Single-cell and spatial transcriptomics

Multi-Trajectory Pseudotime Inference via Permutation Factorizations

Factorizing multiple monotone pseudotimes into one shared donor permutation plus cluster-specific warps (HiP-BIND) yields Alzheimer's donor orderings agreeing better with Braak, ADNC and CERAD staging than single-trajectory B-BIND.

scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation

scStable evaluates scRNA-seq analysis stability by generating synthetic samples with between-sample variation, showing better performance than existing methods in gastric cancer data.

Connectomics by Sequencing Reveals Self-Organizing Principles of Neuronal Networks in Cerebral Organoids

The paper develops COSCO to map neuronal connectivity in cerebral organoids and shows self-organizing network motifs and disease-related topological changes.

Spatially resolved transcriptomics of poplar reveals tissue organization across shoot-associated organs

This study creates a spatial transcriptome atlas of poplar shoot-associated organs, revealing tissue organization and gene expression patterns across different developmental stages and anatomical contexts.

HyperCom: a hypergraph-based method to infer cell-resolved cell-cell communication

HyperCom infers cell-resolved cell-cell communication using hypergraphs from single-cell transcriptomics data, providing a continuous sender-receiver score for ligand-receptor pairs.

REZCUE: Rescuing 3D spatial omics from archived FFPE tissue

REZCUE enables 3D spatial multi-omic analysis of archived FFPE tissue by preserving nucleic acids and proteins for subsequent assays.

A Measure of Transcriptional Dyscoordination for Quantifying Aging in Single Cells

This paper introduces a measure of transcriptional dyscoordination to quantify aging in single cells, showing it increases with age and is linked to cellular processes like senescence and genome instability.

stMCP: Spatial Transcriptomics with a Model Context Protocol Server

The paper introduces stMCP, a framework for spatial transcriptomics analysis using natural language-driven protocols, enabling local execution and reducing data privacy and cost concerns.

AmbiDose: per-cell removal budgets for ambient RNA contamination

AmbiDose removes ambient RNA contamination in single-cell RNA sequencing by using cell-specific removal budgets and ambient profiles, achieving high specificity for same-species UMIs.

Diverse infections transcriptionally reprogram the intestinal epithelium and epithelial-immune cell interactions

The paper creates a single-cell and spatial transcriptomics atlas of the ileum during diverse infections, revealing novel cell states and immune responses.

Identifiable inverse optimal transport for single-cell state transitions

This paper introduces UOT-IOT, an inverse optimal transport framework for single-cell state transitions, showing improved identifiability and performance over existing methods.

"Double-dipping" in testing for phenotypic differences with scRNA-Seq

The paper identifies a 'double-dipping' issue in scRNA-Seq where cell-type clustering and phenotype testing are performed on the same data, leading to inflated false discovery rates, and proposes solutions to mitigate this problem.

Powerful and accurate case-control analysis of spatial molecular data

This paper introduces VIMA, a method for analyzing spatial molecular data to identify disease-associated spatial features, showing improved performance over existing methods in simulations and real datasets.

Integrating single-cell and bulk transcriptomes identifies neuronal heat-stress-associated candidate genes in a reef-building coral

Circular Data Analysis for Spatial Omics

The paper provides guidance on analyzing circular data in spatial omics, using case studies to illustrate key considerations and methods.

ASTRA enables zero-shot super-resolution of spatial transcriptomics across capture geometries

ASTRA enables zero-shot super-resolution of spatial transcriptomics across different capture geometries, improving accuracy and efficiency compared to existing methods.

Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity

STARIT converts spatial transcriptomics data into image tensors for deep learning analysis, enabling subcellular heterogeneity-based cell state identification.

Conservation of gene co-expression networks and neuropsychiatric-neurological disease gene programs between mouse and human frontal cortex at single-cell resolution

The paper compares gene co-expression networks between mouse and human frontal cortex at single-cell resolution, finding modest gene partner similarity but strong module conservation and no species-specific disease gene partner differences.

RNA and protein are not interchangeable in single-cell spatial multi-omics

This study shows that spatial RNA and protein measurements are largely overlapping for lineage markers and abundance but discordant for specific molecules and spatial features, indicating they are complementary rather than interchangeable.

MERFISH-based pooled genetic screening reveals cell non-autonomous phenotypes in intact tissue

The paper introduces an imaging-based pooled screening method to identify genetic perturbations and their effects on cell states and neighboring cells in intact tissues.

How many cells resolve a perturbation direction? A closed-form, control-aware cell quota for single-cell perturbation screens

The paper derives a closed-form cell quota to determine the number of cells needed to accurately estimate perturbation direction in single-cell screens, validated across multiple datasets.

A Graph-Attentive GAN for Rare-Cell-Aware Single-Cell RNA-Seq Data Generation

GARAGE generates realistic synthetic scRNA-seq data by addressing high-dimensional, small-sample and rare-cell challenges using graph attention and adversarial generation.

Explainable t-SNE for Single-Cell RNA-seq Data Analysis: A Cell-DrivenInformation Fusion Framework

The paper introduces c-UMAP, a cell-driven information fusion framework for single-cell RNA-seq data that improves clustering accuracy and interpretability over traditional methods.

Slide-level batch structure limits histology-guided supervision of transcriptomic foundation models

This paper investigates whether histology can improve transcriptomic foundation models by providing cross-modal supervision, but finds limited benefits due to slide-specific structure in gene-expression embeddings.

CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

CellART extracts single-cell information from high-resolution spatial transcriptomics data across multiple platforms, demonstrating efficiency and accuracy in cancer datasets.

scDEcrypter: Uncertainty-aware differential expression analysis for viral infection in scRNA-seq

scDEcrypter improves differential expression analysis in viral infection studies by accounting for uncertainty in cell-state and infection labels, identifying more biologically relevant genes and pathways.

Deep generative embeddings of gene expression and splicing reposition the interpretation of single-cell transcriptomic signatures

Crecerelle integrates gene expression and alternative splicing to improve cell-type annotation and uncover regulatory pathways in single-cell transcriptomics.

ST-DISTAL: Dual-Branch Graph Convolution with Distributional Alignment for Cell-Type Deconvolution

ST-DISTAL improves cell-type deconvolution in spatial transcriptomics by integrating spatial and molecular information through a dual-branch graph convolutional framework.

Dataset structure outweighs method choice in single-cell cell-type annotation

In a 63-tool benchmark, dataset identity explained ≈84% of Cohen's κ variance on synthetic data versus ≈4% for tool identity, and k-nearest-neighbor (kNN) purity predicted accuracy near-linearly (R² = 0.85–0.99).

Method-dependent biases in cell type detection between single-cell and single-nucleus RNA sequencing in the photosymbiotic acoel Praesagittifera naikaiensis

The paper compares scRNA-seq and snRNA-seq in Praesagittifera naikaiensis, finding snRNA-seq better recovers neural transcripts and reduces symbiont reads.