Single-cell and spatial transcriptomics

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.

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

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.

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.

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

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

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

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

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

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

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

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

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

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

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 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.

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

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

The paper introduces REBEL, a method for analyzing differential expression in longitudinal scRNA-seq data, showing it controls false discovery rate and has competitive power.

This paper introduces a computational framework for true 3D spatial reconstruction of tissues, demonstrating improved fidelity over 2D methods across multiple spatial omics modalities.

scLASER simulates and detects time-dependent cellular dynamics in longitudinal studies, showing higher sensitivity than traditional methods in rare cell types and non-linear patterns.

scPlOver infers DNA content from single-cell WGS data using fragment overlaps, improving accuracy in identifying high DNA content cells in ovarian cancer.

This study benchmarks single-cell drug response models across 26 datasets, showing performance varies with dataset type, imbalance, and model design, with scDEAL showing competitive results.

ESFS improves scRNA-seq data interpretability by identifying gene sets with complex expression dynamics, outperforming traditional methods in diverse biological contexts.

This paper develops a platform for longitudinal imaging and transcriptomics of live cells, revealing adaptive drug-resistant states and key drivers of cellular function through integrated analysis.

This study uses dynamic caging to link live-cell behavior with transcriptional responses across perturbations, revealing new insights into immune and inflammatory gene regulation.

The paper uses spatial transcriptomics to analyze cardiomyocyte states in pressure-overloaded mouse hearts, finding pro-fibrotic states linked to microenvironmental changes.

This study maps human B-cell populations and their interactions across 10 tissues using single-cell RNA sequencing, revealing tissue-specific adaptations and residency gene modules.