scATAC-seq

A single-cell multimodal foundation model enables cross modality analysis between transcriptome and chromatin

MUTUAL enables cross-modality analysis between transcriptome and chromatin by learning a shared RNA-ATAC space from multiome data without refitting.

Move BeTween modAlities (MBTA) employs flow matching to predict single cell data modalities

MBTA uses flow matching to address structural mismatch between molecular modalities, improving cross-modal translation and enabling multimodal analysis in single-cell data.

scWeave: A deep learning model that bidirectionally translates between gene expression and chromatin structure at single cell resolution

scWeave translates between gene expression and chromatin structure at single-cell resolution using deep learning, improving cross-modality inference across multiple datasets.

HoloCell: A Generative Foundation Model for Holistic Cellular Modeling

HoloCell is a generative foundation model that integrates epigenomics, transcriptomics, and proteomics data across single-cell multi-omics modalities, demonstrating superior performance in representation learning and cross-modal generation.

Alignment-free integration of single-nucleus ATAC-seq across species with sPYce

sPYce integrates single-nucleus ATAC-seq data across species using k-mer histograms, revealing conserved and divergent regulatory programs without requiring genome alignments.

scDynOmics: An Optimized Transformer Model for Representation Learning from Single-Cell Multiomics

scDynOmics is a transformer model for single-cell multiomics data that outperforms existing models and provides interpretable insights into cellular dynamics.

Disagreement between demultiplexing methods reveals structured cell quality gradients in multiplexed single-cell data

This paper introduces Split-flow, a pipeline that uses concordance between demultiplexing methods to classify cell quality in multiplexed single-cell data, revealing structured quality gradients.

Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration

The paper introduces CLIC scores to quantify confidence in gene-peak associations for single-cell RNA-ATAC integration, improving integration accuracy and biological interpretability.

MultiGAI: Global Attention-Based Integration of Single-Cell Multi-Omics with Batch Effects Correction

MultiGAI corrects batch effects in single-cell multi-omics data while preserving biological signals, showing effectiveness in integration and correction tasks.

Partially Shared Multi-Modal Embedding Learns Holistic Representation of Cell State

APOLLO learns shared and modality-specific information from multi-modal single-cell data to improve understanding of cell state and regulatory mechanisms.

An Integrated Single-Cell and Epigenomic Resource for Comparative Analysis of the Basal Ganglia

The paper presents an integrated single-cell and epigenomic resource for exploring basal ganglia datasets across species, enabling multi-omics and comparative analysis.

scCotag: Diagonal integration of single-cell multi-omics data via prior-informed co-optimal transport and regularized barycentric mapping

scCotag improves single-cell multi-omics integration by using co-optimal transport and regularized barycentric mapping, outperforming existing methods in alignment and embedding accuracy.

Regulatory element modules as universal features for single-cell chromatin analysis

The paper introduces REMO, a set of DNA regulatory element modules for single-cell chromatin analysis, improving cell state separation and annotation.

Atacformer: A transformer-based foundation model for analysis and interpretation of ATAC-seq data

Atacformer is a transformer-based model for scATAC-seq data analysis that improves clustering, cell-type annotation, and cross-modal alignment with RNA-seq data.

scRetinaDB: A Comprehensive Database of Single-Cell and Spatial Omics from Cross-Species Retinas

scRetinaDB aggregates single-cell and spatial omics data from retinas across species to enable cross-species analysis and resource sharing.

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.

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.

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.

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.

Quality control of single-cell ATAC-seq data without peak calling using Chromap

This paper extends Chromap to provide peak-based quality control metrics for single-cell ATAC-seq without peak calling, improving downstream analysis by identifying low-quality cells missed by other methods.

sCIN: A Contrastive Learning Framework for Single-Cell Multi-omics Data Integration

sCIN integrates single-cell multi-omics data using contrastive learning, outperforming existing methods on paired and unpaired datasets.

scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data

scAGCI improves cell clustering in integrated scRNA-seq and scATAC-seq data by using anchor graphs to capture shared and specific information, achieving better performance and biological relevance.

Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment

Alevin-fry-atac provides a fast, memory-efficient method for single-cell ATAC-seq data processing with high accuracy compared to existing tools.

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

scMODAL is a deep learning framework for aligning single-cell multi-omics data using feature links, effectively removing unwanted variation and identifying cell subpopulations.

scAI-SNP: a method for inferring ancestry from single-cell data

scAI-SNP infers ancestry from single-cell data using ancestry-informative SNPs, showing robustness across diverse tissues and modalities.

Global and cross-omics feature aggregation improves single-cell multi-omics integration and clustering

scCODI improves single-cell multi-omics integration and clustering by aggregating cross-omics features and using contrastive learning, outperforming existing methods across nine datasets.

SCARF: Single Cell ATAC-seq and RNA-seq Foundation model

SCARF is a foundation model for integrating single-cell ATAC-seq and RNA-seq data, achieving state-of-the-art performance in cell representation and cross-omics tasks.

Defining effective strategies to integrate multi-sample single-nucleus ATAC-seq datasets via a multimodal-guided approach

The paper benchmarks snATAC-seq integration pipelines using multimodal datasets and recommends SnapATAC2 + Harmony with ENCODE cCRE features for effective integration.

Ocelli: an open-source tool for the visualization of developmental multimodal single-cell data

Ocelli is a Python tool for visualizing developmental multimodal single-cell data using diffusion-based modeling, improving computational efficiency and low-dimensional representation quality.

Scalable high-performance single cell data analysis with BPCells

BPCells enables scalable single-cell RNA-seq and ATAC-seq analysis on modest hardware by using disk-backed streaming algorithms and compressed formats.