scATAC-seq

Scarf: A toolkit for memory efficient analysis of large-scale single-cell genomics data

Scarf provides a memory-efficient toolkit for analyzing large-scale single-cell genomics data on standard hardware.

Functional Inference of Gene Regulation using Single-Cell Multi-Omics

This paper develops FigR, a framework to integrate scATAC-seq and scRNA-seq data to infer gene regulatory networks and identify TF regulators in immune responses.

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

SECEDO detects subclones in tumors using SNVs from ultra-low coverage single-cell DNA sequencing, achieving high accuracy in synthetic and real datasets.

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

The paper uses a single-cell multiome atlas of the human retina and deep learning to identify causal noncoding variants in eye diseases.

Single-cell chromatin and transcriptome dynamics of Synovial Fibroblasts transitioning from homeostasis to pathology in modelled TNF-driven arthritis

This study profiles single-cell transcriptomes and chromatin accessibility of synovial fibroblasts in TNF-driven arthritis, identifying disease-specific subtypes and key regulatory factors.

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

This paper introduces scMTNI, a framework to infer cell type-specific gene regulatory networks on cell lineages using scRNA-seq and scATAC-seq data, showing its effectiveness on simulated and real datasets.

Single Cell Epigenetics Reveal Cell-Cell Communication Networks in Normal and Abnormal Cardiac Morphogenesis

The paper uses single-cell epigenomics and machine learning to model cell-cell communication networks in cardiac development and identify dysregulation in TBX1 deletion.

An epigenome atlas of neural progenitors within the embryonic mouse forebrain

The paper characterizes the epigenomic landscape of embryonic mouse forebrain neural progenitors using snATAC-Seq and integrates multi-omics data to reveal region-specific regulatory mechanisms.

A reference single-cell regulomic and transcriptomic map of cynomolgus monkeys

This study creates a single-cell regulomic and transcriptomic map of cynomolgus monkeys, revealing cell-type similarities to humans over mice.

Gene regulation modules in non-alcoholic fatty liver disease revealed by single-nucleus ATAC-seq

This study uses single-nucleus ATAC-seq and machine learning to identify gene regulation modules and core genes in NAFLD progression in rats.

Integrated single-cell sequencing analysis reveals peripheral immune landscape across the human lifespan

This study uses single-cell sequencing to analyze immune cell dynamics across human lifespan, identifying age-related changes in T cell function, cell interactions, and B cell subsets.

Single cell multi-omic mapping of subclonal architecture and pathway phenotype in primary gastric and metastatic colon cancers

The paper integrates scDNA-seq and scRNA-seq data to characterize subclonal architecture and pathway activity in gastric and colorectal cancers.

Interpretable machine learning models for single-cell ChIP-seq imputation

SIMPA imputes missing regions in scChIP-seq data using machine learning models trained on bulk data, improving clustering and pathway gene identification while enabling model interpretation.

Dimensionality reduction and statistical modeling of scGET-seq data

This paper describes advances in statistical analysis and modeling of scGET-seq data, focusing on dimensionality reduction, statistical modeling, and trajectory analysis.

Learning latent embedding of multi-modal single cell data and cross-modality relationship simultaneously

The paper introduces scDART, a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modal relationships for trajectory inference.

Supervised dimensionality reduction for exploration of single-cell data by Hybrid Subset Selection - Linear Discriminant Analysis

This paper introduces a supervised dimensionality reduction method using hybrid subset selection with linear discriminant analysis to improve visualization and analysis of single-cell data.

Cardiac neural crest lineage diversity and underlying gene regulatory networks revealed by multimodal analysis

The paper uses multimodal analysis to reveal cardiac neural crest lineage diversity and gene regulatory networks involved in their differentiation.

Comprehensive Benchmarking of CITE-seq versus DOGMA-seq Single Cell Multimodal Omics

This study benchmarks DOGMA-seq and CITE-seq in human T cells, finding DOGMA-seq provides more information with optimized permeabilization despite slightly lower mRNA and ADT library quality.

Ex vivo Expansion Potential of Murine Hematopoietic Stem Cells: A Rare Property Only Partially Predicted by Phenotype

This study examines ex vivo expansion of murine HSCs, finding that only a rare subset exhibits functional self-renewal, which is not fully predicted by phenotype.

Remodeling of gene regulatory networks underlying thermogenic stimuli-induced adipose beiging

This study identifies common and distinct chromatin accessibility changes in adipose tissue from mice exposed to cold or Adrb3 agonists, revealing unique functional consequences of these stimuli on lipid composition and thermogenesis.

Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity

This study combines common and rare variant analyses with single-cell multiomics to identify cell-type-specific genetic risk factors for severe COVID-19, revealing molecular mechanisms and potential therapeutic targets.

Adversarial domain translation networks for fast and accurate integration of large-scale atlas-level single-cell datasets

Portal integrates large-scale single-cell datasets efficiently and accurately by preserving biological variation across samples, platforms, and species.

scMinerva: an Unsupervised Graph Learning Framework with Label-efficient Fine-tuning for Single-cell Multi-omics Integrated Analysis

scMinerva is an unsupervised graph learning framework for single-cell multi-omics analysis that outperforms existing methods with few labels and provides biomarkers for COVID-19 cell differentiation.

Cell-specific chromatin landscape of human coronary artery resolves regulatory mechanisms of disease risk

The paper uses single-cell ATAC-seq to analyze coronary artery cells from CAD patients, identifying regulatory mechanisms and risk variants associated with disease.

The epigenomic landscape of single vascular cells reflects developmental origin and identifies disease risk loci

The paper identifies vascular site-specific epigenomic and transcriptomic features and links them to disease risk loci using single-cell data and GWAS integration.

Tracking single cell evolution via clock-like chromatin accessibility

This paper introduces EpiTrace, a method using scATAC data to estimate cell age and lineage by analyzing clock-like chromatin accessibility, revealing insights into development and aging.

A unique epigenomic landscape defines CD8+ tissue-resident memory T cells

This study identifies distinct epigenetic trajectories in CD8+ T cells during acute infection, revealing how chromatin accessibility changes guide the development of tissue-resident memory T cells versus circulating memory T cells.

Multi-omics integration and regulatory inference for unpaired single-cell data with a graph-linked unified embedding framework

This paper introduces GLUE, a framework for integrating unpaired single-cell multi-omics data using regulatory knowledge, showing improved accuracy and scalability across various tasks.

NEAT-seq: Simultaneous profiling of intra-nuclear proteins, chromatin accessibility, and gene expression in single cells

NEAT-seq profiles nuclear proteins, chromatin accessibility, and transcriptomes in single cells, revealing TF regulatory mechanisms and linking GWAS SNPs to chromatin accessibility and target genes.

Chromatin accessibility landscape and active transcription factors in primary human invasive lobular and ductal breast carcinomas

The paper compares chromatin accessibility and active transcription factors between invasive lobular and ductal breast carcinomas using ATAC-seq data from TCGA.