Single-cell and spatial transcriptomics

ssJSD improves scHiC clustering by integrating spatial interaction patterns and sparsity patterns to better distinguish cell types.

The paper investigates the impact of post-selection inference in scRNA-Seq phenotype testing and proposes batch correction to mitigate false discovery rate inflation.

The paper introduces MESIC, a method that maps gene expression data into an interpretable semantic space using gene annotations, enabling biological interpretation of clusters and outliers.

The paper introduces magniFIND-seq, a method for single-cell DNA and RNA analysis using digital PCR and commercial sorting instruments, enabling high-purity isolation of rare cell populations.

With an incomplete deconvolution reference, operator history changes estimates and associations, and the target is not point-identified; fitdrop scores remedies on shrinkage, coverage, certification and false certification, and prices measurements.

DeepSpaceDB 2.0 expands its database to include 1,539 public Xenium datasets, offering improved tools for interactive exploration and analysis of spatial transcriptomics data.

The paper introduces SPHERE-PCA, a method to interpret single-cell state transitions using spherical geometry, revealing structured patterns in developmental and disease datasets.

The paper develops a workflow to reduce autofluorescence in fresh-frozen human brain tissue for multiplexed spatial proteomics and characterizes autofluorescence as a spatially structured feature.

The paper constructs a lifespan single-cell atlas of immune cells, identifies an early-childhood immunometabolic transition around age 6, and develops an age-referenced framework for immune-state analysis.

This paper introduces SCENE, a probabilistic model for embedding cells and genes in scRNA-seq data, achieving state-of-the-art performance and interpretable visualization.

DELPHAI predicts heterogeneous perturbation responses by jointly training a fitness network and optimal transport network to identify differentially expressed genes and depleted cell lineages.

Locat identifies localized marker genes by testing both enrichment within and depletion outside cell populations in single-cell transcriptomics.

The paper introduces immgenT-GP, a gene-program framework that identifies 200 reproducible gene programs in mouse T cells, capturing lineage identity, activation states, and tissue location.

The paper presents a single nuclei RNA sequencing resource of dog brain cells, identifying cell types and revealing differences in opioid receptor expression compared to humans and mice.

The paper introduces methods to estimate and remove ambient contamination in scifi-ATAC data, improving the accuracy of chromatin profiles and biological discovery.

On 495 human Patch-seq neurons, cell type means predicted electrophysiology better than neural networks on scGPT embeddings or gene panels; fusing scGPT embeddings with cell type labels predicted best.

scACORN improves single-cell transcriptomic interpretation by combining specialized small language models with context-engineered agent orchestration, achieving higher accuracy and reducing unsupported gene citations.

FORGE is a configurable Nextflow pipeline for snMultiome analysis that integrates snRNA-seq and snATAC-seq data through latent-variable models and enables regulatory network inference and differential testing.

CellDot improves spatial transcriptomics by accurately decontaminating transcript assignments using optimal transport, enhancing biological interpretation and spatial pattern recovery.

The paper identifies and quantifies leakage in single-cell benchmarks caused by group-owned answers and introduces a tool to assess this leakage before model training.

DiffGSP reverses mRNA diffusion to improve spatial transcriptomics fidelity, revealing fine anatomical structures and spatial gene modules in various tissues.

LIGER2 improves single-cell data integration by enhancing scalability and efficiency while preserving biological variation.

The paper introduces spatialFDA, a framework for analyzing differential co-localisation in spatial omics data, showing its effectiveness in controlled simulations and biological applications.

ImmuneLens links transcriptional states and TCR clonotypes using disentangled multimodal learning to improve antigen-specificity prediction and reveal T cell heterogeneity in immunotherapy.

This study maps uterine remodeling postpartum using single-cell transcriptomics, revealing a novel homeostatic state that does not revert to pre-pregnancy cellular architecture.

LucaCell is a sequence-centric foundation model that improves cross-species single-cell analysis by using mRNA sequence embeddings instead of fixed gene identifiers.

The paper analyzes single-nucleus multi-omics data from mouse hypothalamus to reveal sex-specific aging differences, particularly on the X chromosome.

SpaCoEx selects sparse genes for spatially varying co-expression in spatial transcriptomics, improving spatial coherence and annotation agreement in cancer datasets.

This study uses snRNA-seq to analyze bleomycin-induced lung injury in mice, identifying dynamic changes and rare cell types not well captured by scRNA-seq.

The paper analyzes single-cell data from human tissues to show that aging involves both cell loss and architectural remodeling, with structural decline independent of cell composition.