury, identifying persistent injury-associated cell states and their role in alveolar niche repair.

p-ClustVal improves clustering of scRNASeq data using a p-adic transformation, enhancing performance in 91% of cases across 30 experiments.

TraCSED identifies resistance pathways in breast cancer by analyzing scRNA-seq data and clonal selection dynamics over time.

This study integrates spatial transcriptomics and single-cell data from gastric cancer samples to reveal tumor ecosystem dynamics, evolutionary trajectories, and immune microenvironment features.

This study maps the molecular and cellular dynamics of the developing human neocortex using single-nucleus data, revealing gene regulatory networks and a cancer-related progenitor subtype.

UCS improves cell segmentation for subcellular spatial transcriptomics by integrating nuclei and transcript data, enhancing accuracy and enabling downstream analyses.

This study adapts the ACME HS method to isolate intact single cells from fresh-frozen endocrine tissues, showing it preserves cell integrity and gene expression better than other methods.

This study introduces a Bayesian Dirichlet-multinomial regression model to integrate single-cell RNA sequencing data with clinical variables, uncovering cell-type-specific associations across three diseases.

PACS is a statistical model for analyzing snATAC-seq data that improves power and controls false positives in differential accessibility analysis.

STARLING quantifies cell populations from spatial protein expression data while accounting for segmentation errors, identifying known cell types and quantifying heterogeneity in human tonsil tissue.

CryoSCAPE enables scalable immune profiling using cryopreserved whole blood for multi-omic single cell and functional assays, demonstrating compatibility with high-throughput RNA sequencing and functional testing.

LEMUR identifies gene expression changes across conditions using a continuous latent space, improving analysis of multi-condition single-cell RNA-seq data.

The paper introduces CRAWDAD, a tool for analyzing cell-type spatial relationships across scales in spatially resolved omics data, and demonstrates its application on simulated and real datasets.

The paper develops a method to annotate cell types across species using orthologous marker gene groups, identifying conserved cell-type markers in 15 plant species.

Stator identifies cell types, subtypes, and states in single-cell data by analyzing higher-order gene expression dependencies, revealing cryptic states and subtypes not captured by proximity-based clustering.

The paper uses spatial transcriptomics to map cell types and their spatial organization in healthy and fibrotic human liver, identifying changes in hepatocyte populations during fibrosis.

This paper introduces a VAE-based model for integrating multimodal single-cell data, showing it can map queries across modalities and improve data imputation and biological discovery.

This paper introduces GBCD to decompose tumor transcriptional heterogeneity in scRNA-seq data, identifying a new prognostic gene expression program in pancreatic cancer.

The paper uses single-cell transcriptomics to analyze stress responses in HepaRG cells exposed to various chemicals, identifying distinct phenotypic groups and dynamic shifts in cellular subpopulations.

The paper introduces a machine learning framework to identify multiscale cellular interactions in spatial-omics data, revealing both known and novel communication patterns across tissues.

The paper introduces SciDAP, a platform enabling biologists to analyze single-cell sequencing data without coding expertise using reproducible pipelines.

SPEX provides a web-based platform for spatial omics analysis, enabling modular and user-friendly processing of spatial transcriptomics and proteomics data.

SeuratExtend integrates R and Python tools with databases to streamline scRNA-seq analysis, offering enhanced visualization and functional analysis capabilities.

This paper evaluates a Voronoi-based cell segmentation method for spatial transcriptomics, showing improved cell type separation and transcript preservation compared to traditional clustering methods.

macpie streamlines analysis of high-throughput transcriptomic profiling data from plate-based screens using R, integrating preprocessing, quality control, and multimodal data integration.

JADE improves multi-slice spatial transcriptomics analysis by jointly aligning slices and learning shared embeddings, outperforming existing methods on benchmark datasets.

This study uses scRNA-seq data to identify developmental-like cell states in brain tumors, suggesting potential cells of origin for medulloblastoma, glioma, and diffuse midline glioma.

This paper introduces scAGG, a model for classifying Alzheimer's disease from single-nucleus RNA-seq data by aggregating single-cell embeddings, and finds it accurately classifies AD and healthy controls with disease severity ordering in the latent space.

The paper introduces APP, a clustering method that reduces high-dimensional biological data to lower dimensions to improve clustering performance across multiple data types.

This paper introduces MMoCHi, a multimodal classifier for CITE-seq data that improves immune cell subset annotation by integrating transcriptomic and proteomic data.