Single-cell and spatial transcriptomics

DelSIEVE infers cell phylogeny and variants from scDNA-seq data, improving detection of deletions and evolutionary events compared to existing methods.

Harp improves deconvolution accuracy by harmonizing cell reference profiles and overcoming batch effects in bulk gene expression data.

The paper introduces a method to study cell-cell interactions at single-cell resolution, revealing unique transcriptomic signatures in T cell-monocyte complexes during TB and dengue infection.

SpaIM improves spatial transcriptomics by imputing missing gene expressions using scRNA-seq data, outperforming 12 existing methods across 53 datasets.

This study creates a spatial single-cell atlas of the claustro-insular region in mice, revealing distinct neuronal subtypes and identifying Nr4a2 as a key regulator of neuronal identity and excitability.

GraphVelo extends RNA velocity inference to multimodal data by using graph-based machine learning to preserve vector information across different data representations.

The paper integrates single-cell proteomics and transcriptomics to study human hematopoietic stem and progenitor cells, identifying protein markers and dynamics not captured by mRNA alone.

CytoSpatio models spatial relationships between cell types across tissues using multirange, multitype point processes and generates synthetic tissue structures.

The paper introduces a spatial dissimilarity method to analyze single-cell and spatial transcriptomics data, identifying alternative splicing and allele-specific gene expression in various cell types.

GENESIS converts Multiome GEX data into synthetic scRNA-Seq profiles using generative models, improving cell-type classification accuracy.

TrimNN identifies spatial cell organization patterns from spatial transcriptomics and proteomics data to reveal associations between cell-type arrangements and phenotypes.

scClassify2 is a message-passing neural network for cell state identification, outperforming existing methods in annotating adjacent cell states and applicable to multiple data types.

The paper creates a comprehensive human cortical atlas using single-cell and spatial transcriptomics data to reveal cellular organization and functional correlations in the cerebral cortex.

This paper introduces a method for high-plex spatial transcriptomics using dual probe ligation and rolling-circle amplification to analyze FFPE tissues.

Singletrome enhances scRNA-seq analysis by integrating lncRNA annotations, improving mapping specificity and enabling lncRNA-based cell type clustering and disease prediction.

spVelo infers RNA velocity in multi-batch spatial transcriptomics data, showing improved velocity consistency, transition accuracy, and direction correctness compared to existing methods.

HarmoDecon mitigates multi-scale biases in spatial cell-type deconvolution by using a semi-supervised deep learning model, improving accuracy in spatial domain clustering and marker gene correlations.

The paper uses CRISPRa Perturb-seq to perturb transcription factors in fibroblasts, recapitulating in vivo transcriptional states and identifying key regulators.

COEXIST integrates serial multiplexed tissue images at the single-cell level by combining molecular profiles with spatial information to improve profiling and platform validation.

This study applies intrinsic dimension analysis to single-cell transcriptomic data to capture expression trajectories and define a cell potency score across species and differentiation processes.

PreDigs provides a database of context-specific cell-type markers and subtypes for digestive system cell annotation, offering tools for single-cell classification and analysis of over 3.4 million cells.

The paper uses single-cell multiomics to analyze mRNA metabolism and chromatin accessibility dynamics during the cell cycle, revealing oscillatory patterns and regulatory mechanisms.

This study evaluates precision and accuracy in sc/snRNA-seq data, identifies factors affecting reproducibility, and introduces a tool to assess data quality and differential expression reliability.

The paper uses single-cell RNA-seq and spatial transcriptomics to identify and characterize distinct cell types at the murine brain interface, revealing their molecular features and potential interactions.

The paper benchmarks deep learning models against linear baselines for predicting gene perturbation effects and finds they do not outperform simple models.

The paper develops STAR-FACS to sort cells by SNVs and shows distinct transcriptomes in glioblastoma cells with different TERT promoter mutations.

The paper integrates single-nucleus and spatial transcriptomics data to map the molecular landscape of the human hippocampus, identifying cell types and spatial domains.

The paper argues against using Silhouette metrics for evaluating single-cell integration methods due to their unsuitability in handling batch effects and proposes alternative strategies.

BioLLM integrates and benchmarks single-cell foundation models, revealing scGPTs' robust performance and highlighting limitations of scBERT in gene-level tasks.

SpaSEG is a convolutional neural network model for analyzing spatially resolved transcriptomics data, showing superior reliability and efficiency in detecting spatial patterns and intercellular interactions.