University of Massachusetts Medical School Faculty Publications

Title

Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the python programming environment

UMMS Affiliation

Program in Bioinformatics and Integrative Biology; Department of Microbiology and Physiological Systems

Publication Date

2021-10-02

Document Type

Article

Disciplines

Bioinformatics

Abstract

MOTIVATION: Visualizing two-dimensional (2D) embeddings (such as UMAP or tSNE) is a useful step in interrogating single-cell RNA sequencing (scRNA-Seq) data. Subsequently, users typically iterate between programmatic analyses (including clustering and differential expression) and visual exploration (e.g., coloring cells by interesting features) to uncover biological signals in the data. Interactive tools exist to facilitate visual exploration of embeddings such as performing differential expression on user-selected cells. However, the practical utility of these tools is limited because they don't support rapid movement of data and results to and from the programming environments where most of the data analysis takes place, interrupting the iterative process.

RESULTS: Here, we present the Single-cell Interactive Viewer (Sciviewer), a tool that overcomes this limitation by allowing interactive visual interrogation of embeddings from within Python. Beyond differential expression analysis of user-selected cells, Sciviewer implements a novel method to identify genes varying locally along any user-specified direction on the embedding. Sciviewer enables rapid and flexible iteration between interactive and programmatic modes of scRNA-Seq exploration, illustrating a useful approach for analyzing high-dimensional data.

AVAILABILITY: Code and examples are provided at https://github.com/colabobio/sciviewer. reserved.

DOI of Published Version

10.1093/bioinformatics/btab689

Source

Kotliar D, Colubri A. Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the python programming environment. Bioinformatics. 2021 Oct 2:btab689. doi: 10.1093/bioinformatics/btab689. Epub ahead of print. PMID: 34601589. Link to article on publisher's site

Comments

This article is based on a previously available preprint in bioRxiv.

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

Bioinformatics (Oxford, England)

PubMed ID

34601589

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