4.8 Article

Integrated analysis of multimodal single-cell data with structural similarity

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Biochemical Research Methods

Benchmarking atlas-level data integration in single-cell genomics

Malte D. Luecken et al.

Summary: This study benchmarked 68 method and preprocessing combinations on 85 batches of gene expression data, highlighting the importance of highly variable gene selection in improving method performance. When dealing with complex integration tasks, scANVI, Scanorama, scVI, and scGen consistently performed well, while the performance of single-cell ATAC-sequencing integration was strongly influenced by the choice of feature space.

NATURE METHODS (2022)

Article Multidisciplinary Sciences

UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization

April R. Kriebel et al.

Summary: Single-cell genomic technologies offer a unique opportunity for defining molecular cell types. This study introduces a nonnegative matrix factorization algorithm for integrating single-cell datasets with shared and unshared features. The incorporation of unshared features significantly improves the integration of various types of single-cell datasets.

NATURE COMMUNICATIONS (2022)

Article Biotechnology & Applied Microbiology

Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data

Lijia Yu et al.

Summary: This study systematically benchmarks a range of clustering algorithms for single-cell RNA-seq data and summarizes the strengths and weaknesses of each method. The authors evaluate the performance of the algorithms using a large number of datasets and provide a multi-aspect recommendation to users.

GENOME BIOLOGY (2022)

Article Biochemical Research Methods

Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data

Chunman Zuo et al.

Summary: The study introduces a single-cell multimodal variational autoencoder model that effectively integrates transcriptomic and chromatin accessibility information, accurately representing the multilayer profiles of cells and demonstrating good capabilities in dissecting cellular heterogeneity, denoising and imputing data, as well as constructing associations between multilayer omics data.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemistry & Molecular Biology

Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models

Chenling Xu et al.

Summary: As the number of single-cell transcriptomics datasets increases, integrating data to achieve a common ontology of cell types and states becomes the natural next step. scVI and scANVI are two methods that can effectively integrate data and perform well in cell state annotation, with high accuracy, scalability, and adaptability.

MOLECULAR SYSTEMS BIOLOGY (2021)

Article Biochemical Research Methods

Single-cell chromatin state analysis with Signac

Tim Stuart et al.

Summary: Signac is a comprehensive toolkit for the analysis of single-cell chromatin data, enabling end-to-end analysis and interoperability with the Seurat package for multimodal analysis.

NATURE METHODS (2021)

Article Biochemical Research Methods

Joint probabilistic modeling of single-cell multi-omic data with totalVI

Adam Gayoso et al.

Summary: totalVI is a framework for end-to-end joint analysis of CITE-seq data which probabilistically represents data as a composite of biological and technical factors, providing a cohesive solution for common analysis tasks. It demonstrates strong performance in tasks such as dimensionality reduction, dataset integration, correlation estimation, and differential expression testing.

NATURE METHODS (2021)

Article Multidisciplinary Sciences

Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Rongxin Fang et al.

Summary: The paper introduces SnapATAC, a software package for analyzing single cell ATAC-seq datasets, which can dissect cellular heterogeneity and map cellular states' trajectories. The Nystrom method allows processing data from up to a million cells, and it incorporates existing tools for single cell ATAC-seq dataset analysis. SnapATAC is applied to mouse secondary motor cortex profiles and identifies candidate regulatory elements and cell-type specific transcriptional regulators.

NATURE COMMUNICATIONS (2021)

Article Cell Biology

Pre-existing chromatin accessibility and gene expression differences among naive CD4+ T cells influence effector potential

Dakota Rogers et al.

Summary: The study found that pre-existing transcriptional and chromatin landscape differences among naive CD4(+) T cells impact their effector potential, with some cells showing a tendency towards follicular helper T cell lineage. Furthermore, the strength of TCR signal during thymic development plays a crucial role in establishing chromatin landscapes that ultimately shape the effector potential of naive CD4(+) T cells.

CELL REPORTS (2021)

Review Biotechnology & Applied Microbiology

Computational principles and challenges in single-cell data integration

Ricard Argelaguet et al.

Summary: The development of single-cell multimodal assays has provided a powerful tool for investigating cellular heterogeneity in multiple dimensions. Data integration is a key challenge in analyzing single-cell multimodal data, with existing strategies utilizing similar mathematical ideas but having distinct goals and principles.

NATURE BIOTECHNOLOGY (2021)

Article Multidisciplinary Sciences

BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

Kevin E. Wu et al.

Summary: BABEL is a deep learning method that can translate between the transcriptome and chromatin profiles of a single cell, enabling computation of paired multiomic measurements when only one modality is experimentally available. The method accurately translates information between different modalities in several datasets and generalizes well to cell types in new biological contexts.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

Article Biochemical Research Methods

SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration

Yingxin Cao et al.

Summary: The proposed deep generative model framework SAILER aims to learn a low-dimensional nonlinear latent representation of each cell from scATAC-seq data, ensuring independence from extrinsic confounding factors. Experimental results demonstrate that SAILER outperforms other methods in learning biologically more meaningful cell representations, leading to significant improvements in downstream analyses. SAILER can easily scale to process millions of cells and has been implemented into a software package for large-scale scATAC-seq data analysis.

BIOINFORMATICS (2021)

Article Biochemistry & Molecular Biology

Integrated analysis of multimodal single-cell data

Yuhan Hao et al.

Summary: The study introduces a weighted-nearest neighbor analysis framework to learn the relative utility of each data type in each cell, enabling integrative analysis of multiple modalities. Applied to a CITE-seq dataset, the method constructs a multimodal reference atlas of the circulating immune system and successfully identifies and validates previously unreported lymphoid subpopulations.
Article Biotechnology & Applied Microbiology

Cobolt: integrative analysis of multimodal single-cell sequencing data

Boying Gong et al.

Summary: Cobolt is a novel method designed for analyzing data from joint-modality platforms and integrating multiple datasets across different modalities. It demonstrates its integration capabilities by jointly analyzing multi-modality data of gene expression and chromatin accessibility with single-cell RNA-seq and ATAC-seq datasets.

GENOME BIOLOGY (2021)

Article Biotechnology & Applied Microbiology

Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities

Rohit Singh et al.

Summary: Schema is a tool that synthesizes multiple biological information modalities using a metric learning strategy, which can be used for inferring cell types, data visualization, performing differential gene expression analysis, and estimating evolutionary pressure on peptide sequences.

GENOME BIOLOGY (2021)

Article Biochemical Research Methods

Unsupervised topological alignment for single-cell multi-omics integration

Kai Cao et al.

BIOINFORMATICS (2020)

Article Biochemical Research Methods

SCIM: universal single-cell matching with unpaired feature sets

Stefan G. Stark et al.

BIOINFORMATICS (2020)

Article Multidisciplinary Sciences

A human cell atlas of fetal chromatin accessibility

Silvia Domcke et al.

SCIENCE (2020)

Article Multidisciplinary Sciences

A human cell atlas of fetal gene expression

Junyue Cao et al.

SCIENCE (2020)

Article Biotechnology & Applied Microbiology

Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

Brian Hie et al.

NATURE BIOTECHNOLOGY (2019)

Article Biochemistry & Molecular Biology

Comprehensive Integration of Single-Cell Data

Tim Stuart et al.

Article Biochemistry & Molecular Biology

Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity

Joshua D. Welch et al.

Review Immunology

Human Dendritic Cell Subsets, Ontogeny, and Impact on HIV Infection

Jake William Rhodes et al.

FRONTIERS IN IMMUNOLOGY (2019)

Article Biotechnology & Applied Microbiology

High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell

Song Chen et al.

NATURE BIOTECHNOLOGY (2019)

Article Biochemical Research Methods

Fast, sensitive and accurate integration of single-cell data with Harmony

Ilya Korsunsky et al.

NATURE METHODS (2019)

Article Multidisciplinary Sciences

SCALE method for single-cell ATAC-seq analysis via latent feature extraction

Lei Xiong et al.

NATURE COMMUNICATIONS (2019)

Article Multidisciplinary Sciences

From Louvain to Leiden: guaranteeing well-connected communities

V. A. Traag et al.

SCIENTIFIC REPORTS (2019)

Editorial Material Biochemical Research Methods

OMIP-044: 28-Color Immunophenotyping of the Human Dendritic Cell Compartment

Florian Mair et al.

CYTOMETRY PART A (2018)

Review Immunology

Human dendritic cell subsets: an update

Matthew Collin et al.

IMMUNOLOGY (2018)

Article Multidisciplinary Sciences

Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations

Zhana Duren et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)

Review Biotechnology & Applied Microbiology

Single-Cell DNA Methylation Profiling: Technologies and Biological Applications

Ino D. Karemaker et al.

TRENDS IN BIOTECHNOLOGY (2018)

Article Biochemistry & Molecular Biology

A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility

Darren A. Cusanovich et al.

Article Multidisciplinary Sciences

Developmental diversification of cortical inhibitory interneurons

Christian Mayer et al.

NATURE (2018)

Article Biochemical Research Methods

Deep generative modeling for single-cell transcriptomics

Romain Lopez et al.

NATURE METHODS (2018)

Article Biochemical Research Methods

chromVAR : inferring transcription-factor-associated accessibility from single-cell epigenomic data

Alicia N. Schep et al.

NATURE METHODS (2017)

Article Biochemical Research Methods

Simultaneous epitope and transcriptome measurement in single cells

Marlon Stoeckius et al.

NATURE METHODS (2017)

Article Biotechnology & Applied Microbiology

MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Joshua D. Welch et al.

GENOME BIOLOGY (2017)

Article Immunology

Transcriptome profiling of human FoxP3+ regulatory T cells

Ravikiran Bhairavabhotla et al.

HUMAN IMMUNOLOGY (2016)

Article Multidisciplinary Sciences

Single-cell Hi-C reveals cell-to-cell variability in chromosome structure

Takashi Nagano et al.

NATURE (2013)

Article Biochemical Research Methods

Software for Computing and Annotating Genomic Ranges

Michael Lawrence et al.

PLOS COMPUTATIONAL BIOLOGY (2013)

Review Immunology

FOXP3+ regulatory T cells in the human immune system

Shimon Sakaguchi et al.

NATURE REVIEWS IMMUNOLOGY (2010)

Article Biochemical Research Methods

mRNA-Seq whole-transcriptome analysis of a single cell

Fuchou Tang et al.

NATURE METHODS (2009)

Article Biotechnology & Applied Microbiology

Model-based Analysis of ChIP-Seq (MACS)

Yong Zhang et al.

GENOME BIOLOGY (2008)