Mar 14, 2020

projectR: An R/Bioconductor package for transfer learning via PCA, NMF, correlation, and clustering

Bioinformatics
Gaurav SharmaGenevieve Stein-O'Brien

Abstract

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 import to 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. 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. projectR is available on Bioconductor and at https://github.com/genesofeve/projectR. Supplementary data are available at Bioinformatics online.

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Patient-Controlled Analgesia
Genomics
Dimensionality
Spatial Analysis
Transfer Factor
Validation
Source
Analysis
Psychological Transfer

Related Feeds

CZI Human Cell Atlas Seed Network

The aim of the Human Cell Atlas (HCA) is to build reference maps of all human cells in order to enhance our understanding of health and disease. The Seed Networks for the HCA project aims to bring together collaborators with different areas of expertise in order to facilitate the development of the HCA. Find the latest research from members of the HCA Seed Networks here.