Bioinformatics 2020 Jun 1; 36(11):3582-3584
GM124922,Chan- Zuckerberg Initiative and Silicon Valley Community Foundation
SUMMARY: Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including 'unwanted' variation that needs to be removed in downstream analyses (e.g. batch effects) and 'wanted' or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying 'wanted' variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods.
AVAILABILITY AND IMPLEMENTATION: The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA.
CONTACT: firstname.lastname@example.org or email@example.com.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Lawlor, Nathan; Marquez, Eladio J; Lee, Donghyung; and Ucar, Duygu, "V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data." (2020). Faculty Research 2020. 117.