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Computational approaches for high‐throughput single‐cell data analysis
Author(s) -
Todorov Helena,
Saeys Yvan
Publication year - 2019
Publication title -
the febs journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 204
eISSN - 1742-4658
pISSN - 1742-464X
DOI - 10.1111/febs.14613
Subject(s) - epigenome , computer science , variety (cybernetics) , computational biology , data science , throughput , biological data , proteome , biology , bioinformatics , artificial intelligence , genetics , dna methylation , telecommunications , gene expression , gene , wireless
During the past decade, the number of novel technologies to interrogate biological systems at the single‐cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single‐cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high‐dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single‐cell data in an automated and unbiased way.