Journal
GENOME BIOLOGY
Volume 19, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-018-1536-8
Keywords
Clustering; Gene expression data; Clust; K-means; Cross-clustering; Click; Markov clustering; Hierarchical clustering; Self-organizing maps; WGCNA
Funding
- Bill & Melinda Gates Foundation [OPP1129902]
- European Union's Horizon 2020 research and innovation program [637765]
- Royal Society
- Bill and Melinda Gates Foundation [OPP1129902] Funding Source: Bill and Melinda Gates Foundation
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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 genes and outperforms widely used methods. Additionally, clust can simultaneously cluster multiple datasets, enabling users to leverage the large quantity of public expression data for novel comparative analysis. Clust is available at https://github.com/BaselAbujamous/clust.
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