Related references
Note: Only part of the references are listed.Unraveling B cell trajectories at single cell resolution
Dhakshayini Morgan et al.
TRENDS IN IMMUNOLOGY (2022)
BepiTBR: T-B reciprocity enhances B cell epitope prediction
James Zhu et al.
ISCIENCE (2022)
Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization
Haiyang Huang et al.
COMMUNICATIONS BIOLOGY (2022)
Sprod for de-noising spatially resolved transcriptomics data based on position and image information
Yunguan Wang et al.
NATURE METHODS (2022)
Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics
Ze Zhang et al.
NATURE METHODS (2021)
Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data
Tianshi Lu et al.
CELL REPORTS (2021)
Initialization is critical for preserving global data structure in both t-SNE and UMAP
Dmitry Kobak et al.
NATURE BIOTECHNOLOGY (2021)
Humoral and cellular correlates of a novel immune-related adverse event and its treatment
Amrit S. Gonugunta et al.
JOURNAL FOR IMMUNOTHERAPY OF CANCER (2021)
A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data
Ruizhi Xiang et al.
FRONTIERS IN GENETICS (2021)
CyAnno: a semi-automated approach for cell type annotation of mass cytometry datasets
Abhinav Kaushik et al.
BIOINFORMATICS (2021)
Integrated analysis of multimodal single-cell data
Yuhan Hao et al.
CELL (2021)
Deep learning-based prediction of the T cell receptor-antigen binding specificity
Tianshi Lu et al.
NATURE MACHINE INTELLIGENCE (2021)
The single-cell pathology landscape of breast cancer
Hartland W. Jackson et al.
NATURE (2020)
CD22 Expression in B-Cell Acute Lymphoblastic Leukemia: Biological Significance and Implications for Inotuzumab Therapy in Adults
Francesco Lanza et al.
CANCERS (2020)
Mass Cytometry and Single-Cell RNA-seq Profiling of the Heterogeneity in Human Peripheral Blood Mononuclear Cells Interacting with Silver Nanoparticles
My Kieu Ha et al.
SMALL (2020)
A Quantitative Framework for Evaluating Single-Cell Data Structure Preservation by Dimensionality Reduction Techniques
Cody N. Heiser et al.
CELL REPORTS (2020)
Model-based clustering for flow and mass cytometry data with clinical information
Ko Abe et al.
BMC BIOINFORMATICS (2020)
The use and limitations of single-cell mass cytometry for studying human microglia function
Camila Fernandez-Zapata et al.
BRAIN PATHOLOGY (2020)
Systematic Study of the Immune Components after Ischemic Stroke Using CyTOF Techniques
Yaning Li et al.
JOURNAL OF IMMUNOLOGY RESEARCH (2020)
SCIM: universal single-cell matching with unpaired feature sets
Stefan G. Stark et al.
BIOINFORMATICS (2020)
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
George C. Linderman et al.
NATURE METHODS (2019)
In-Depth Characterization of Monocyte-Derived Macrophages using a Mass Cytometry-Based Phagocytosis Assay
Daniel Schulz et al.
SCIENTIFIC REPORTS (2019)
A comparison of single-cell trajectory inference methods
Wouter Saelens et al.
NATURE BIOTECHNOLOGY (2019)
Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry
Fan Zhang et al.
NATURE IMMUNOLOGY (2019)
Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells
Eleni P. Mimitou et al.
NATURE METHODS (2019)
Comprehensive Integration of Single-Cell Data
Tim Stuart et al.
CELL (2019)
Exploring single-cell data with deep multitasking neural networks
Matthew Amodio et al.
NATURE METHODS (2019)
The art of using t-SNE for single-cell transcriptomics
Dmitry Kobak et al.
NATURE COMMUNICATIONS (2019)
Dimensionality reduction for visualizing single-cell data using UMAP
Etienne Becht et al.
NATURE BIOTECHNOLOGY (2019)
Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry
Chotima Boettcher et al.
NATURE NEUROSCIENCE (2019)
Visualizing structure and transitions in high-dimensional biological data
Kevin R. Moon et al.
NATURE BIOTECHNOLOGY (2019)
A comparison framework and guideline of clustering methods for mass cytometry data
Xiao Liu et al.
GENOME BIOLOGY (2019)
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
Shiquan Sun et al.
GENOME BIOLOGY (2019)
Comparison of CyTOF assays across sites: Results of a six-center pilot study
Michael D. Leipold et al.
JOURNAL OF IMMUNOLOGICAL METHODS (2018)
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Andrew Butler et al.
NATURE BIOTECHNOLOGY (2018)
SAVER: gene expression recovery for single-cell RNA sequencing
Mo Huang et al.
NATURE METHODS (2018)
An accurate and robust imputation method scImpute for single-cell RNA-seq data
Wei Vivian Li et al.
NATURE COMMUNICATIONS (2018)
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Jiarui Ding et al.
NATURE COMMUNICATIONS (2018)
Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry
Stephane Chevrier et al.
CELL SYSTEMS (2018)
Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry
Karolyn A. Oetjen et al.
JCI INSIGHT (2018)
The Other Function: Class II-Restricted Antigen Presentation by B Cells
Lital N. Adler et al.
FRONTIERS IN IMMUNOLOGY (2017)
RNA-Seq and CyTOF immuno-profiling of regenerating lacrimal glands identifies a novel subset of cells expressing muscle-related proteins
Dillon Hawley et al.
PLOS ONE (2017)
Applications of Mass Cytometry in Clinical Medicine The Promise and Perils of Clinical CyTOF
Gregory K. Behbehani
CLINICS IN LABORATORY MEDICINE (2017)
Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data
Lukas M. Weber et al.
CYTOMETRY PART A (2016)
Automated mapping of phenotype space with single-cell data
Nikolay Samusik et al.
NATURE METHODS (2016)
High-dimensional genomic data bias correction and data integration using MANCIE
Chongzhi Zang et al.
NATURE COMMUNICATIONS (2016)
Computational flow cytometry: helping to make sense of high-dimensional immunology data
Yvan Saeys et al.
NATURE REVIEWS IMMUNOLOGY (2016)
Diffusion maps for high-dimensional single-cell analysis of differentiation data
Laleh Haghverdi et al.
BIOINFORMATICS (2015)
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Jacob H. Levine et al.
CELL (2015)
FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data
Sofie Van Gassen et al.
CYTOMETRY PART A (2015)
Spatial reconstruction of single-cell gene expression data
Rahul Satija et al.
NATURE BIOTECHNOLOGY (2015)
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
Emma Pierson et al.
GENOME BIOLOGY (2015)
SWIFT-Scalable Clustering for Automated Identification of Rare Cell Populations in Large, High-Dimensional Flow Cytometry Datasets, Part 2: Biological Evaluation
Tim R. Mosmann et al.
CYTOMETRY PART A (2014)
Frequency determination of rare populations by flow cytometry: A hematopoietic stem cell perspective
Alexandra Rundberg Nilsson et al.
CYTOMETRY PART A (2013)
SCREENING CyTOF-the next generation of cell detection
Regina K. Cheung et al.
NATURE REVIEWS RHEUMATOLOGY (2011)
Locality-sensitive hashing for finding nearest neighbors
Malcolm Slaney et al.
IEEE SIGNAL PROCESSING MAGAZINE (2008)
Role of polyamines derived from arginine in differentiation and proliferation of human blood cells
T Maeda et al.
BIOLOGICAL & PHARMACEUTICAL BULLETIN (2006)
Comparing community structure identification -: art. no. P09008
L Danon et al.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2005)
Share Paper
