Dimensionality reduction by UMAP to visualize physical and genetic interactions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-03-24

AUTHORS

Michael W. Dorrity, Lauren M. Saunders, Christine Queitsch, Stanley Fields, Cole Trapnell

ABSTRACT

Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae. Proximity in low-dimensional UMAP space identifies groups of genes that correspond to protein complexes and pathways, and finds novel protein interactions, even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms. More... »

PAGES

1537

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-020-15351-4

DOI

http://dx.doi.org/10.1038/s41467-020-15351-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1125828889

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32210240


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198 Department of Medicine, University of Washington, 98195, Seattle, WA, USA
199 rdf:type schema:Organization
 




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