Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data
Martin Barron,Jun Li +1 more
Reads0
Chats0
TLDR
Wang et al. as discussed by the authors presented ccRemover, a new method that reliably identifies the cell-cycle effect and removes it, which can serve as an important pre-processing step for many scRNA-Seq data analyses.Abstract:
Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for discovering and describing cell types in heterogeneous tissues, yet its measurement of expression often suffers from large systematic bias. A major source of this bias is the cell cycle, which introduces large within-cell-type heterogeneity that can obscure the differences in expression between cell types. The current method for removing the cell-cycle effect is unable to effectively identify this effect and has a high risk of removing other biological components of interest, compromising downstream analysis. We present ccRemover, a new method that reliably identifies the cell-cycle effect and removes it. ccRemover preserves other biological signals of interest in the data and thus can serve as an important pre-processing step for many scRNA-Seq data analyses. The effectiveness of ccRemover is demonstrated using simulation data and three real scRNA-Seq datasets, where it boosts the performance of existing clustering algorithms in distinguishing between cell types.read more
Citations
More filters
Journal ArticleDOI
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.
TL;DR: A practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation is presented.
Journal ArticleDOI
High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer.
Kevin Grosselin,Adeline Durand,Justine Marsolier,Adeline Poitou,Elisabetta Marangoni,Fariba Nemati,Ahmed Dahmani,Sonia Lameiras,Fabien Reyal,Olivia Frenoy,Yannick Pousse,Marcel Reichen,Adam Woolfe,Colin Brenan,Andrew D. Griffiths,Céline Vallot,Annabelle Gérard +16 more
TL;DR: A single-cell chromatin immunoprecipitation followed by sequencing approach paves the way to study the role of chromatin heterogeneity, not just in cancer but in other diseases and healthy systems, notably during cellular differentiation and development.
Journal ArticleDOI
SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species.
Yuqi Tan,Patrick Cahan +1 more
TL;DR: The tool, SingleCellNet, is described, which addresses issues and enables the classification of query single-cell RNA-seq data in comparison to reference single- CellNet data, and it is able to classify across platforms and species.
Journal ArticleDOI
Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney
Rajasree Menon,Edgar A. Otto,Austin Kokoruda,Jian Zhou,Zidong Zhang,Euisik Yoon,Yu Chih Chen,Olga G. Troyanskaya,Jason R. Spence,Matthias Kretzler,Cristina Cebrian +10 more
TL;DR: A comprehensive and dynamic gene expression profile of the developing human kidney at the single-cell level is provided and computational approaches infer developmental trajectories and interrogate the complex network of signaling pathways and cellular transitions.
Journal ArticleDOI
Single-Cell Transcriptomics Bioinformatics and Computational Challenges.
TL;DR: Current state-of-the-art bioinformatics tools and methods for scRNA-Seq analysis are reviewed, as well as addressing some critical analytical challenges that the field faces.
References
More filters
Journal ArticleDOI
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Rafael A. Irizarry,Bridget G. Hobbs,Francois Collin,Yasmin Beazer-Barclay,Kristen J. Antonellis,Uwe Scherf,Terence P. Speed +6 more
TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Journal ArticleDOI
An Introduction to the Bootstrap.
Journal ArticleDOI
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Cole Trapnell,Davide Cacchiarelli,Davide Cacchiarelli,Jonna Grimsby,Prapti Pokharel,Shuqiang Li,Michael A. Morse,Michael A. Morse,Niall J. Lennon,Kenneth J. Livak,Tarjei S. Mikkelsen,Tarjei S. Mikkelsen,John L. Rinn,John L. Rinn,John L. Rinn +14 more
TL;DR: Monocle is described, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points that revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation.
Journal ArticleDOI
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Anoop P. Patel,Itay Tirosh,John J. Trombetta,Alex K. Shalek,Shawn M. Gillespie,Hiroaki Wakimoto,Daniel P. Cahill,Brian V. Nahed,William T. Curry,Robert L. Martuza,David N. Louis,Orit Rozenblatt-Rosen,Mario L. Suvà,Mario L. Suvà,Aviv Regev,Aviv Regev,Aviv Regev,Bradley E. Bernstein,Bradley E. Bernstein,Bradley E. Bernstein +19 more
TL;DR: The genome sequence of single cells isolated from brain glioblastomas was examined, which revealed shared chromosomal changes but also extensive transcription variation, including genes related to signaling, which represent potential therapeutic targets.
Journal ArticleDOI
Orchestrating high-throughput genomic analysis with Bioconductor
Wolfgang Huber,Vincent J. Carey,Robert Gentleman,Simon Anders,Marc R. J. Carlson,Benilton S. Carvalho,Héctor Corrada Bravo,Sean Davis,Laurent Gatto,Thomas Girke,Raphael Gottardo,Florian Hahne,Kasper D. Hansen,Rafael A. Irizarry,Michael S. Lawrence,Michael I. Love,James W. MacDonald,Valerie Obenchain,Andrzej K. Oleś,Hervé Pagès,Alejandro Reyes,Paul Shannon,Gordon K. Smyth,Dan Tenenbaum,Levi Waldron,Martin Morgan +25 more
TL;DR: An overview of Bioconductor, an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology, which comprises 934 interoperable packages contributed by a large, diverse community of scientists.
Related Papers (5)
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
Massively parallel digital transcriptional profiling of single cells
Grace X.Y. Zheng,Jessica M. Terry,Phillip Belgrader,Paul Ryvkin,Zachary Bent,Ryan Wilson,Solongo B. Ziraldo,Tobias Daniel Wheeler,Geoffrey P. McDermott,Junjie Zhu,Mark T. Gregory,Joe Shuga,Luz Montesclaros,Jason G. Underwood,Donald A. Masquelier,Stefanie Y. Nishimura,Michael Schnall-Levin,Paul Wyatt,Christopher Hindson,Rajiv Bharadwaj,Alexander Wong,Kevin D. Ness,Lan Beppu,H. Joachim Deeg,Christopher McFarland,Keith R. Loeb,Keith R. Loeb,William J. Valente,William J. Valente,Nolan G. Ericson,Emily A. Stevens,Jerald P. Radich,Tarjei S. Mikkelsen,Benjamin J. Hindson,Jason H. Bielas +34 more