Single-cell and spatial transcriptomics

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.

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.

This study evaluates the Xenium In Situ spatial transcriptomics platform using mouse brain and human breast cancer datasets, comparing it to other technologies and benchmarking analysis tools.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

This paper introduces a method to prevent over-clustering in scRNA-seq by controlling double-dipping, improving differential expression analysis accuracy.

Spotiphy transforms spatial transcriptomics data into single-cell-resolved whole-transcriptome images, revealing novel cellular compositions and spatial domains in Alzheimer's and breast tissue.

This paper introduces Perturb-FISH, a method combining spatial transcriptomics and CRISPR screening to study transcriptional circuits, showing intracellular and intercellular effects in immune and neurological contexts.

The paper introduces a boosting autoencoder method to incorporate structural assumptions into dimensionality reduction for single-cell RNA sequencing data, identifying small gene sets that explain latent dimensions.