Abstract：
:A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows molecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc).
journal_name
J Biotechnoljournal_title
Journal of biotechnologyauthors
Ulfenborg B,Karlsson A,Riveiro M,Andersson CX,Sartipy P,Synnergren Jdoi
10.1016/j.jbiotec.2020.12.002subject
Has Abstractpub_date
2021-01-20 00:00:00pages
1-10eissn
0168-1656issn
1873-4863pii
S0168-1656(20)30324-2journal_volume
326pub_type
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