Single-cell and spatial transcriptomics

Benchmarking niche identification via domain segmentation for spatial transcriptomics data

The paper benchmarks domain segmentation algorithms for spatial transcriptomics data, showing they fail to identify biologically defined niches due to noise and highlights the need for specialized methods.

Limitations of differential expression for cell-type marker discovery in single-cell RNA sequencing atlases

The paper shows that differential expression methods like Wilcoxon rank-sum test produce unreliable cell-type markers due to cluster size dependence, and proposes a classification-based alternative with better specificity.

Geometric inference of cross-species transcriptome correspondence using Gromov-Wasserstein optimal transport

Species-OT uses Gromov-Wasserstein optimal transport to compare transcriptome geometries across species, identifying gene correspondences and transcriptomic discrepancies.

Octave: Scale-resolved Evaluation of Spatial Gene Expression Prediction from Histology

The paper introduces OCTAVE, a scale-resolved evaluation method for spatial gene expression prediction from histology, showing that fine-scale differences are missed by standard metrics.

A Metacell Model of Single Cell RNA-seq Counts Yields a Gaussian Mixture Model in PCA Space

The paper shows that applying a metacell model to scRNA-seq data results in PCA embeddings that follow a Gaussian mixture model, but underestimates variance in real datasets due to unaccounted gene correlations.

SFUMATO: Bayesian probabilistic clustering for uncertainty-aware spatialtranscriptomics analysis and mapping

SFUMATO provides a Bayesian probabilistic framework for spatial transcriptomics that captures uncertainty and enables continuous visualization of transcriptional domains.

Rank-Preserving Alignment Enables Cross-platform Learning and Phenotyping for Single-Cell and Spatial Proteomics

RAMP improves cross-platform proteomics analysis by preserving protein expression order, enhancing cell-type annotation and prediction across multiple cancer types.

SCITRAM: a Single-cell Integrated transcription regulation modeler for predicting master transcription factors

SCITRAM predicts master transcription factors from single-cell transcriptomics data by reconstructing gene regulatory networks and modeling transcriptional cascades.

Lineage-informed factor analysis reveals heritable programs of single-cell gene expression

The paper introduces scPFA, a lineage-informed factor analysis method, to uncover heritable gene expression programs from single-cell lineage-tracing data.

Microchimerism in the human brain, quantitative assessment and single nuclei profiling establish cell types and diversity

The paper quantifies maternal microchimerism in the human brain using snRNA-seq and identifies diverse cell types across age groups.

Pretrained gene representations transfer mean expression more broadly than spatial patterns in virtual spatial transcriptomics

This study evaluates how pretrained gene representations contribute to virtual spatial transcriptomics by separating their effects on mean expression and spatial patterns across multiple datasets.

Interpretable models for scRNA-seq data embedding with multi-scale structure preservation

This paper introduces two tools for improving the trustworthiness of single-cell dimensionality reduction by enhancing quality metrics and multi-scale structure preservation.

Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments

Deep Lineage: Single-Cell Lineage Tracing and Fate Inference Using Deep Learning

Deep Lineage uses deep learning to analyze time-series single-cell RNA-sequencing with lineage-tracing data to predict cell fate and gene expression profiles more accurately than existing methods.

Quantifying distribution shifts in single-cell data with scXMatch

scXMatch quantifies distribution shifts between single-cell data from different conditions using a non-parametric graph-based test, showing robust performance across gene expression, chromatin accessibility, and morphology data.

Generative modeling reveals the connection between nuclear morphology and gene expression

COSMIC, a generative model, quantifies the relationship between nuclear morphology and gene expression using paired single-cell data, revealing morphology-associated genes in prostate cancer and spatial transcriptomics of zebrafish embryos.

THE MICROANATOMY OF INTRACRANIAL HUMAN THROMBI BY SINGLE NUCLEI TRANSCRIPTOMICS

The paper uses single-nuclei RNA sequencing to analyze intracranial thrombi, identifying distinct cell populations and transcriptional changes, and inferring ligand-receptor interactions relevant to stroke.

Joint recording of past and present transcriptomes reconstructs single-cell trajectories

This paper introduces scReMeMber-Seq, a method to reconstruct single-cell trajectories by linking past and present transcriptomes using methylation-based memory recording.

Multiscale harmonization, semantic integration, and immersive exploration of single-cell data in support of novel biological insights

A free Meta Quest virtual reality app harmonizes CODEX, Visium, Xenium, volumetric immunofluorescence and electron microscopy data from 5 organs into 4 scale levels using the Human Reference Atlas.

ProxiNet transfers spatially learned cellular proximity to dissociated single-cell transcriptomes

ProxiNet transfers spatially learned cellular proximity from spatial transcriptomics to dissociated single-cell transcriptomes, revealing tissue organization and age-related changes in Alzheimer's.

Multi-Model Machine Learning Consensus Identifies a Dual-Interferon Gene Signature in Ischemic Stroke: An Integrative Single-Cell Transcriptomic Analysis

This paper uses a multi-model consensus framework to identify a dual-interferon gene signature in ischemic stroke using single-cell RNA sequencing data from a mouse model.

MechanoMaST - a multimodal pipeline for spatially registering mechanical and transcriptomic tissue data

MechanoMaST integrates mechanical and transcriptomic data from tissue samples to enable spatial correlation with high accuracy, revealing a stiffness-related gene signature in colorectal cancer.

Accurate and scalable demultiplexing of single-cell RNA sequencing using BEACON

BEACON improves demultiplexing accuracy in single-cell RNA sequencing by learning background counts, outperforming existing methods and enabling new biological insights.

CycloCross: Adversarial single-cell RNA-seq data translation across species

Translating cells between mouse, macaque and human scRNA-seq with a CycleGAN, without ortholog mapping, preserved developmental time structure, imputed missing states and produced more realistic synthetic cells than scGAN.

DECIPHER integrates disentangled representation learning and prototype-based cell-type deconvolution across molecular modalities

DECIPHER improves cell-type deconvolution across molecular modalities by combining disentangled representation learning with prototype-based methods, showing robust performance in simulated and real datasets.

CHACAM: a cell-cell interaction-guided hierarchical attention model for high-precision cell identity annotation of scRNA-seq data in early C. elegans embryogenesis

CHACAM improves cell identity annotation in C. elegans scRNA-seq data by integrating cell-cell interactions and gene expression to resolve ambiguous lineages.

Platform-specific count-matrix preprocessing workflows for spatial transcriptomics data analysis

This study benchmarks preprocessing workflows for spatial transcriptomics data across multiple platforms and identifies platform-specific recommendations to improve downstream analysis.

Alcohol Use Disorder and Smoking-Associated Molecular Alterations in Human Prefrontal Cortex in Single Nucleus (sn) RNA-Seq

The paper identifies molecular alterations in the prefrontal cortex of individuals with alcohol use disorder and smoking, revealing distinct and combined pathway changes across cell types.

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

GeneSPOT is a gene-centric framework that characterizes spatial gene relationships and their reorganization across biological conditions and time.

Integrating morphology and gene expression of neural cells in unpaired single-cell data using GeoAdvAE

GeoAdvAE integrates unpaired single-cell morphology and transcriptomics to align cell types and reveal disease-associated microglia signatures.