Single-cell and spatial transcriptomics

The paper generates a scRNA-seq reference atlas of living and early post-mortem human retina, revealing distinct transcriptional dynamics and a novel mlCone cluster.

SCassist uses large language models to guide and enhance single-cell RNA sequencing analysis by providing recommendations and interpretations throughout the workflow.

SCALPEL quantifies single-cell transcript isoforms with higher sensitivity than existing tools, revealing novel cell populations and isoform regulation in mouse spermatogenesis and iPSC differentiation.

PerturbNet predicts cell state changes from unseen chemical and genetic perturbations using deep learning and perturbation response data.

This paper benchmarks 40 single-cell multi-modal integration algorithms across paired, unpaired, and mosaic datasets to evaluate usability, accuracy, and robustness.

CosMx spatial transcriptomics of bone marrow from 6 refractory/relapsed AML patients shows CD14+ monocytes rising in leukemia-cell neighborhoods after pembrolizumab in responders, dendritic cells in nonresponders.

The paper introduces Cell Marker Accordion, a tool for accurate and interpretable single-cell and spatial omics annotation across health and disease contexts.

This study assesses how spatial microenvironments influence gene expression and cell-cell communication using scRNA-seq and spatial transcriptomics, finding limited transcriptional changes and weak correlation between communication predictions and spatial colocalization.

PRISM is a Python package for interactive analysis of multiplexed tissue microarrays, enabling translational and clinical research using spatial proteomic data from multiple platforms.

Ensemblex improves accuracy in genetic demultiplexing of pooled scRNA-seq samples by combining four algorithms with accuracy weighting.

This paper introduces a Bayesian model to infer intra-tumoral clonal structure and copy number alterations from scRNA-seq data, achieving high accuracy in clustering and CNA state inference.

The paper introduces spARI, a spatially aware adjusted Rand index for evaluating spatial transcriptomics clustering, showing improved performance over traditional ARI in simulation and real datasets.

Proust detects spatial domains using contrastive self-supervised learning on multi-omics data, showing improved accuracy across benchmark datasets.

HIDE improves cell-type deconvolution by incorporating a cell hierarchy, showing better performance in simulations and applied to breast cancer data.

MorphLink identifies disease-related morphological-molecular interplays in multi-modal spatial omics data by linking interpretable morphological features with molecular measurements.

The paper introduces SigXTalk, a method to analyze crosstalk in cell-cell communication using scRNA-seq data, identifying shared molecules and regulatory networks in disease conditions and over time.

sCIN integrates single-cell multi-omics data using contrastive learning, outperforming existing methods on paired and unpaired datasets.

The paper introduces Projection-TAGs, a tool for multiplex projection tracing and multi-modal profiling of projection neurons in mice.

scAGCI improves cell clustering in integrated scRNA-seq and scATAC-seq data by using anchor graphs to capture shared and specific information, achieving better performance and biological relevance.

CellAgentChat uses agent-based modeling to analyze single-cell data and detect cell-cell interactions, offering visualization and perturbation capabilities.

cytoKernel assesses differential expression in single-cell data by using kernel embeddings to detect both aggregate and subtle variations, outperforming existing methods.

GPerturb models single-cell perturbation data using Gaussian processes to estimate gene-level effects and provide uncertainty estimates, showing competitive performance on simulated and real datasets.

This paper shows that binarizing scRNA-seq data and integrating it with scATAC-seq data using TF-IDF and LSI improves clustering for multiomic analysis.

This paper refines the Tangram spatial mapping tool to improve consistency and reliability in mapping single-cell RNA-seq data to spatial transcriptomics data.

The paper integrates ten scRNA-seq datasets to create a limb skeletal cell atlas covering development to adulthood, identifying 39 cell states and enabling cell-cell communication and perturbation studies.

Beaver improves transcriptome assembly at single-cell resolution by combining a fragment graph with random forest models, outperforming existing assemblers in precision.

Alevin-fry-atac provides a fast, memory-efficient method for single-cell ATAC-seq data processing with high accuracy compared to existing tools.

LEGEND integrates scRNA-seq and spatial transcriptomic data to identify co-expressed gene groups, revealing spatial coherence and context-specific gene functions.

The paper uses single-cell multiomics to study gene regulatory programs in mouse embryonic skin, identifying cis-regulatory elements and transcription factors involved in development.

ADTnorm normalizes and integrates CITE-seq datasets to remove batch effects and improve cell-type separation across 13 public datasets.