Dataset Information
ProjectR: an R/Bioconductor package for transfer learning via PCA, NMF, correlation and clustering.
Ontology highlight
ABSTRACT: Motivation
Dimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically importent in analysis of large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset.Results
We developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis.Availability and implementation
projectR is available on Bioconductor and at https://github.com/genesofeve/projectR.Contact
gsteinobrien@jhmi.edu or ejfertig@jhmi.edu.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Sharma G
PROVIDER: S-EPMC7267840 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
projectR: an R/Bioconductor package for transfer learning via PCA, NMF, correlation and clustering.
Bioinformatics (Oxford, England) 20200601 11
<h4>Motivation</h4>Dimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically importent in analysis of large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to ...[more]