Single-cell and spatial transcriptomics

Linking Genetic Risk to Disease-Relevant Cellular States via Metacell-Informed Modeling with ICePop

ICePop links genetic risk to disease-relevant cellular states by analyzing single-cell transcriptomics data across multiple traits, identifying 1,684 disease-cell type associations.

Spatial transcriptomics implicates the thalamus and cortex in autism and schizophrenia

The paper uses spatial transcriptomics to identify brain regions enriched for ASD and schizophrenia genes in mice, finding the thalamus, cortex, and hippocampus as key areas.

Deep Learning for Cross-Domain Spatial Transcriptomic Modeling of Tissue Repair

This paper introduces a cross-domain spatial transcriptomic framework to analyze tissue repair dynamics, showing coherent tissue state separation and distinct spatial organization patterns in wound healing versus tumor microenvironments.

Integrating trajectory inference and gene regulatory network analysis to resolve transcriptional programs of T cell state transitions in the tumor microenvironment

This paper provides a computational workflow for analyzing T cell state transitions in the tumor microenvironment using single-cell RNA sequencing data, integrating trajectory inference and gene regulatory network analysis.

Differential Analysis of Gene Spatial Organisation with Minkowski Functionals and Tensors

The paper introduces minkiPy, a geometric framework for analyzing spatial gene expression patterns using Minkowski functionals and tensors, and applies it to spatial transcriptomics datasets.

Single-nucleus profiling reveals age-associated remodeling opposed by parity in the postmenopausal human ovary

The paper uses single-nucleus profiling to show age-related changes in the postmenopausal ovary, which are counteracted by parity.

DeSpotX: Identifiability-Based Decontamination for Spatial Transcriptomics

DeSpotX improves spatial transcriptomics by decontaminating transcript data using a deep generative model that identifies contamination and enhances biological coherence.

Riemannian Tensor Analysis Identifies a Bifurcation State in the Single-Cell Transcriptomic Landscape of Parkinson's Disease

This paper identifies a bifurcation state in Parkinson's disease transcriptomics using a Riemannian tensor framework, showing it as a geometric saddle point between healthy and diseased states.

A Comprehensive Benchmarking of Spatial Deconvolution andDomain Detection Methods across Diverse Tissues and SpatialTranscriptomic Technologies

This paper benchmarks spatial deconvolution and domain detection methods across diverse tissues and technologies, identifying top-performing approaches and highlighting method limitations.

Disagreement between demultiplexing methods reveals structured cell quality gradients in multiplexed single-cell data

This paper introduces Split-flow, a pipeline that uses concordance between demultiplexing methods to classify cell quality in multiplexed single-cell data, revealing structured quality gradients.

Transferable spatial omics deconvolution with SpaRank

SpaRank is a transferable spatial deconvolution framework that uses rank-based encodings to robustly infer cell-type compositions across diverse biological contexts and omics modalities.

Cell Type-informed Characterization of Spatial Niches from Spatial Multimodal and Multi-omics Data

SpaNECT is a framework for characterizing cell niches from spatial multimodal and multi-omics data, outperforming existing methods in diverse biological contexts.

GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics

GatorDuo improves spatial domain identification in spatial transcriptomics by refining dual graphs with pseudo-label agreement and adaptive clustering.

Inferring absolute counts from proportions by constraining multivariate normal distributions

This paper introduces MCI, a method to estimate absolute counts from proportional data using multivariate normal distributions, and demonstrates its effectiveness on white blood cell counts and single-cell mRNA data.

Region-Specific Transcriptional Signatures of Brain Aging in the Absence of Neuropathology at the Single-cell Level

This study uses snRNA-seq to analyze age-related transcriptional changes in four brain regions across young and aged individuals, identifying region-specific age-associated genes and biological processes.

NovaClone: A Network-Based Algorithm for Clonal and Subclonal Genotyping of Barcoded Transgene Integrations

NovaClone improves clonal and subclonal genotyping in single-cell lineage tracing by addressing technical artifacts and providing quality control metrics.

StabCell: Stability selection for clustering and marker detection in single-cell RNA sequencing

StabCell integrates clustering and marker detection in scRNA-seq to improve robustness and reduce false positives by assessing stability across subsampled data.

SigBridgeR: An Integrative Framework and Toolkit for Comprehensive Screening and Benchmarking of Phenotype-Associated Cell Subpopulations in Single-Cell Transcriptomics

SigBridgeR benchmarks phenotype-associated cell subpopulations across four cancer types using eight algorithms, providing standardized workflows and visualization tools for reproducible analysis.

ConvergeCELL: An end-to-end platform from patient transcriptomics to therapeutic hypotheses

ConvergeCELL translates patient transcriptomics into therapeutic hypotheses by integrating patient representation, interpretability, and large language models, outperforming baselines in gene recovery and classification.

An optimized workflow for spatial transcriptomics across early development in Xenopus

The paper describes an optimized spatial transcriptomics workflow for Xenopus embryos, enabling high-resolution gene expression mapping across developmental stages.

Identifying Context-Specific Cell-Cell Interaction Genes Without Ligand-Receptor Databases from Spatial Transcriptomics

CellNeighborEX v2 identifies context-specific cell-cell interaction genes from spatial transcriptomics data without relying on ligand-receptor databases.

Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration

The paper introduces CLIC scores to quantify confidence in gene-peak associations for single-cell RNA-ATAC integration, improving integration accuracy and biological interpretability.

Predicting gene-specific regulation with transcriptomic andepigenetic single-cell data

The paper introduces MetaFR, a method that links scATAC-seq open-chromatin variation to scRNA-seq gene expression, showing improved performance over existing methods.

Lense: Optimizing data preprocessing in single-cell omics using LLMs

Lense optimizes single-cell omics preprocessing by using language models to select the best pipeline based on visual comparisons of low-dimensional representations.

Pathway-informed Universal Domain Adaptation for Single-cell RNA-seq Data

This paper introduces scPathOT, a pathway-informed domain adaptation method for single-cell RNA-seq data, demonstrating its ability to accurately annotate cell types and uncover functional mechanisms across diverse biological contexts.

pyTrance finds co-localizing RNAs in subcellular spatial transcriptomics data

pyTrance identifies co-localizing RNAs in spatial transcriptomics data with higher accuracy than existing methods, revealing biologically relevant RNA co-localization patterns in mouse brain tissue.

Clone-level multi-modal prediction of tumour drug response

This paper introduces scClone2DR, a framework predicting tumour drug response at the clone level using single-cell DNA/RNA sequencing and drug-screening data, showing improved accuracy in identifying clonal sensitivities and resistance.

InterScale reveals multi-scale cellular interaction programs in spatial transcriptomics

InterScale models both local and global cellular interactions in spatial transcriptomics data, revealing spatially restricted differentiation programs and disease-associated tissue remodeling.

SRSA-VAE: Self-Attention-Based Feature Learning for Single-Cell Multimodal Clustering

SRSA-VAE improves single-cell clustering by using self-attention and residual connections, outperforming existing methods on scRNA-seq and CITE-seq data.

A novel metric reveals previously unrecognized distortion in dimensionality reduction of scRNA-Seq data

Comparing local point neighborhoods before and after dimensionality reduction reveals that standard scRNA-seq pipelines, t-SNE, and UMAP often distort local neighborhoods by more than 95%, altering downstream clustering.