Single-cell and spatial transcriptomics

Coordinated dysregulation of modular gene activity in human neuropathologies

The paper introduces CoPA, a method to identify dysregulated gene coexpression modules in specific cell types across neuropathologies, finding reproducible dysregulation in Alzheimers and schizophrenia.

LRSPAT: A low-rank framework for spatial omics statistics

LRSPAT is a low-rank framework for spatial omics statistics that improves speed, memory efficiency, and accuracy in identifying spatially variable genes in high-resolution spatial transcriptomics data.

seqproc: An efficient, flexible, and concise tool for sequence geometry description and transformation

seqproc is a tool that efficiently compiles sequence-geometry descriptions into execution graphs, outperforming existing tools in speed and memory usage for single-cell RNA-sequencing protocols.

mmVelo: A deep generative model for estimating cell state-dependent dynamics across multiple modalities

mmVelo estimates cell state-dependent dynamics across multiple modalities using deep generative modeling, validated on mouse and human data.

COBRA: Cell-type-specific Orthogonal Batch effect Removal Algorithm in single cell RNA-sequencing data

COBRA removes cell-type-specific batch effects in scRNA-seq data, preserving biological differences and improving integration across datasets.

Spatial transcriptomics reveals site-specific cellular and metabolic heterogeneity in bladder carcinoma in situ

This study uses spatial transcriptomics to show that bladder carcinoma in situ has spatially structured cellular and metabolic heterogeneity within a single patient.

Visual LLM-guided consensus spatial domain detection with L-STAR

L-STAR improves spatial domain detection by combining visual LLM guidance with consensus-based integration of multiple methods across diverse datasets.

Closing the fusion-detection gap in single-cell RNA-seq with a scalable, probe-based workflow

The paper introduces a probe-based workflow to detect oncogenic gene fusions in single-cell RNA-seq, validated in cancer cell lines and pediatric B-ALL cohorts.

Decoding the Transcriptome Dark Matter: Construction of Single-Cell Whole-Transcriptome Regulatory Atlas by dropTotal

The paper introduces dropTotal, a method for single-cell total RNA-seq that detects both coding and non-coding transcripts, and applies it to human glioma nuclei to uncover ncRNA-driven regulatory landscapes.

fastCNV: Fast and accurate copy number variation prediction from High-Definition Spatial Transcriptomics and scRNA-Seq Data

fastCNV predicts copy number variations from spatial transcriptomics and scRNA-seq data, showing high accuracy and speed improvements over existing methods.

nf_xpatial: A Reproducible Framework for Standardized Preprocessing and Clustering of Xenium Data

nf_xpatial provides a standardized Nextflow pipeline for preprocessing and clustering Xenium spatial transcriptomics data, enabling reproducible analysis and parameter comparison.

Cross-chemistry single-nucleus RNA-seq identifies gene length and CpG-island promoters as determinants of transcriptional noise.

The paper compares two scRNA-seq platforms to quantify transcriptional noise and identifies gene length and CpG-island promoters as factors influencing noise levels.

scLANTERN: High-Throughput Retrospective Lineage Tracing via Full-Length Single-Cell Transcriptomics and Expressed Repeat Variation

This paper introduces scLANTERN, a method using full-length single-cell transcriptomics and expressed repeat variation to enable high-throughput retrospective lineage tracing in cancer.

scProtoTransformer: Scalable Reference Mapping Across Molecules, Cells and Donors

scProtoTransformer provides a scalable reference mapping framework across molecular, cellular, and donor levels using a prototype-based Transformer model with improved interpretability and performance.

Design and Assembly of Combinatorial DNA Barcodes for Probe-based Genomics Applications

The paper introduces a framework for designing and assembling combinatorial DNA barcodes for probe-based genomics, demonstrating their use in decoding complex RNA expression in mouse brains.

Conserved principles of spatial biology define tumor heterogeneity and response to immunotherapy

This study identifies conserved spatial structures in tumors that predict immune response, using a classification framework based on transcriptional covariation.

G2T: Tissue Reconstruction from Gene Expression via Embedding-Distance Flow Matching

G2T reconstructs tissue geometry from gene expression data using a generative deep learning model, outperforming existing methods on spatial reconstruction benchmarks.

A Scalable Cross-Modal Workflow for Linking In Vivo Neural Activity to Neuronal Cell Types via Spatial Gene Profiling

This paper presents a cross-modal workflow linking in vivo neural activity to molecular cell types using spatial transcriptomics, achieving accurate alignment across thousands of neurons in the mouse dorsal hippocampus.

HIDE-Deconv: A hierarchical deconvolution framework for multiscale characterization of cellular remodeling

HIDE-Deconv improves deconvolution by analyzing cellular composition across multiple resolution levels, revealing hidden cellular remodeling in diseases.

Benchmarking Imputation Methods for Single-Cell RNA Sequencing Data Using Peripheral Blood Mononuclear Cells from Acute Myocardial Infarction Patients

The paper benchmarks imputation methods on scRNA-seq data from AMI patients to assess their impact on downstream analysis.

dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data

dioscRi improves transferability of deep learning models for cytometry data by integrating normalization, cell type hierarchies, and clinical outcome prediction.

BatchRefiner: fast, significant improvement in batch integration of single-cell embeddings with ensemble refinement

BatchRefiner improves batch integration of scRNA-seq and scATAC-seq embeddings by ensemble refinement, showing significant performance gains across datasets and metrics.

A mechanism-annotated benchmark reveals limited fidelity to drug-response signatures in single-cell perturbation models

This paper introduces a benchmark to evaluate single-cell drug perturbation models by assessing their fidelity to drug-response signatures, finding that expression similarity is a poor proxy for mechanism preservation.

Uni-TINT: Unifying T and Innate Lymphoid Cell Taxonomy with a high resolution, pan-disease and pan-tissue single-cell transcriptomic atlas

Uni-TINT creates a unified single-cell atlas of T and innate lymphoid cells across diseases and tissues, identifying new cell subtypes and functional associations.

scDisent: regulatory-aware disentangled representation learning for multi-omic single-cell analysis

scDisent separates regulatory and expression variables in multi-omic single-cell data, improving integration and interpretability.

Robustness to nuisance perturbations enables unsupervised evaluation of single-cell foundation models

This paper evaluates single-cell foundation models using an unsupervised framework that assesses their robustness to nuisance perturbations, revealing significant differences in structural stability between models ranked similarly by standard benchmarks.

Spatial mapping of pediatric brain tumors across diagnoses and relapses

The paper creates a spatial transcriptomic atlas of pediatric brain tumors, identifying spatial archetypes and relapse-associated clones across multiple diagnoses.

STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes

STcompare identifies differentially spatially patterned genes by comparing spatial correlation and fold-change across structurally matched tissues in spatial transcriptomics data.

Hypothalamic Cell Type Evolution across Vertebrates

This study identifies conserved and clade-specific hypothalamic cell types across vertebrates using single-cell transcriptomics, revealing conserved transcription factors and rewired regulatory linkages.

CytoGate-Bench: an LLM benchmark for cross-panel cell gating in cytometry

CytoGate-Bench evaluates LLMs for cross-panel cell gating in cytometry by reformulating manual gating as a zero-shot task using expert-annotated data.