Single-cell and spatial transcriptomics

CLUEY improves cell type detection and clustering in single-cell omics by integrating biological knowledge to guide clustering and enhance interpretability.

The paper introduces iRECODE, a noise reduction method for single-cell data that improves accuracy and efficiency in handling technical and batch noise across various sequencing modalities.

The paper analyzes transcriptomic diversity in amygdalar subdivisions across humans and nonhuman primates using single-nucleus RNA sequencing and identifies distinct neuronal subtypes and transcriptional specializations.

CellSexID uses sex-linked gene expression to computationally track donor and recipient cells in chimeric models and beyond.

The China Brain Multi-omics Atlas Project (CBMAP) creates a comprehensive molecular reference map of over 1,000 human brains to address underrepresentation in East Asian populations and advance research on aging and neuropsychiatric disorders.

This study creates an integrated single-cell atlas of human atherosclerotic plaques using scRNA-seq data, enabling accurate cell type annotation and downstream analyses.

This study identifies 44 genes, including novel markers like OAS2 and OASL, as potential early diagnostic and prognostic markers for serous endometrial carcinoma using a mouse model and multi-omics approaches.

OmnibusX provides a code-free, privacy-centric platform for multi-omics analysis by integrating diverse tools and workflows into a single application.

The paper constructs a single-cell transcriptomic atlas of the human retina from Chinese donors, identifying 10 cell types and subtypes, and mapping their molecular programs and biological functions.

The paper constructs a scRNA-seq atlas of human fetal retina to map retinal ganglion cell maturation trajectories and identify intrinsic and extrinsic factors across developmental stages.

UniFORM normalizes multiplex tissue imaging data by aligning negative populations and preserving positive variation, outperforming existing methods in batch effect mitigation and downstream analysis.

This study compares single-cell RNA-seq methods for neutrophil transcriptome profiling in clinical samples, finding all methods produce high-quality data with 10X FLEX showing strong concordance with flow cytometry.

The paper presents two workflows for analyzing multiplexed imaging data from cancer tissues, applied to over 10,000 samples from 2500 patients, enabling large-scale tumor microenvironment studies.

iSCALE predicts super-resolution gene expression and annotates tissue architecture in large tissues, revealing features missed by conventional spatial transcriptomics.

scTrace+ improves cell fate inference by integrating lineage-tracing data with transcriptomic similarities using a Kernelized Probabilistic Matrix Factorization model, accurately predicting cell fates and identifying driver genes.

The paper introduces TCAT, a pipeline to annotate T-cell GEPs, identifying 46 reproducible programs across diverse tissues and diseases.

GHIST predicts spatial gene expression at single-cell resolution from histology images, showing superior performance across different resolutions and datasets.

The paper introduces a Nextflow-based pipeline for analyzing Oxford Nanopore single-cell RNA sequencing data, enabling gene and transcript-level quantification with portability and reproducibility.

Scvi-hub provides a platform for sharing and analyzing single-cell omics datasets using pre-trained models, enabling efficient tasks like visualization and annotation with reduced computational demands.

scHDeepInsight improves immune cell classification in scRNA-seq by using a hierarchical deep learning framework with adaptive loss and image-based features, achieving 93.2% accuracy across seven datasets.

This paper benchmarks single-cell methods by analyzing 282 papers, highlighting challenges in combining benchmarking studies and preventing fatigue.

The paper develops a framework to characterize and design gene panels for spatially resolved omics, showing that both computational and manually curated panels have strengths in capturing cell types and reducing redundancy.

This study optimizes MERFISH protocols to improve RNA imaging performance in cell culture and tissue samples.

The paper develops a computational method to assess tissue-specific gene expression of essential genes in human and mouse using scRNA-seq data and finds that essential genes show consistent expression across cell types with significant co-expression networks and pathway enrichment.

PILOT-GM-VAE analyzes single cell disease atlases to perform sample-level clustering, trajectory inference, and batch correction using Gaussian Mixture Variational Autoencoders with optimal transport.

STEAM evaluates clustering performance in spatial transcriptomics by comparing results across methods and providing statistical metrics to assess consistency and reliability.

CytoNormPy provides a fast Python implementation of CytoNorm for removing batch effects in cytometry data, with added clustering and visualization tools.

The paper uses single-cell sequencing data and statistical physics methods to identify collective modes of gene expression in early ascidian embryos.

The paper develops cell-type-specific transcriptomic aging clocks using single-nuclei RNA sequencing data from human prefrontal cortex tissue, showing distinct aging patterns across cell types.

This study uses spatial transcriptomics and organoids to define cellular diversity at the swine maternal-fetal interface, revealing conserved trophoblast populations and novel markers.