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Updated: Oct 18, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Dylan Kotliar^1,2, Andrés Colubri^2,3

¹Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.

Bioinformatics (Oxford, England)

|October 3, 2021

Summary

This summary is machine-generated.

Sciviewer enables interactive visualization of single-cell RNA sequencing (scRNA-Seq) embeddings directly within Python. This tool facilitates seamless integration between visual exploration and programmatic analysis for uncovering biological insights.

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Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques

Single-cell Quantitation of mRNA and Surface Protein Expression in Simian Immunodeficiency Virus-infected CD4+ T Cells Isolated from Rhesus macaques

Published on: September 25, 2018

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-Seq) generates high-dimensional data requiring robust analytical methods.
Visualizing 2D embeddings (e.g., UMAP, tSNE) is crucial for exploring scRNA-Seq data, but current tools often disrupt the iterative analysis workflow.
Existing interactive tools lack seamless data transfer between programming environments and visual interfaces, hindering efficient biological discovery.

Purpose of the Study:

To introduce the Single-cell Interactive Viewer (Sciviewer), a novel tool for interactive visual exploration of scRNA-Seq embeddings within Python.
To enable rapid iteration between programmatic analysis and visual data interrogation.
To provide a method for identifying genes with local variations along user-defined directions in embeddings.

Main Methods:

Development of the Sciviewer tool, integrating interactive visualization with Python-based programmatic analysis.
Implementation of differential expression analysis on user-selected cell populations within the embedding.
Introduction of a novel algorithm to detect genes exhibiting localized expression changes along specified embedding trajectories.

Main Results:

Sciviewer allows users to interactively explore scRNA-Seq embeddings directly within their Python analysis environment.
The tool supports differential expression analysis and a new method for identifying locally varying genes.
Sciviewer facilitates a fluid workflow, connecting visual exploration with computational analysis.

Conclusions:

Sciviewer overcomes the limitations of existing tools by enabling integrated interactive and programmatic analysis of scRNA-Seq data.
The tool enhances the efficiency of uncovering biological signals from high-dimensional single-cell data.
Sciviewer represents a significant advancement for the analysis of complex biological datasets.