Single-cell and spatial transcriptomics

Differential Expression Analysis for Longitudinal Single-Cell RNA-Sequencing Studies Using REBEL

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.

Reconstructing True 3D Spatial Omics at Single-Cell Resolution

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: a robust framework for simulating and detecting time-dependent single-cell dynamics in longitudinal studies

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: inferring DNA content from amplification-free single-cell WGS using fragment overlaps

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

Predicting Pre-treatment Resistance or Post-treatment Effect? A Systematic Benchmarking of Single-Cell Drug Response Models

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.

Entropy Sorting Feature Selection: information-theoretic gene set identification improves single-cell RNA sequencing data interpretability

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

Spatial transcriptomic profiling of decalcified murine musculoskeletal samples via Xenium Prime 5K

This paper develops a sample processing pipeline for spatial transcriptomics of murine musculoskeletal tissues, achieving high-quality transcript detection across multiple tissue types using the Xenium Prime 5K platform.

Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

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.

Linking live-cell behavior to transcriptional responses across perturbations using dynamic caging

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

Spatial imprints of emergent cardiomyocyte states in the pressure-overloaded heart

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

AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from Histopathology

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

High-resolution single-cell atlas of the human B cell compartment and immune microenvironment across tissues

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.

A Cross-Species Generative Cell Atlas Across 1.5 Billion Years of Evolution: The TranscriptFormer Single-cell Model

The paper introduces TranscriptFormer, a generative model trained on 112 million cells across 12 species, demonstrating cross-species cell type classification and disease state prediction.

STAT: A multi-agent framework for integrated and interactive spatial transcriptomics analysis

STAT is a multi-agent framework that improves spatial transcriptomics analysis by enabling interactive, transparent, and efficient multi-task analysis across different data resolutions.

geneSync: Gene Symbol Harmonization for Large-scale RNA-seq Data Integration

geneSync harmonizes gene symbols across RNA-seq datasets to improve integration quality by resolving nomenclature inconsistencies.

Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing

SCOTCH improves isoform-level gene expression characterization using long-read single-cell RNA sequencing by refining sub-exon boundaries and reducing false positives.

Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust

ACE-OF-Clust improves single-cell clustering by aligning results across methods and identifying informative features in scRNA-seq and spatial transcriptomics data.

MUSE enables cross-species multi-omics integration that incorporates transcriptional regulatory modules

MUSE integrates multi-omics data across species to align cellular states while preserving species-specific features and regulatory module conservation.

A Single-Cell Peripheral Immune Atlas Spanning High-Risk Lesions to Invasive Breast Cancer in Black and White Women

The paper constructs a single-cell peripheral immune atlas for Black and White women across breast disease states, identifying race-associated immune differences linked to chronic inflammation and immune aging in Black patients.

EssentCell: Discovering Essential Evolutionary Relations in Noisy Single-Cell Data

EssentCell identifies essential evolutionary relations in noisy single-cell data by solving a minimum-flip problem with false positive constraints.

Computational Identification of Migrating T cells in Spatial Transcriptomics Data

This study introduces ReMiTT, a computational method to identify T cell migration trails in spatial transcriptomics data, revealing chemokine trends and enriched pathways associated with T cell movement in tumors.

CAPTAIN: A multimodal foundation model pretrained on co-assayed single-cell RNA and protein

CAPTAIN is a multimodal foundation model trained on single-cell RNA and protein data, improving multi-omics integration and uncovering protein-driven intercellular dynamics.

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Phoenix predicts spatially resolved single-cell gene expression from histology, identifying new biomarkers and validating them across cancers and treatment regimens.

Striping artifact removal in VisiumHD data through nuclear counts modeling

The paper introduces a statistical destriping method for VisiumHD spatial transcriptomics data using nuclear counts modeling, improving stripe-factor estimation and preserving biological signal.

Building computational benchmarks: an Omnibenchmark reimplementation of a single-cell preprocessing pipeline evaluation

The paper evaluates five single-cell RNA sequencing pipelines using Omnibenchmark, highlighting differences in parameter implementation across tools.

cellNexus: Quality control, annotation, aggregation and analytical layers for the Human Cell Atlas data

cellNexus enhances Human Cell Atlas data by adding quality control, metadata, normalization, and analysis layers to enable multi-study analyses and large-scale biological discovery.

CRAK-Velo: Chromatin Accessibility Kinetics integration improves RNA Velocity estimation

CRAK-Velo improves RNA velocity estimation by integrating chromatin accessibility data, enabling accurate cell-type deconvolution and insights into gene-chromatin interactions.

Single-cell spatial multiomics identifies POSTN+ CAFs mediating chemoradiotherapy resistance in rectal cancer

The paper identifies POSTN-expressing cancer-associated fibroblasts (CAFs) linked to chemoradiotherapy resistance in rectal cancer using spatial multi-omics data.

anndataR improves interoperability between R and Python in single-cell transcriptomics

anndataR enables interoperability between R and Python by allowing native reading and writing of H5AD files in R, with compatibility testing and conversion to R single-cell objects.

Cell Type Weighted Dimensionality Reduction

This paper introduces CWDR, a dimensionality reduction method that improves visualization of cell types by incorporating lineage information, reducing overlap between clusters in high-dimensional data.