The self-organizing maps portraying has been proven to be a powerful approach for
analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for
“multi-omic” integrative analyses. However, the SOM method has a major disadvantage: it requires
the retraining of the entire dataset once a new sample is added, which can be resource- and timedemanding.
It also shifts the gene landscape, thus complicating the interpretation and comparison
of results. To overcome this issue, we have developed two approaches of transfer learning that
allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The
extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by
“meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine
regression model on top of the original SOM algorithm to “predict” the portrait of a new sample.
Both methods have been shown to accurately combine existing and new data. With simulated data,
exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time
and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of
the projection methods with independent datasets mapped on existing SOM space. Moreover, both
methods well handle the projection of samples with new characteristics that were not present in
training datasets.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89203 |
| Date | 23 January 2024 |
| Creators | Nikoghosyan, Maria, Loeffler-Wirth, Henry, Davidavyan, Suren, Binder, Hans, Arakelyan, Arsen |
| Publisher | MDPI |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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