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DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics.

Salim Ghannoum1, Waldir Leoncio Netto2, Damiano Fantini3

  • 1Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway.

International Journal of Molecular Sciences
|February 12, 2021
PubMed
Summary

DIscBIO is an open-source pipeline simplifying transcriptomic analysis for cellular sub-populations. It integrates multiple tools for reproducible biomarker discovery and gene enrichment, aiding researchers with limited programming skills.

Keywords:
DEGsERCC spike-insJupyter notebookbinderbiomarkersdecision treesgene filteringnetwork analysisnormalizationsingle-cell sequencing

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Area of Science:

  • Computational Biology
  • Transcriptomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) generates complex data.
  • Integrating multiple computational packages for scRNA-seq analysis is challenging for non-programmers.
  • Reproducible analysis of cellular sub-populations is crucial.

Purpose of the Study:

  • To present DIscBIO, an open-source, multi-algorithmic pipeline for scRNA-seq data analysis.
  • To enable easy, efficient, and reproducible identification of cellular sub-populations and biomarkers.
  • To facilitate gene enrichment analysis in a network context.

Main Methods:

  • Integration of multiple scRNA-seq analysis packages into a single pipeline.
  • Utilizes clustering and differential analysis on single-cell sequencing read counts.
  • Incorporates decision trees for biomarker discovery and network-based gene enrichment analysis.
  • Provides analysis via R package, command-line, and user-friendly Jupyter notebooks.
  • Offers a cloud-based version using Binder for accessibility.

Main Results:

  • DIscBIO successfully analyzes transcriptomic data to identify cellular sub-populations.
  • Demonstrated biomarker discovery and gene enrichment capabilities using real-world datasets (breast cancer CTCs, myxoid liposarcoma cell cycle).
  • Notebooks provide a narrative guide for R users to understand and apply the pipeline.
  • Cloud version serves as a tutorial for users with limited programming experience.

Conclusions:

  • DIscBIO enhances the accessibility and reproducibility of scRNA-seq data analysis.
  • The pipeline empowers researchers, including those with limited programming skills, to perform complex transcriptomic analyses.
  • DIscBIO facilitates biomarker discovery and gene enrichment, advancing cellular sub-population research.