Single-cell and spatial transcriptomics

Gravlax: an annotation-independent molecular evidence archive for single-cell RNA-seq

Gravlax archives single-cell RNA-seq data in a compact format that preserves molecular evidence, enabling reanalysis under new annotations and discovery of unannotated features.

A single-cell RNA-seq catalog of ground truth gene coregulation

The paper curates a single-cell RNA-seq catalog with known coregulation relationships to evaluate normalization methods for identifying gene coregulation.

CDSeq2 integrates individual-level cell-type expression deconvolution with recoverability-guided prioritization

CDSeq2 improves expression deconvolution and eQTL analysis by integrating Bayesian deconvolution with prioritization of recoverable profiles in bulk RNA-seq data.

stEDGE enables edge-guided multiscale reconstruction of hierarchical spatial domains and transition interfaces in spatial transcriptomics

stEDGE reconstructs hierarchical spatial domains and transition interfaces in spatial transcriptomics by using edge-guided methods to capture multiscale organization beyond flat partitions.

celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool

celltypeEnrich annotates scRNA-seq clusters using consensus enrichment from 26 reference datasets, achieving 62-72% accuracy across three tissues and two species.

Detecting cell segmentation errors using doublet methods

The paper evaluates doublet detection methods for identifying segmentation errors in spatial transcriptomics by simulating molecular admixture between neighboring cells.

Poly Pipeline: A Polyvalent Spatial Transcriptomics Workflow Validated Across Polyploid and Diploid Organisms

Poly Pipeline is a spatial transcriptomics workflow validated across polyploid and diploid organisms, demonstrating effectiveness in clustering and co-expression network analysis.

Multi-gene biomarkers reveal spatial organization and subpopulation-specific damage response in intrahepatic biliary epithelial cells

The paper identifies BEC subpopulations and their spatial organization using scRNA-seq and metagenes, revealing subpopulation-specific damage responses in mouse liver.

Single-nucleus profiling of social behaviour in a mutualistic reef fish

This study maps the telencephalon transcriptome of cleaner wrasse to identify conserved neural cell populations and gene networks involved in social behavior.

CUBE: Multimodal Representation Learning Reveals Biological Structure Across Histomorphology, Spatial Protein Phenotypes, and Transcriptome-Associated Signals

CUBE integrates spatial protein and transcriptomic data via histology, creating a multimodal representation that captures biological structure from incompletely paired data.

SMORE: joint dimension reduction and cell population discovery on single-cell methylome data

SMORE performs joint dimensionality reduction and cell population discovery on single-cell methylome data, accurately recovering population structures in human datasets.

Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes

This study shows that single-cell experiments often lack power to detect small-effect genes, even with 600 donors, and highlights the impact of sequencing depth and cell counts on results.

ssJSD: A fusion of sparsity and spatial information for HiC single-cell clustering

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

Post-selection inference in testing for phenotypic differences with scRNA-Seq

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

Mapping Gene Expression to an Interpretable Semantic Space

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.

Single-cell DNA cytometry with magnetic- and fluorescence-activated bead sorting

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.

ConvexGating infers gating strategies from clusters in single cell cytometry data

ConvexGating automatically learns unbiased, interpretable gating strategies for flow cytometry data, improving consistency and reducing contamination in cell population identification.

Incomplete references leave bulk deconvolution targets non-identifiable, but identification has a computable precision price

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: an interactive spatial transcriptomics database for large-scale Xenium data exploration

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.

Interpretable spherical geometry of single-cell state transitions from dominant principal components

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

Spatial organization and mitigation of autofluorescence in multiplexed spatial proteomics of aged fresh-frozen human brain

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.

A lifespan-scale single-cell atlas defines an early-childhood immunometabolic transition linked to age-referenced immune states

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.

Setting the SCENE for Interpretable Cell-Gene Embeddings in Single-Cell RNA-seq

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.

Spatial Organellomics Maps Cell State Diversity and Metabolic Adaptation in Tissues

This paper introduces sOrganellomics, a method to map cell state diversity and metabolic adaptation in tissues using spatial imaging and machine learning, revealing new insights into hepatocyte organization and metabolic changes under stress.

DELPHAI predicts heterogeneous perturbation responses with learned single-cell fitness

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: Joint enrichment and depletion testing identifies localized marker genes in single-cell transcriptomics

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

A Gene-Program Architecture of Mouse T cells

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.

Post-hoc long-read sequencing links leukemic mutation status to single-cell transcriptomes

The paper develops a post-hoc method to link mutation status to single-cell transcriptomes using long-read sequencing, showing it can detect BCR::ABL1-positive cells in chronic myeloid leukemia.

A single nuclei expression resource for exploring dog brain cell transcriptomic diversity

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.

Empirical Estimation of Ambient Contamination in Combinatorial Single-Cell Methods Using Multi-Reference Mapping

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