Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis
Authors
Keywords
-
Journal
GENOME RESEARCH
Volume 30, Issue 4, Pages 611-621
Publisher
Cold Spring Harbor Laboratory
Online
2020-04-21
DOI
10.1101/gr.247759.118
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Integrating single-cell transcriptomic data across different conditions, technologies, and species
- (2018) Andrew Butler et al. NATURE BIOTECHNOLOGY
- Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity
- (2018) Quy H. Nguyen et al. Nature Communications
- Single-Cell Transcriptional Profiling Reveals Cellular Diversity and Intercommunication in the Mouse Heart
- (2018) Daniel A. Skelly et al. Cell Reports
- Differences in Cell Cycle Status Underlie Transcriptional Heterogeneity in the HSC Compartment
- (2018) Felicia Kathrine Bratt Lauridsen et al. Cell Reports
- Integrated Single Cell Analysis Reveals Cell Cycle and Ontogeny Related Transcriptional Heterogeneity in Hscs
- (2018) Benjamin Povinelli et al. EXPERIMENTAL HEMATOLOGY
- Universal method for robust detection of circadian state from gene expression
- (2018) Rosemary Braun et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
- (2017) Tom Smith et al. GENOME RESEARCH
- Impact of regulatory variation across human iPSCs and differentiated cells
- (2017) Nicholas E. Banovich et al. GENOME RESEARCH
- Genetically Encoded Tools for Optical Dissection of the Mammalian Cell Cycle
- (2017) Asako Sakaue-Sawano et al. MOLECULAR CELL
- Scaling single-cell genomics from phenomenology to mechanism
- (2017) Amos Tanay et al. NATURE
- Human haematopoietic stem cell lineage commitment is a continuous process
- (2017) Lars Velten et al. NATURE CELL BIOLOGY
- SC3: consensus clustering of single-cell RNA-seq data
- (2017) Vladimir Yu Kiselev et al. NATURE METHODS
- Single-cell epigenomics: Recording the past and predicting the future
- (2017) Gavin Kelsey et al. SCIENCE
- Single-cell transcriptomics to explore the immune system in health and disease
- (2017) Michael J. T. Stubbington et al. SCIENCE
- Single-Cell Multiomics: Multiple Measurements from Single Cells
- (2017) Iain C. Macaulay et al. TRENDS IN GENETICS
- Reconstructing cell cycle pseudo time-series via single-cell transcriptome data
- (2017) Zehua Liu et al. Nature Communications
- Visualizing the structure of RNA-seq expression data using grade of membership models
- (2017) Kushal K. Dey et al. PLoS Genetics
- Batch effects and the effective design of single-cell gene expression studies
- (2017) Po-Yuan Tung et al. Scientific Reports
- Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes
- (2017) Mengjie Chen et al. Scientific Reports
- Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations
- (2016) Yong Lu et al. IMMUNITY
- ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system
- (2016) Jacob J. Hughey et al. NUCLEIC ACIDS RESEARCH
- TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
- (2016) Zhicheng Ji et al. NUCLEIC ACIDS RESEARCH
- Effects of cell-cycle-dependent expression on random fluctuations in protein levels
- (2016) Mohammad Soltani et al. Royal Society Open Science
- Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data
- (2016) Martin Barron et al. Scientific Reports
- Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
- (2015) Evan Z. Macosko et al. CELL
- Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
- (2015) Aleksandra A. Kolodziejczyk et al. Cell Stem Cell
- Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells
- (2015) Monika S. Kowalczyk et al. GENOME RESEARCH
- Noise in gene expression is coupled to growth rate
- (2015) Leeat Keren et al. GENOME RESEARCH
- Computational assignment of cell-cycle stage from single-cell transcriptome data
- (2015) Antonio Scialdone et al. METHODS
- Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
- (2015) Florian Buettner et al. NATURE BIOTECHNOLOGY
- Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments
- (2015) Ning Leng et al. NATURE METHODS
- Cell cycle staging of individual cells by fluorescence microscopy
- (2015) Vassilis Roukos et al. Nature Protocols
- Adaptive piecewise polynomial estimation via trend filtering
- (2014) Ryan J. Tibshirani ANNALS OF STATISTICS
- featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
- (2013) Y. Liao et al. BIOINFORMATICS
- The Cell-Cycle State of Stem Cells Determines Cell Fate Propensity
- (2013) Siim Pauklin et al. CELL
- The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
- (2013) Yang Liao et al. NUCLEIC ACIDS RESEARCH
- Genetic Determinants and Cellular Constraints in Noisy Gene Expression
- (2013) A. Sanchez et al. SCIENCE
- Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data
- (2012) Goo Jun et al. AMERICAN JOURNAL OF HUMAN GENETICS
- EBImage--an R package for image processing with applications to cellular phenotypes
- (2010) G. Pau et al. BIOINFORMATICS
- Visualizing Spatiotemporal Dynamics of Multicellular Cell-Cycle Progression
- (2008) Asako Sakaue-Sawano et al. CELL
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started