Single-cell and spatial transcriptomics

Tribus is a semi-automated tool for cell phenotyping from multiplexed imaging and proteomic data, achieving high accuracy and efficiency compared to existing methods.

The paper uses spatial transcriptomics and computational methods to show that vision affects both cell state and cell type composition in the visual cortex during critical periods.

The paper compares enhancer codes of cell types between mammalian neocortex and avian pallium using deep learning, finding higher divergence in excitatory neurons than other cell types.

This paper introduces DEFND-seq, a method for jointly profiling DNA and RNA from individual nuclei, enabling analysis of gene expression with copy number and SNV associations.

mitoXplorer 3.0 provides new tools for analyzing mitochondrial variability in single-cell RNA-seq data, with applications in understanding mitochondrial dysfunction in disease.

paraCell provides an interactive tool for analyzing and visualizing host-parasite single-cell RNA-Seq data without requiring programming skills.

SpatialLeiden integrates spatial embeddings into Leiden clustering to create a spatially aware method that performs well compared to existing spatial clustering techniques.

scRecover imputes dropout zeros in scRNA-seq data while preserving true zeros, improving downstream analysis through accurate zero discrimination.

The paper introduces a genetic barcoding method for single-cell transcriptomic studies in Drosophila to improve cell type identification and experimental tracking.

MGPfactXMBD is a model-based manifold-learning framework for scRNA-seq data that identifies bifurcating transcriptional modules underlying cell fate determination, outperforming existing methods in trajectory inference and biological interpretation.

This paper introduces an attention-based deep neural network to detect cis-regulatory elements at the single-cell level using multi-omics data, showing improved accuracy and cell state clustering.

Phospho-seq integrates phosphorylated protein profiling with scATAC-seq and scRNA-seq to study cell signaling and gene regulation in single cells.

PASSAGE identifies phenotype-associated spatial signatures in spatial transcriptomics data using a deep learning framework.

INSTINCT integrates multi-sample spatial chromatin accessibility sequencing data by removing batch effects and noise, enabling comprehensive analysis of epigenomic landscapes.

spaTrack infers spatial cell trajectories using optimal transport, revealing spatiotemporal dynamics in regeneration and tumor development.

STALocator localizes single cells to spatial transcriptomics data, improving spatial reconstruction and gene expression analysis in multiple datasets.

The paper develops CLEANSER, a model to filter ambient gRNAs in single-cell CRISPR screens, improving gRNA-cell assignment accuracy and differential gene expression analysis.

SCEMENT integrates large-scale single cell RNA-seq data efficiently, outperforming existing methods in speed and memory usage while preserving quantitative gene expression information.

GBMPurity is a deep learning tool that estimates glioblastoma tumour purity from bulk RNA-seq data, outperforming existing methods and revealing subtype-specific purity patterns.

The paper introduces FAST, a reference-free deconvolution method for spatial transcriptomics data that integrates gene expression, spatial coordinates, and histology information, outperforming existing methods in simulations and real data.

uniLIVER creates a human liver cell atlas using sc/snRNA-seq data and develops LiverCT to map abnormal datasets to the reference atlas, revealing changes in cell type composition and states in liver cancer.

This study shows that scRNA-seq on FFPE tissue is suitable for analyzing breast cancer, identifying a subpopulation of multi-ciliated cells in invasive ductal carcinomas.

STAIG integrates spatial transcriptomics data with histological images using graph-contrastive learning to analyze spatial domains without prealignment.

DeST-OT aligns spatiotemporal transcriptomics data from developmental timepoints using optimal transport to model cell growth, death, and differentiation.

The paper introduces spatial-Mux-seq, a multi-modal spatial technology that profiles five modalities in tissues, revealing new insights into cell types, gene regulation, and epigenetic gradients in mouse embryos and brains.

This study evaluates the HIVE scRNA-seq method for processing equine BAL cells, finding it identifies major cell types but underrepresents T cells and overrepresents macrophages, with potential for improvement.

The paper analyzes single-cell spatial transcriptomes of the macaque cortex to reveal non-random neuronal distribution and collaborative clustering patterns.

This paper introduces bacterial-MERFISH, a method to profile bacterial transcriptomes spatially, and applies it to study E. coli and B. thetaiotaomicron under different conditions.

This paper introduces GASTON, an interpretable deep learning method to map spatial gene expression topography, identifying gradients and domains in spatial transcriptomics data.