Single-cell and spatial transcriptomics

GatorSC improves single-cell transcriptomic analysis by integrating multi-scale cell and gene graphs with a Mixture-of-Experts fusion framework, outperforming existing methods in clustering, imputation, and annotation.

This study uses spatial transcriptomics to show that therapy resistance in melanoma is shaped by both intrinsic cell states and the tumor microenvironment, with distinct resistance programs coexisting in tumors and associated with immune signatures.

The paper evaluates Xenium spatial transcriptomics performance, identifies technical limitations, and introduces SPLIT to improve signal purity and cell-type resolution.

SEBULA detects multiplets in snATAC-seq using a semi-parametric empirical Bayes model and integrates with scRNA-seq data, showing superior performance on benchmark datasets.

INSPIRE integrates and interprets multiple spatial transcriptomics datasets, revealing tissue structures and biological processes across diverse samples and technologies.

CANDIES denoises and integrates spatial multi-omics data using a conditional diffusion model and contrastive learning, improving downstream analysis tasks.

This paper introduces PRESENT, a contrastive learning framework for integrating spatially resolved multi-omics data across samples and modalities, showing improved performance in detecting spatially functional domains and biological regulatory mechanisms.

This paper introduces NTVE, a non-destructive transcriptomics method using vesicular export to monitor RNA dynamics in living cells over time.

SpaMosaic integrates spatial multi-omics data using contrastive learning and graph neural networks, improving spatial domain identification and missing modality imputation across diverse datasets.

The paper benchmarks sketching methods on spatial transcriptomics data, showing that spatially aware methods outperform others in preserving tissue structure and capturing rare cell states.

This paper develops a VAE-based framework to translate mouse brain gene expression to human, showing that mouse disease models predict human Alzheimer's and Parkinson's patterns and identify the best mouse model for AD patients.

SEAGALL uses geometry-aware graph attention networks to extract biologically relevant features from single-cell data, revealing cell identity drivers beyond marker genes.

CMDdemux improves single-cell demultiplexing accuracy by integrating hashing and mRNA data, outperforming existing methods on both high- and low-quality datasets.

The paper compares machine learning models for predicting single-cell protein expression from RNA-seq data to mRNA levels, finding that models outperform mRNA as a proxy.

This study classifies and maps cell types in normal human lymph nodes using high-dimensional spatial proteomics, revealing novel cell subsets and interactions.

This paper benchmarks spatial transcriptomics in stem cell-derived organoids using Stereo-seq, optimizing RNA capture and introducing regional analysis methods.

MUSTARD reduces dimensionality of multi-sample scRNA-seq data using trajectory guidance, revealing sample-level heterogeneity and associated gene markers.

CSsingle improves bulk and spatial transcriptomic data decomposition by integrating with scRNA-seq, showing better accuracy and revealing MCCs as potential biomarkers in EAC.

This study uses spatial transcriptomics to map subcellular mRNA localization across tissues, revealing conserved and tissue-specific patterns.

ProtoCloud improves cell type annotation by providing interpretable, uncertainty-aware predictions and better performance on rare cell types across multiple datasets.

SpatialFinder uses a vision-language model with human feedback to prioritize high-value regions in H&E slides for spatial transcriptomics, improving ROI selection accuracy.

Spatial transcriptomics of live healthy donor livers shows most hepatocyte genes are zonated, with key functions pericentrally shifted versus mouse, pig, boar and cow.

This study compares and optimizes methods for analyzing cell type neighbor preferences in spatial tissue data, introducing COZI which outperforms existing approaches.

SLIDE-EX predicts cell type-specific gene expression from breast cancer histopathology images, outperforming bulk RNA-seq deconvolution and slide-based models in chemotherapy response prediction.

QCatch provides a standardized QC reporting framework for single-cell sequencing data, generating interactive HTML reports and annotated H5AD files for downstream analysis.

The paper introduces a weighted averaging approach for differential expression analysis in single cell and spatial RNASeq to reduce false positives and negatives without assuming data distributions.

scDiffusion-X generates high-fidelity multi-omics data and enables modality translation with uncertainty quantification and gene regulatory network inference.

SpaPheno links spatial transcriptomics to clinical phenotypes using interpretable machine learning to identify predictive biomarkers for patient outcomes.

This paper develops computational tools for allelic imbalance analysis using single-nucleus RNA-seq and demonstrates their application in a Parkinson's disease cohort.

Hydra is a deep generative framework for single-cell omics data that improves cell type annotation and feature selection, particularly for rare populations.