Single cells make big data: New challenges and opportunities in transcriptomics

Philipp Angerer; Lukas Simon; Sophie Tritschler; F. Alexander Wolf; David Fischer; Fabian J. Theis

doi:10.1016/j.coisb.2017.07.004

Single cells make big data: New challenges and opportunities in transcriptomics

Philipp Angerer, Lukas Simon, Sophie Tritschler, F. Alexander Wolf, David Fischer, Fabian J. Theis

Chair of Mathematical Modelling of Biological Systems

Helmholtz Zentrum München German Research Center for Environmental Health

Research output: Contribution to journal › Review article › peer-review

160 Scopus citations

Abstract

Recent technological advances have enabled unprecedented insight into transcriptomics at the level of single cells. Single cell transcriptomics enables the measurement of transcriptomic information of thousands of single cells in a single experiment. The volume and complexity of resulting data make it a paradigm of big data. Consequently, the field is presented with new scientific and, in particular, analytical challenges where currently no scalable solutions exist. At the same time, exciting opportunities arise from increased resolution of single-cell RNA sequencing data and improved statistical power of ever growing datasets. Big single cell RNA sequencing data promises valuable insights into cellular heterogeneity which may significantly improve our understanding of biology and human disease. This review focuses on single cell transcriptomics and highlights the inherent opportunities and challenges in the context of big data analytics.

Original language	English
Pages (from-to)	85-91
Number of pages	7
Journal	Current Opinion in Systems Biology
Volume	4
DOIs	https://doi.org/10.1016/j.coisb.2017.07.004
State	Published - Aug 2017

Keywords

Big data
Machine learning
Single-cell RNA-seq
Single-cell transcriptomics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.coisb.2017.07.004

Cite this

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title = "Single cells make big data: New challenges and opportunities in transcriptomics",

abstract = "Recent technological advances have enabled unprecedented insight into transcriptomics at the level of single cells. Single cell transcriptomics enables the measurement of transcriptomic information of thousands of single cells in a single experiment. The volume and complexity of resulting data make it a paradigm of big data. Consequently, the field is presented with new scientific and, in particular, analytical challenges where currently no scalable solutions exist. At the same time, exciting opportunities arise from increased resolution of single-cell RNA sequencing data and improved statistical power of ever growing datasets. Big single cell RNA sequencing data promises valuable insights into cellular heterogeneity which may significantly improve our understanding of biology and human disease. This review focuses on single cell transcriptomics and highlights the inherent opportunities and challenges in the context of big data analytics.",

keywords = "Big data, Machine learning, Single-cell RNA-seq, Single-cell transcriptomics",

author = "Philipp Angerer and Lukas Simon and Sophie Tritschler and Wolf, {F. Alexander} and David Fischer and Theis, {Fabian J.}",

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language = "English",

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T1 - Single cells make big data

T2 - New challenges and opportunities in transcriptomics

AU - Angerer, Philipp

AU - Simon, Lukas

AU - Tritschler, Sophie

AU - Wolf, F. Alexander

AU - Fischer, David

AU - Theis, Fabian J.

PY - 2017/8

Y1 - 2017/8

N2 - Recent technological advances have enabled unprecedented insight into transcriptomics at the level of single cells. Single cell transcriptomics enables the measurement of transcriptomic information of thousands of single cells in a single experiment. The volume and complexity of resulting data make it a paradigm of big data. Consequently, the field is presented with new scientific and, in particular, analytical challenges where currently no scalable solutions exist. At the same time, exciting opportunities arise from increased resolution of single-cell RNA sequencing data and improved statistical power of ever growing datasets. Big single cell RNA sequencing data promises valuable insights into cellular heterogeneity which may significantly improve our understanding of biology and human disease. This review focuses on single cell transcriptomics and highlights the inherent opportunities and challenges in the context of big data analytics.

AB - Recent technological advances have enabled unprecedented insight into transcriptomics at the level of single cells. Single cell transcriptomics enables the measurement of transcriptomic information of thousands of single cells in a single experiment. The volume and complexity of resulting data make it a paradigm of big data. Consequently, the field is presented with new scientific and, in particular, analytical challenges where currently no scalable solutions exist. At the same time, exciting opportunities arise from increased resolution of single-cell RNA sequencing data and improved statistical power of ever growing datasets. Big single cell RNA sequencing data promises valuable insights into cellular heterogeneity which may significantly improve our understanding of biology and human disease. This review focuses on single cell transcriptomics and highlights the inherent opportunities and challenges in the context of big data analytics.

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KW - Machine learning

KW - Single-cell RNA-seq

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DO - 10.1016/j.coisb.2017.07.004

M3 - Review article

AN - SCOPUS:85041430720

SN - 2452-3100

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SP - 85

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JO - Current Opinion in Systems Biology

JF - Current Opinion in Systems Biology

ER -

Single cells make big data: New challenges and opportunities in transcriptomics

Abstract

Keywords

UN SDGs

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