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Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data

Abstract:

Identifying co-expressed gene clusters can provide evidence for genetic or physical interactions. Thus, co-expression clustering is a routine step in large-scale analyses of gene expression data. We show that commonly used clustering methods produce results that substantially disagree and that do not match the biological expectations of co-expressed gene clusters. We present clust, a method that solves these problems by extracting clusters matching the biological expectations of co-expressed ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1186/s13059-018-1536-8

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Plant Sciences
ORCID:
0000-0002-8168-7179
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Plant Sciences
ORCID:
0000-0001-8583-5362
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Funding agency for:
Kelly, S
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Grant:
Horizon 2020, 637765
Publisher:
BioMed Central Publisher's website
Journal:
Genome Biology Journal website
Volume:
19
Pages:
Article: 172
Publication date:
2018-10-25
Acceptance date:
2018-09-07
DOI:
EISSN:
1474-7596
Pubs id:
pubs:896691
URN:
uri:0ecca461-e6ac-4afd-9bb9-2ef70c41f6d2
UUID:
uuid:0ecca461-e6ac-4afd-9bb9-2ef70c41f6d2
Local pid:
pubs:896691

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