Single-cell and spatial transcriptomics

This paper integrates single-cell RNA-seq data to create a human corneal cell state meta-atlas and develops a machine learning tool to annotate cell states and identify key transcription factors.

The paper introduces a copula-based method to model non-linear gene co-expression changes along temporal trajectories in scRNA-seq data, applied to study pituitary development in mice.

PoweREST estimates statistical power for detecting differentially expressed genes in spatial transcriptomics experiments using 10X Genomics Visium data.

The paper introduces CHOCOLAT-G2P, a method integrating functional genomics and spatial transcriptomics to study genotype-phenotype relationships in tumors, revealing zonation patterns and epistasis in liver cancer.

This study uses snRNA-seq to identify molecular features of human airway microfold cells and discovers a novel epithelial cell type with an interferon-stimulated gene signature.

PEtracer maps cell state and lineage dynamics in vivo with high spatial resolution using prime editing and multimodal imaging.

The paper develops an algorithm to correct cell-type composition in bulk RNAseq data, revealing novel gene pathways in post-myocardial infarction cardiac remodeling.

Decipher is a deep generative model that integrates single-cell RNA-seq data to visualize and characterize transitions between normal and perturbed cell states across different diseases.

The paper develops a theoretical model to predict multiplet types in mx-scRNA-seq and shows that partial stealth multiplets can significantly affect data when labeling is suboptimal.

CausCell improves explainability and controllability of single-cell data through causal disentanglement learning and counterfactual generation.

CytoPheno automates cell type naming and marker definition in cytometry data by standardizing markers and matching them to ontologies.

Spatial-scERA reconstructs spatial single-cell enhancer activity in multicellular organisms using integrated transcriptomic and spatial data.

This paper introduces a model that combines expressive representation learning with interpretable Gaussian processes to analyze single-cell multi-omics data, revealing relationships between cell clusters and marker genes.

The paper introduces scComplete-seq, a method to profile non-polyadenylated RNA in single cells, revealing changes in coding and non-coding transcriptomes and enhancer RNA activity in PBMCs.

Bering improves cell segmentation and annotation in spatial transcriptomics by using graph embeddings and transfer learning, achieving better accuracy across different technologies and tissues.

STHD provides probabilistic cell typing for high-resolution spatial transcriptomics data by directly modeling single-spot gene expression without aggregation, improving segmentation and downstream analysis.

SCNT provides an R package for analyzing and visualizing single-cell and spatial transcriptomics data, offering tools for processing, quality control, and visualization.

scPDA denoises protein expression in single-cell droplet data, improving cell-type identification by removing noise more thoroughly while preserving biological signals.

SpatioMark quantifies how spatial proximity affects cell phenotype by analyzing spatial proteomics and transcriptomics data, linking findings to patient survival.

This paper introduces TopoVelo, a method to infer spatial and temporal dynamics of cell fate transitions from spatial transcriptomic data, improving spatial coherence and accuracy in cell differentiation and migration analysis.

LMD identifies localized genes in single-cell RNA-seq data to characterize cellular diversity without batch correction, outperforming existing methods.

The paper introduces spatialMNN, an algorithm that integrates spatial transcriptomics data to identify spatial domains by incorporating 2D spatial information into mutual nearest neighbors analysis.

The paper generates a scRNA-seq reference atlas of living and early post-mortem human retina, revealing distinct transcriptional dynamics and a novel mlCone cluster.

SCassist uses large language models to guide and enhance single-cell RNA sequencing analysis by providing recommendations and interpretations throughout the workflow.

SCALPEL quantifies single-cell transcript isoforms with higher sensitivity than existing tools, revealing novel cell populations and isoform regulation in mouse spermatogenesis and iPSC differentiation.

PerturbNet predicts cell state changes from unseen chemical and genetic perturbations using deep learning and perturbation response data.

This paper benchmarks 40 single-cell multi-modal integration algorithms across paired, unpaired, and mosaic datasets to evaluate usability, accuracy, and robustness.

CosMx spatial transcriptomics of bone marrow from 6 refractory/relapsed AML patients shows CD14+ monocytes rising in leukemia-cell neighborhoods after pembrolizumab in responders, dendritic cells in nonresponders.

The paper introduces Cell Marker Accordion, a tool for accurate and interpretable single-cell and spatial omics annotation across health and disease contexts.

This study assesses how spatial microenvironments influence gene expression and cell-cell communication using scRNA-seq and spatial transcriptomics, finding limited transcriptional changes and weak correlation between communication predictions and spatial colocalization.