Single-cell and spatial transcriptomics

ResolVI - addressing noise and bias in spatial transcriptomics

ResolVI improves spatial transcriptomics by correcting molecule misassignment and batch effects, enhancing cell state distinction and cross-dataset integration.

singIST: an R/Bioconductor library and Quarto dashboard for automated single-cell comparative transcriptomics analysis ofdisease models and humans

singIST compares disease model scRNA-seq data to human references to assess recapitulation at superpathway, cell type, and gene levels.

A Counterfactual Framework for Directional Cell-Cell Interaction Analysis in Spatial Transcriptomics

This paper introduces a counterfactual framework to analyze directional cell-cell interactions in spatial transcriptomics, identifying significant asymmetric influences between tumor, immune, and stromal compartments in cholangiocarcinoma tissue.

Fast, flexible analysis of differences in cellular composition with crumblr

Crumblr is a statistical method for analyzing changes in cell type composition across studies, showing increased power over existing methods in simulations and applied to multiple single-cell RNA-seq datasets.

Spatial Transcriptomics Reveal Developmental Dynamics of the Human Cerebral Cortex and Striatum

This study uses spatial transcriptomics and snRNA-seq to identify 25 forebrain regions and characterize dynamic changes in the human fetal cortex and striatum during late first and early second trimesters.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

The paper introduces dreamlet, an R package for efficient differential expression analysis of large-scale single-cell transcriptomics data, showing improved speed and memory usage compared to existing methods.

Single-nucleus transcriptome-wide association study of human brain disorders

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

Single-Nucleus Atlas of Cell-Type Specific Genetic Regulation in the Human Brain

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

OT-knn: a neighborhood-aware optimal transport framework for aligning spatial transcriptomics data

OT-knn aligns spatial transcriptomics data by integrating local neighborhood information within an optimal transport framework, achieving accurate alignment across diverse datasets and conditions.

Fishash: A contingency table approach to Perturb-seq guide assignment

Fishash assigns perturbations to cells in Perturb-seq data using Fisher's Exact Test, improving accuracy and speed over existing methods.

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.

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.

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.

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.

Towards foundation models that learn across biological scales

The paper introduces Xpressor, a framework for cross-scale learning in biology, improving cell-type prediction and embedding quality.

Determining the age of single cells using scMLEAge

The paper develops a Bayesian statistical framework to predict single-cell age from transcriptomic profiles and demonstrates its accuracy on Tabula Muris Senis data.

A Single-Cell Atlas of the Mouse Dural Meninges Reveals Pervasive Sex Differences Across Cellular Compartments

This study creates a single-cell transcriptomic atlas of the adult mouse dural meninges, revealing widespread sex differences across cell types, communication, and disease-related features.

Analysis of multicellular anatomical structures from spatial omics data using sosta

The paper introduces a method for analyzing multicellular anatomical structures from spatial omics data and provides an open-source tool for this purpose.

Breast cancer through the lens of whole transcriptome spatial imaging

The paper uses spatial transcriptomics to analyze a breast tumor, identifying spatially distinct biological processes and potential therapeutic targets.

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

The paper benchmarks 13 models for predicting single-cell perturbation responses across 25 datasets, finding performance varies with perturbation effect size and evaluation metric.

Impact of single-cell RNA reference selection for the deconvolution of breast cancer spatial transcriptomics datasets

This study evaluates how reference selection affects deconvolution accuracy in breast cancer spatial transcriptomics datasets, finding that small references can be effective and that perfect reference-ST matching improves results.

Reconstructing signaling histories of single cells via perturbation screens and transfer learning

The paper develops a framework combining perturbation screens and transfer learning to reconstruct signaling histories in cells and optimize differentiation protocols.

Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data

This paper introduces wSIR, a scalable supervised dimensionality reduction method for spatial transcriptomics data, which retains predictive power of spatial coordinates and is demonstrated on breast cancer and embryonic development datasets.

Scalable, Generalizable, and Uncertainty-Aware Integration of Spatial Multi-Omics Across Diverse Modalities and Platforms with SCIGMA

SCIGMA integrates spatial multi-omics data across modalities and platforms using a deep learning framework with uncertainty-aware contrastive learning, outperforming existing methods in domain detection and modality preservation.

Velociraptor: Cross-Platform Quantitative Search Using Hallmark Cell Features

Velociraptor identifies and searches for novel cell types across platforms using hallmark feature sets, revealing new biological insights in glioblastoma and immunology.

Systematic evaluation of robustness to cell type mismatch of deconvolution methods for spatial transcriptomics data

This study evaluates how deconvolution methods for spatial transcriptomics data perform when cell types are missing from reference datasets, finding that performance decreases with the number of missing cell types.

HESTIA: Scalable Multimodal Integration of Histology and High-Resolution Spatial Transcriptomics for Robust Spatial Domain Identification

HESTIA integrates histology and spatial transcriptomics to identify spatial domains in large-scale data, improving clustering accuracy and mapping tumor heterogeneity.

A Single-cell Spatiotemporal Manifold of Tissue Morphology and Dynamics

This paper introduces a learned single-cell spatiotemporal manifold to analyze tissue morphology and dynamics over time.

Halo: a pretrained model for whole-cell segmentation from nuclei images in spatial transcriptomics

Halo improves whole-cell segmentation in spatial transcriptomics by integrating nuclear morphology and RNA transcript distribution, outperforming existing methods across diverse tissues.