Single-cell and spatial transcriptomics

The paper introduces NNclinSSOAP, a GxP-ready pipeline for processing single-cell and spatial transcriptomics data, enabling reproducible and scalable analysis in clinical settings.

This paper introduces NCLUSION, a nonparametric clustering method for scRNA-seq that identifies marker genes and clusters simultaneously, showing improved performance and reduced runtime.

SpaGene improves spatial transcriptomics by imputing missing gene expressions, achieving higher accuracy than existing methods and revealing immune dynamics in lung tumors.

GALA provides a landmark-free framework for spatial alignment across resolutions and modalities in spatial transcriptomics, improving accuracy and efficiency.

SpaFun identifies domain-representative genes in spatial transcriptomics data using functional principal component analysis, outperforming existing methods in accuracy and efficiency.

The paper identifies how heterogeneous single-cell observations distort manifold-based inference and proposes topological stability descriptors to assess reliability.

scGPD designs gene panels for spatial transcriptomics using single-cell data, achieving better coverage and classification accuracy than existing methods.

This paper introduces TranspaceR, an R package with statistical methods for analyzing spatial transcriptomics data, improving quality control, gene selection, and normalization for large-scale datasets.

This paper introduces spVIPES, a framework for disentangling shared and private factors of variation in single-cell RNA-seq data, showing improved performance over existing batch correction methods.

stVCR reconstructs spatiotemporal dynamics from time-series spatial transcriptome data by integrating generative deep learning with optimal transport and spatial priors.

Souporcell3 improves demultiplexing for high-donor scRNA-seq datasets by using K-Harmonic Means and iterative refinement, achieving higher accuracy than previous methods.

GeneExt improves gene annotation using scRNA-seq data, enhancing gene expression quantification and cross-species cell type comparisons.

This study constructs a single-cell chromatin accessibility atlas across mouse tissues to reveal age-related cellular and epigenomic changes, showing widespread population shifts and sex-dependent dynamics.

The paper introduces dominoSignal, a software tool that statistically tests differential cell signaling from single-cell data to infer changes in cell-cell communication networks.

mosna analyzes spatial omics data to identify cell interactions predictive of immunotherapy response and survival in cancer.

NeuroMDAVIS is a multi-omics visualization model that integrates multiple data modalities to generate low-dimensional embeddings preserving local and global structures, outperforming existing methods.

This study evaluates six methods for calling single-nucleotide variants from scRNA-seq data and finds that some methods provide phylogenetically informative genotype calls, with implications for understanding cancer progression.

The paper identifies tumor-specific malignant cell types encoded across the transcriptome, independent of patient origin or computational biases.

APOLLO learns shared and modality-specific information from multi-modal single-cell data to improve understanding of cell state and regulatory mechanisms.

This paper introduces Spatial Perturb-Seq, a CRISPR-based method to screen genes in intact tissues, revealing gene effects on neuronal communication in mouse brains.

The paper uses spatial single-cell data to identify tumor cell clusters (villages) and their microenvironmental dependencies, showing their impact on patient outcomes.

CAMEX integrates, aligns, and annotates multi-species scRNA-seq data using a GNN, outperforming existing methods and enabling species-specific cell type detection.

The paper uses multiomic single-cell and spatial data to identify gene regulatory networks and transcription factors driving cortical regionality in the human brain.

This paper creates a gut transcriptomics atlas using MERFISH to show how microbiota influences cell types, spatial organization, and molecular responses in the murine gut.

The paper develops a computational pipeline to calibrate reaction-diffusion models of ligand signaling using transcriptomics data, applied to TGF{beta} isoforms in wound healing.

SwarmMAP uses swarm learning to automate cell-type annotation in single-cell data without sharing raw data, achieving high accuracy across human tissues.

The paper introduces mFISH3D, a multiplexed staining technique combined with AI for high-resolution whole-brain cell mapping in mice and humans.

The paper generates a single-cell multi-omics atlas of pediatric bone marrow, revealing age-related shifts in lineage differentiation linked to stromal cell heterogeneity.

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.