Single-cell and spatial transcriptomics

The paper introduces an optimized snRNA-seq workflow for metabolic tissues, emphasizing ambient RNA removal, doublet detection, and data integration to improve analysis accuracy.

The paper compares short-read and long-read single-cell RNA sequencing in mouse retina, identifying 44,325 transcript isoforms, 38% previously uncharacterized.

SpaGRN predicts spatially informed gene regulatory networks from spatial transcriptomics data, revealing spatiotemporal regulatory patterns in Drosophila development.

STopover analyzes spatial transcriptomics data to identify cell-cell interactions and colocalization patterns in the tumor microenvironment.

The paper introduces CFS, a method to compare cell groups across datasets without integration, identifying conserved astrocyte subpopulations in mouse motor cortex and spinal cord.

Immunopipe provides a flexible pipeline for integrated analysis of scRNA-seq and scTCR-seq data to enhance understanding of T-cell immunity and immunotherapy.

SciGeneX detects gene modules in single-cell and spatial transcriptomics data to reveal cell populations, outperforming existing methods and uncovering rare cell types in human thymus data.

FaST is a fast, lightweight pipeline for analyzing high-resolution spatial transcriptomics data from Illumina flow cells, enabling rapid processing of large datasets without requiring imaging procedures.

SOAPy is a Python package that analyzes spatial omics data to study spatial architecture, dynamics, and communication across various biological contexts.

Ocelli is a Python tool for visualizing developmental multimodal single-cell data using diffusion-based modeling, improving computational efficiency and low-dimensional representation quality.

PyEvoCell uses an LLM to aid in interpreting single-cell trajectory analysis by suggesting lineages, performing differential expression analysis, and validating hypotheses with PubMed citations.

scMUSCL improves scRNA-seq clustering by transferring knowledge from multiple annotated datasets without requiring batch correction or prior cluster number knowledge.

The paper develops a 3D imaging technique using DNA microscopy to map transcriptomes, genotypes, and morphologies in zebrafish embryos.

This paper benchmarks algorithms for identifying spatially variable genes in spatial transcriptomics using synthesized and real-world datasets to evaluate their effectiveness and application scenarios.

The paper investigates the reliability of mitochondrial DNA variants as lineage markers, finding that most are pre-existing heteroplasmy rather than de novo mutations, and introduces a score to identify reliable markers.

CytoSimplex visualizes cell differentiation trajectories using simplex geometry to represent cell fate commitment and potential in 2D and 3D plots.

CellPie is a fast, unsupervised method for factor discovery in spatial transcriptomics using joint non-negative matrix factorization, showing improved performance on human cancer datasets.

TimeFlow computes pseudotime for flow cytometry data by tracking density changes on a graph, showing good performance across patients and cell states.

The paper introduces gsMap, a method that maps disease-relevant cells to human complex traits using spatial transcriptomics and GWAS data, revealing spatial patterns of schizophrenia and depression-associated glutamatergic neurons.

This paper develops mouse-Geneformer, a deep learning model for mouse single-cell transcriptomes, showing cross-species utility for human data and disease modeling.

The paper applies spatial transcriptomics to the adult Drosophila brain and body to map mRNA locations and integrate with single-cell data.

The paper maps cellular states in yeast using a single-cell transcriptome atlas generated from genetic perturbations and identifies regulators of transcriptional heterogeneity.

scDILT integrates single-cell datasets while preserving reference clustering patterns and mapping new cells to annotated clusters, outperforming existing methods on simulated and real data.

The paper introduces a method to estimate the relative localization of transcripts between the nucleus and cytosol using combined nuclear, cytosolic, and whole-cell RNA-seq data.

The paper benchmarks spatially resolved gene expression simulation models using a framework that evaluates 13 methods across 10 datasets and 35 metrics, highlighting performance differences and factors affecting model accuracy.

The paper develops a computational framework to jointly analyze transcriptional and mechanical signals in spatial transcriptomics data from mouse embryos, identifying predictive signatures of tissue compartment boundaries.

This study compares droplet-based single-cell sequencing platforms to help researchers select the most suitable one for their applications.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

MuCST integrates heterogeneous morphology images and spatial transcriptomics data using contrastive learning, identifying tissue subpopulations and tumor domains with high accuracy.