Related references
Note: Only part of the references are listed.Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data
Elise Amblard et al.
BIOINFORMATICS (2022)
Current trends in flow cytometry automated data analysis software
Melissa Cheung et al.
CYTOMETRY PART A (2021)
Comprehensive analysis of single cell ATAC-seq data with SnapATAC
Rongxin Fang et al.
NATURE COMMUNICATIONS (2021)
DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data
Bobby Ranjan et al.
NATURE COMMUNICATIONS (2021)
Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
Tian Tian et al.
NATURE COMMUNICATIONS (2021)
Integrated analysis of multimodal single-cell data
Yuhan Hao et al.
CELL (2021)
Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection
Nejat Arinik et al.
IEEE ACCESS (2021)
PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells
Shobana V. Stassen et al.
BIOINFORMATICS (2020)
SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection
Shibiao Wan et al.
GENOME RESEARCH (2020)
Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
Yu A. Malkov et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)
Single-cell RNA-seq clustering: datasets, models, and algorithms
Lihong Peng et al.
RNA BIOLOGY (2020)
Embracing the dropouts in single-cell RNA-seq analysis
Peng Qiu
NATURE COMMUNICATIONS (2020)
Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study
Chao Feng et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2020)
Tools for the analysis of high-dimensional single-cell RNA sequencing data
Yan Wu et al.
NATURE REVIEWS NEPHROLOGY (2020)
Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data
Peng Liu et al.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY (2020)
Single-cell RNA-seq data clustering: A survey with performance comparison study
Ruiyi Li et al.
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (2020)
SCHNEL: scalable clustering of high dimensional single-cell data
Tamim Abdelaal et al.
BIOINFORMATICS (2020)
GRACE: A Graph-Based Cluster Ensemble Approach for Single-Cell RNA-Seq Data Clustering
Jihong Guan et al.
IEEE ACCESS (2020)
Demystifying drop-outs in single-cell UMI data
Tae Hyun Kim et al.
GENOME BIOLOGY (2020)
Benchmarking principal component analysis for large-scale single-cell RNA-sequencing
Koki Tsuyuzaki et al.
GENOME BIOLOGY (2020)
Impact of similarity metrics on single-cell RNA-seq data clustering
Taiyun Kim et al.
BRIEFINGS IN BIOINFORMATICS (2019)
High-Dimensional Single-Cell Cartography Reveals Novel Skeletal Muscle-Resident Cell Populations
Lorenzo Giordani et al.
MOLECULAR CELL (2019)
Comprehensive Integration of Single-Cell Data
Tim Stuart et al.
CELL (2019)
Challenges in unsupervised clustering of single-cell RNA-seq data
Vladimir Yu Kiselev et al.
NATURE REVIEWS GENETICS (2019)
Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA Sequencing Clustering Methods
Monika Krzak et al.
FRONTIERS IN GENETICS (2019)
Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things
Christophe Guyeux et al.
JOURNAL OF SENSOR AND ACTUATOR NETWORKS (2019)
A comparison framework and guideline of clustering methods for mass cytometry data
Xiao Liu et al.
GENOME BIOLOGY (2019)
From Louvain to Leiden: guaranteeing well-connected communities
V. A. Traag et al.
SCIENTIFIC REPORTS (2019)
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Alexander B. Rosenberg et al.
SCIENCE (2018)
Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry
Stephane Chevrier et al.
CELL SYSTEMS (2018)
Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes
Roman A. Romanov et al.
NATURE NEUROSCIENCE (2017)
Comparative Analysis of Single-Cell RNA Sequencing Methods
Christoph Ziegenhain et al.
MOLECULAR CELL (2017)
Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors
Huipeng Li et al.
NATURE GENETICS (2017)
Massively parallel digital transcriptional profiling of single cells
Grace X. Y. Zheng et al.
NATURE COMMUNICATIONS (2017)
Mass Cytometry: Single Cells, Many Features
Matthew H. Spitzer et al.
CELL (2016)
Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos
Mubeen Goolam et al.
CELL (2016)
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)
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
Maayan Baron et al.
CELL SYSTEMS (2016)
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Jacob H. Levine et al.
CELL (2015)
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Amit Zeisel et al.
SCIENCE (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)
A deep profiler's guide to cytometry
Sean C. Bendall et al.
TRENDS IN IMMUNOLOGY (2012)
Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE
Peng Qiu et al.
NATURE BIOTECHNOLOGY (2011)
Fast unfolding of communities in large networks
Vincent D. Blondel et al.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2008)
Defining and identifying communities in networks
F Radicchi et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2004)
Finding and evaluating community structure in networks
MEJ Newman et al.
PHYSICAL REVIEW E (2004)
Share Paper
