Projected t-SNE for batch correction.

Journal Article (Journal Article)

Motivation

Low-dimensional representations of high-dimensional data are routinely employed in biomedical research to visualize, interpret and communicate results from different pipelines. In this article, we propose a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects. Without correction, interesting structure in the data can be obscured by batch effects. The proposed algorithm can therefore significantly aid visualization of high-dimensional data.

Results

The proposed methods are based on linear algebra and constrained optimization, leading to efficient algorithms and fast computation in many high-dimensional settings. Results on artificial single-cell transcription profiling data show that the proposed procedure successfully removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on cell types. When applied to single-cell gene expression data to investigate mouse medulloblastoma, the proposed method successfully removes batches related with mice identifiers and the date of the experiment, while preserving clusters of oligodendrocytes, astrocytes, and endothelial cells and microglia, which are expected to lie in the stroma within or adjacent to the tumours.

Availability and implementation

Source code implementing the proposed approach is available as an R package at https://github.com/emanuelealiverti/BC_tSNE, including a tutorial to reproduce the simulation studies.

Contact

aliverti@stat.unipd.it.

Full Text

Duke Authors

Cited Authors

  • Aliverti, E; Tilson, JL; Filer, DL; Babcock, B; Colaneri, A; Ocasio, J; Gershon, TR; Wilhelmsen, KC; Dunson, DB

Published Date

  • June 2020

Published In

Volume / Issue

  • 36 / 11

Start / End Page

  • 3522 - 3527

PubMed ID

  • 32176244

Pubmed Central ID

  • PMC7267829

Electronic International Standard Serial Number (EISSN)

  • 1367-4811

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btaa189

Language

  • eng