Single-cell and spatial transcriptomics

Interpretable Integration of CITE-seq RNA and ADT Profiles Without Explicit Modality-Weight Tuning via Tensor Decomposition-Based Unsupervised Feature Extraction

The paper introduces a tensor decomposition method for integrating CITE-seq RNA and ADT data without modality-weight tuning, showing competitive but not superior performance compared to existing methods.

Multi-scale modeling of human tissues from spatial transcriptomics with TERRA

TERRA is a foundation model for spatial transcriptomics that predicts tissue behavior under perturbation and identifies multi-scale biological insights across tissues.

Iterative co-creation of harmonized human and non-human primate cellular and structural ontologies and 3D common coordinate frameworks for the basal ganglia

The paper describes the iterative creation of harmonized ontologies and 3D frameworks for the basal ganglia across human and non-human primates to enable cross-species comparisons of cellular and molecular data.

A single-cell transcriptomic atlas of the periventricular proliferative zone in the late gestation fetal brain in the pigtail macaque

The paper constructs a single-cell RNA-Seq atlas of the periventricular proliferative zone in late-gestation pigtail macaque fetal brain, revealing cell states similar to human fetal brain.

A single-cell and multi-platform spatial atlas of the human pancreas resolves a transformation-associated epithelial axis and a recurrent boundary-organized tumour microenvironment

This paper integrates single-cell and spatial data from multiple pancreas conditions to identify a transformation-associated epithelial axis and a recurrent tumour microenvironment organization.

Do Geometric Outliers Identify Important Genes in Single-Cell Foundation Models?

This study evaluates whether geometric outliers in single-cell foundation models identify important genes, finding model-specific patterns without consistent biological significance.

Building optimized single-cell reference atlases with scAtlasTb

The paper introduces scAtlasTb, a framework for building and optimizing single-cell reference atlases, demonstrating its application on three Human Cell Atlas projects.

Reconstructing whole-organism cell phylogenies with resolved ancestral transcriptional states

This paper reconstructs whole-organism cell phylogenies with resolved ancestral transcriptional states in zebrafish larvae using a novel computational framework called LEAP.

An end-to-end framework for single-cell-resolution, whole-transcriptomic spatial profiling in post-mortem human brain

This paper presents an end-to-end framework for single-cell whole-transcriptome spatial profiling of post-mortem human brain tissue, improving library quality and enabling cross-sample harmonization.

Tissue-level division of labor is coordinated across organs through universal programs and context-specific adaptations

The paper identifies universal and tissue-specific transcriptional programs in human and mouse tissues using single-cell RNA sequencing and shows how they coordinate function across organs.

PROFET Predicts Continuous Gene Expression Dynamics from scRNA-seq Data to Elucidate Heterogeneity of Cancer Treatment Responses

hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data

hoodscanR identifies and analyzes cell neighborhoods in spatial transcriptomics data, revealing transcriptional differences in tumor cells across neighborhoods.

A unified analysis of cell-type and trajectory-associated pathways in single-cell data using Phoenix

Phoenix identifies cell-type and trajectory-associated pathways in single-cell data using random forest models and non-parametric testing, outperforming existing tools in capturing small pathway activities and revealing cross-species overlaps.

Reconstructing lineage-constrained gene-program dynamics with PhyloFM

PhyloFM reconstructs continuous cell-fate dynamics using lineage constraints from single-cell data, improving accuracy in multiple biological systems.

Improving RNA-seq protocols

The paper optimizes RNA-seq protocols to increase usable reads by 60% using a funnel strategy, improving cost-efficiency in bulk RNA-seq.

Multi-Sample and Multi-Group Spatial Colocalization Analysis Using PANORAMIC

PANORAMIC improves spatial colocalization analysis by accounting for within-sample uncertainty, revealing stronger immune cell colocalization in specific colorectal cancer subtypes.

SpaMTP: Integrative Statistical Analysis and Visualisation of Spatial Metabolomics and Transcriptomics data.

SpaMTP integrates spatial metabolomics and transcriptomics data through a comprehensive analysis pipeline and visualization tools.

CellTune: An integrative software for accurate cell classification in spatial proteomics

CellTune is a software tool for cell classification in spatial proteomics that outperforms existing methods with high accuracy and resolution.

Forecasting cell state futures from static snapshots

This paper introduces CellTempo, a framework that forecasts long-range cell state trajectories from static transcriptomic data, demonstrating its ability to predict lineage progression and distinguish perturbation responses from long-term fate changes.

Continual integration of single-cell multimodal data with MIRACLE

MIRACLE enables efficient continual integration of single-cell multimodal data, updating cell atlases with new data without reintegration, and identifies novel cell types and transfers labels across datasets.

DPCGS: a computational framework for linking GWAS to single-cell transcriptomics in complex traits and diseases

DPCGS links GWAS to single-cell transcriptomics to identify trait-associated cell subpopulations and regulatory programs with higher accuracy than existing methods.

SpatialFormer: Universal Spatial Representation Learning from Subcellular Molecular to Multicellular Landscapes

SpatialFormer integrates spatial and gene expression data to improve tasks like batch correction and cell-type annotation in spatial transcriptomics.

Finding stable clusterings of single-cell RNA-seq data

The paper introduces a method to assess clustering stability in single-cell RNA-seq data by comparing clusterings of subsets with the full dataset, finding that some clusterings are stable even with large datasets.

An integrated single-cell atlas of human lung across the lifespan

The paper constructs a single-cell atlas of the human lung across the lifespan, identifying new cell types and regenerative mechanisms linked to lung diseases.

Scale-Aware Compositional Inference Improves Reproducibility and Uncovers Convergent Aging Programs in Spatial Transcriptomics

This paper introduces a scale-aware framework for spatial differential expression that improves reproducibility and identifies convergent aging programs in aged mouse brains.

A pan-cancer single-cell atlas of pericytes

The paper constructs a pan-cancer single-cell RNA sequencing atlas of pericytes, revealing a common transcriptional program in tumour-associated pericytes and their specialised subtypes across multiple cancers.

Assessing Scale and Predictive Diversity in Models for Single-Cell Transcriptomics based on Geneformer

This study evaluates transformer-based models for single-cell transcriptomics, finding that larger datasets don't consistently improve downstream tasks and rank-based models struggle with batch effect correction.

Interpretable spatial ecological dynamics reveal ecological retention escape and amplification in pediatric leukemia

The paper develops a spatial ecological framework to analyze pediatric leukemia tissues, identifying ecological contexts and dynamics linked to disease persistence and relapse.

Custom Probe-Based Spatial Transcriptomics Enables Microbiome Detection in FFPE Colorectal Cancer Tissue

This study develops a custom probe-based spatial transcriptomics method for detecting microbiomes in FFPE colorectal cancer tissue, showing spatially patterned bacterial signals and preserving host transcriptomic structure.

Quantitative assessment of cell fate commitment in single-cell transcriptomics using scCS

The paper introduces scCS, a framework for quantifying cell fate commitment parameters in single-cell transcriptomics, using methods like DFFP and providing diagnostics and visualizations.