scATAC-seq

Photolabile oligonucleotides combined with topologically imposed light gradients enable spatially resolved single-cell transcriptomics and epigenomics

scSTAMP-seq enables spatially resolved single-cell transcriptomics and epigenomics by using photolabile oligonucleotides and light gradients to capture cell positions before sequencing.

Highly accurate reference and method selection for universal cross-dataset cell type annotation with CAMUS

CAMUS improves cross-dataset cell type annotation by selecting optimal references and methods, achieving accuracy gains across multiple single-cell datasets.

Learning induces persistent chromatin loops underlying robust gene expression during memory recall

The paper identifies persistent chromatin loops in hippocampal neurons after fear conditioning that correlate with gene expression changes during memory recall.

Age-associated transcriptomic and epigenetic alterations in mouse hippocampus

This study identifies age-related transcriptomic and epigenetic changes in mouse hippocampus, highlighting cell-type-specific alterations in chromatin accessibility and gene regulation.

Establishing a continuum of cell types in the visual cortex

The paper shows that the L2/3 continuum in the mouse visual cortex develops through two phases: a genetically programmed initial phase and a visually experience-driven second phase.

PEDIA-BRAIN: A single nuclei multiomic encyclopedia of the human pons provides a resource for normal development and disease vulnerability

PEDIA-BRAIN provides a single nuclei multiomic resource of the human pons across development, revealing oligodendrocyte maturation trajectories and disease-related perturbations in DMG and MS.

CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data

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

Discovering cell types and states from reference atlases with heterogeneous single-cell ATAC-seq features

EpiPack improves scATAC-seq analysis by integrating heterogeneous features and detecting perturbed cell types and states through transfer learning and graph-based modeling.

GUANACO: A Unified Web-Based Platform for Single-Cell Multi-Omics Data Visualization

GUANACO provides a code-free, interactive platform for visualizing single-cell RNA-seq and ATAC-seq data, enabling non-computational users to explore and share results effectively.

Cryo-mtscATAC-seq for single-cell mitochondrial DNA genotyping and clonal tracing in archived human tissues

The paper introduces Cryo-mtscATAC-seq to genotype mitochondrial DNA and trace clones in frozen human tissues, revealing clonal patterns in various diseases.

Comprehensive Noise Reduction in Single-Cell Data with the RECODE Platform

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.

Single-Cell Integration of Chromatin Accessibility and Transcriptomics Reveals Regulatory Networks in Ovarian Tumor-Infiltrating Adaptive NK Cells

This study integrates chromatin accessibility and transcriptomics data from ovarian tumor-infiltrating adaptive NK cells to identify key transcription factors and their regulatory networks.

Epigenomic Heterogeneity of Non-Functional Pancreatic Neuroendocrine Tumors Uncovered by Single nucleus and Spatial ATAC Profiling

This study uses single-nucleus and spatial ATAC-seq to uncover epigenomic heterogeneity and spatial niches in non-functional pancreatic neuroendocrine tumors.

BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell data

BayesCNet improves inference of cell type-specific regulatory networks by leveraging cell type hierarchy, outperforming existing methods, especially for rare cell types.

Comparative single-cell multiomic analysis reveals evolutionarily conserved and species-specific cellular mechanisms mediating natural retinal aging.

The paper identifies conserved and species-specific aging mechanisms in retinal cells across zebrafish, mice, and humans using single-cell multi-omics analyses.

Dissecting epigenome dynamics in human immune cells upon viral and chemical exposure by multimodal single-cell profiling

The paper presents a multimodal epigenome atlas of human immune cells exposed to viral and chemical agents, revealing epigenetic changes in transcription factor binding sites and cytokine networks.

OmnibusX: A unified platform for accessible multi-omics analysis

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

Cancer subclone detection based on DNA copy number in single cell and spatial omic sequencing data

Clonalscope detects cancer subclones using DNA copy number data from single-cell and spatial omic sequencing, identifying spatially segregated subclones and validating them with matched scDNA-seq data.

Genetic regulation of cell type-specific chromatin accessibility shapes immune function and disease risk

The paper maps chromatin accessibility and genetic variation in immune cells, showing how integrating cell-type-specific data improves disease risk understanding.

A Human Single-cell Atlas Identifies OLR1+ Scar-associated Macrophages as a Therapeutic Target for Chronic Liver Disease

An emergent disease-associated motor neuron state precedes cell death in a mouse model of ALS

The paper identifies a disease-associated motor neuron state in a mouse ALS model that precedes cell death and links it to human ALS genetic risk factors.

Integrated ambient modeling and genetic demultiplexing of single-cell RNA+ATAC multiome experiments with Ambimux

The paper introduces ambimux, a method to estimate and demultiplex ambient molecule contamination in single-cell RNA+ATAC multiome data, improving the detection of biologically meaningful signals.

A multi-component power-law penalty corrects distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions

The paper corrects distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions using multi-component power-law penalties informed by Gaussian mixture modeling and polymer-physics scaling.

REnformer, a single-cell ATAC-seq predicting model to investigate open chromatin sites

REnformer predicts chromatin accessibility from single-cell ATAC-seq data, outperforming Enformer in accuracy and error rates, and validates its utility with a known thalassemia variant.

Three-State Gene Expression Model Parameterized for Single-Cell Multi-Omics Data

The paper introduces a three-state gene expression model to analyze single-cell multi-omics data, revealing distinct gene expression strategies through parameter inference.

scSTAR2: a multiomics integration algorithm to reveal disease specific cellular signatures by bridging single-cell resolution features and clinical metadata

scSTAR2 integrates multiomics data to identify disease-specific cellular signatures by linking single-cell features with clinical metadata.

The Establishment of Cell-Type Specific Gene Regulation in the Sea Urchin Embryo

This study creates a single-cell atlas of gene expression, transcription, and chromatin accessibility in sea urchin embryos, revealing dynamic regulatory elements and lineage-specific gene activation patterns.

CLM-access: A Specialized Foundation Model for High-dimensional Single-cell ATAC-seq analysis

CLM-access is a foundation model for scATAC-seq data that improves batch correction, cell type annotation, and multi-modal integration compared to existing methods.

Single Cell Multiomics Across Nine Mammals Reveals Cell Type Specific Regulatory Conservation in the Brain

The paper identifies cell type-specific regulatory elements in nine mammals and shows their conservation and association with neuropsychiatric and neurodegenerative disease risks.

scMomer: A modality-aware pretraining framework for single-cell multi-omics modeling under missing modality conditions

scMomer is a pretraining framework for single-cell multi-omics modeling under missing modality conditions, generating biologically meaningful embeddings and outperforming unimodal approaches in downstream tasks.