Single-cell and spatial transcriptomics

This study evaluates single-cell language model configurations by systematically testing pre-training strategies and identifies best practices for improving performance.

This paper introduces a compound model for single-cell RNA-seq data without UMIs, using Pearson residuals to improve gene selection and embeddings in Smart-seq2 datasets.

The paper evaluates single cell transcriptomic methods on challenging placental tissue, finding that sample processing and storage have greater impact on results than technical variations.

This study benchmarks bioinformatics methods for single-cell and spatial RNA-seq Nanopore long-read data, evaluating their accuracy, robustness, and scalability across different protocols and sequencing conditions.

This study creates a single-cell RNA-Seq atlas of the pigtail macaque placenta in late gestation, revealing similarities to human placental cell populations.

SMURF improves spatial transcriptomic analysis by accurately assigning mRNAs to cells and revealing gene-expression zonation in tissues.

ARCADIA integrates scRNA-seq and spatial proteomics to reveal spatially dependent transcriptional programs without cell barcode pairing or feature-level correspondence.

ParTIpy scales archetypal analysis for biological trade-offs and integrates with single-cell workflows, enabling task inference in large datasets.

CODA aligns and compares spatial transcriptomics data across samples, identifying spatially differential genes associated with disease and sleep regulation.

SegJointGene improves cell segmentation and spatial gene prioritization by integrating spatial gene expression with imaging data using deep learning.

The paper introduces a data portal and open-source tools for single-cell transcriptomics of pediatric tumors, providing uniformly processed RNA-seq data and metadata from over 700 samples across 55 cancer types.

This paper uses Visium HD spatial transcriptomics to create a high-resolution map of human liver cell types and spatial gene expression in adult and pediatric samples.

DESpace2 identifies differential spatial patterns in spatial omics data by comparing gene expression across experimental conditions.

The paper shows that clinically relevant leukemia phenotypes emerge at the macrostate level from single cell data, revealing how disease states aggregate from noisy single cell transcriptomes.

The paper evaluates cluster replicability across two mouse brain atlases using single-cell and single-nucleus RNA sequencing, identifying 2009 reciprocally matched clusters with consistent spatial and gene expression patterns.

The paper introduces scVizComm, a web tool for visualizing cell-cell communication in single-cell transcriptomics data with pathway-centric prioritization.

This paper introduces a Bayesian framework for differential gene expression analysis that scales to over 10 million cells and integrates data from multiple patients.

scMagnifier improves fine-grained cell subtype resolution in scRNA-seq by using GRN-informed perturbations and consensus clustering, enhancing accuracy in both single and multi-batch data.

DeepDive disentangles covariate effects on single-cell epigenomes, enabling counter-factual analyses and identifying diabetes-linked beta cell subtypes.

scTimeBench benchmarks single-cell time-series analysis methods across forecast accuracy, embedding coherence, and lineage fidelity using eight datasets from four species.

The paper maps the human immune system across the lifespan using scRNA-seq and snATAC-seq, revealing unique infant immune signatures and age-related changes in immune cell populations.

Phi-Space ST identifies continuous cell states in spatial transcriptomics data using scRNA-seq references, revealing niche-specific cell types and patterns in cancer tissues.

The paper characterizes human endometrial stem/progenitor cells using scRNA-seq, identifying key markers and signaling pathways involved in their function and interactions.

SciPhy estimates time-scaled phylogenies and cell population parameters from sequential genetic lineage tracing data using a Bayesian framework, showing improved accuracy over existing methods.

GATHeR reconstructs full-length, paired B-cell receptor sequences from scRNA-seq data, improving subclass, allele, and isoform resolution, especially in naive and memory B cells.

The paper introduces circVDJ-seq for efficient T cell receptor profiling from single-cell and spatial multi-omics data, revealing distinct immune microenvironments and T cell clonality patterns in various clinical contexts.

CellBouncer demultiplexes and analyzes pooled single-cell data, revealing mitochondrial incompatibilities between humans and chimpanzees.

This study evaluates how pre-training dataset size and diversity affect single-cell foundation model performance on zero-shot and fine-tuned tasks using 22.2 million cells.

This study develops an iSCST framework for FFPE biopsies, identifying colitis-associated cell networks and VDZ response signatures in IBD patients.

This paper introduces an optimized smFISH protocol for plant tissue cryosections, enabling precise quantification of mRNA and validation of scRNA-seq data.