scATAC-seq

Single-cell multiomics reveals disrupted glial gene regulatory programs in Alzheimer's disease via interpretable machine learning

This study uses single-cell multi-omics data to identify disrupted glial gene regulatory programs in Alzheimer's disease using interpretable machine learning.

A transcriptional clock of the human pluripotency transition

The paper uses human pluripotent stem cells to study the epiblast development transition, revealing a transcriptional clock mechanism that governs timing and directionality through gene regulatory networks.

Chromatin remodeling integrates vertebrate body axis elongation and cell fate determination

This study shows that chromatin remodeling coordinates cell fate and movement during zebrafish tailbud elongation by modulating nuclear volume and gene expression.

Multimodal brain cell atlas across the adult macaque lifespan

The paper constructs a multimodal brain cell atlas of adult macaques across their lifespan, revealing aging-related molecular changes and identifying aging hotspots and regulatory loci.

Single-nucleus multiomics reveals the gene-regulatory networks underlying sex determination of murine primordial germ cells

This study uses single-nucleus multiomics to uncover gene regulatory networks and chromatin dynamics in murine PGCs during sex determination.

Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework

UniVI integrates multimodal single-cell data using a mixture-of-experts β-variational autoencoder, improving cross-modal alignment and label transfer across various data types and study designs.

Mapping the ISR Landscape in Cognitive Disorders via single-cell multi-omics

The paper generates a single-cell ISR atlas in a mouse model of persistent ISR activation, identifying cell-type-specific ISR programs and their roles in cognitive decline.

Machine Learning Prediction of Non-Coding Variant Impact in Cell-Class-Specific Human Retinal Cis-Regulatory Elements

The paper develops ML models to predict the impact of non-coding variants on cell-class-specific regulatory elements using single nucleus ATAC-seq data, achieving over 90% accuracy.

PEAKQC: Periodicity Evaluation in scATAC-seq data for quality assessment

PEAKQC evaluates single-cell ATAC-seq quality using wavelet-based convolution of fragment length distribution patterns, improving downstream analysis by filtering low-quality cells.

Single-cell multiome analysis supports α-to-β transdifferentiation in human pancreas

The paper identifies transitional cell states between alpha and beta cells in human islets using single-cell multiome data, suggesting potential for alpha-to-beta transdifferentiation.

Reducing batch effects in single cell chromatin accessibility measurements by pooled transposition with MULTI-ATAC

The paper introduces MULTI-ATAC, a method to reduce batch effects in scATAC-seq by pooling samples before transposition, enabling scalable and batch-free experiments.

A pathogenic subpopulation of human glioma associated macrophages linked to glioma progression

This study identifies a pathogenic subpopulation of glioma-associated macrophages (mGAMs) linked to glioma progression through GRN analysis and functional validation.

Simultaneous single-cell CRISPR, RNA, and ATAC-seq enables multiomic CRISPR screens to identify gene regulatory relationships

The paper introduces CAT-ATAC, a method combining CRISPR, RNA, and ATAC-seq in single cells to identify gene regulatory relationships and validate them with loss-of-function experiments.

Multi-lineage transcriptional and cell communication signatures define evolving personalized mechanisms that initiate and perpetuate rheumatoid arthritis

The paper identifies shared transcriptional and cell communication signatures in RA patients and at-risk individuals, showing how multiple pathogenic mechanisms contribute to disease initiation and progression.

EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment

EpiFoundation is a foundation model for scATAC-seq that learns cell representations through peak-to-gene alignment, achieving state-of-the-art performance in downstream tasks.

Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells

Phospho-seq integrates phosphorylated protein profiling with scATAC-seq and scRNA-seq to study cell signaling and gene regulation in single cells.

RAS pathway activation drives clonal selection and monocytic differentiation in FLT3 and BCL2 inhibitor resistance

INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation

INSTINCT integrates multi-sample spatial chromatin accessibility sequencing data by removing batch effects and noise, enabling comprehensive analysis of epigenomic landscapes.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

This study identifies regulatory elements that switch from silencers to enhancers in breast cancer cells using single-cell ATAC-seq and RNA-seq data.

Telomemore enables single-cell analysis of cell cycle and chromatin condensation

Telomemore quantifies subtelomeric reads from single-cell data to assess chromatin condensation and complements cell cycle analysis in single-cell RNA-seq.

scGRIP: a graph-based explainable AI framework for single-cell multi-omics Gene Regulatory Inference with Prior Knowledge

scGRIP improves gene regulatory network inference in single-cell multi-omics data by integrating prior knowledge and explainable AI techniques.

ECLARE: multi-teacher contrastive learning via ensemble distillation for diagonal integration of single-cell multi-omic data

ECLARE integrates unpaired single-cell multi-omic data using multi-teacher contrastive learning, improving cell pairing accuracy and biological structure preservation in depression studies.

JIND-Multi: Leveraging Multiple Labeled Datasets for Automated Annotation of Single-Cell RNA and ATAC Data

JIND-Multi improves cell annotation in single-cell RNA and ATAC data by transferring labels across multiple datasets, reducing unclassified cells and maintaining accuracy.

Functional characterization of eQTLs and asthma risk loci with scATAC-seq across immune cell types and contexts

The paper uses scATAC-seq to analyze chromatin accessibility and gene expression dynamics in immune cells from asthmatic children, identifying eQTLs and their regulatory roles in asthma risk.

Single-nucleus multiome ATAC and RNA sequencing reveals the molecular basis of thermal acclimation in Drosophila melanogaster embryos

The paper uses single-nucleus multiome sequencing to study how thermal acclimation affects chromatin accessibility and gene expression in Drosophila embryos, revealing homeostatic mechanisms that enhance heat tolerance.

PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data

PACS is a statistical model for analyzing snATAC-seq data that improves power and controls false positives in differential accessibility analysis.

Single-cell multi-omics reveals tumor microenvironment factors underlying poor immunotherapy responses in ALK-positive lung cancer

The paper uses single-cell multi-omics to identify immunosuppressive factors in ALK-positive lung cancer that contribute to poor immunotherapy responses.

Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach

The paper introduces SciDAP, a platform enabling biologists to analyze single-cell sequencing data without coding expertise using reproducible pipelines.

dandelionR: Single-cell immune repertoire trajectory analysis in R

dandelionR provides an R implementation of Dandelion's workflow for analyzing single-cell RNA and immune receptor sequencing data.

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Learning and removing Tn5/DNase-I sequence bias lets a small base-resolution CNN (ChromBPNet) recover TF motifs and footprints from accessibility data and rival Enformer at predicting chromatin QTL effects.