Single-cell and spatial transcriptomics

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

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

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.

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.

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.

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.

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.

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

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

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

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

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

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.

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.

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

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

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.

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

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

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

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.

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

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.

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

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

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