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Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish
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scClustViz - Single-cell RNAseq cluster assessment and visualization.

Brendan T Innes1,2, Gary D Bader1,2

  • 1Molecular Genetics, University of Toronto, Toronto, Ontario, M5S3E1, Canada.

F1000Research
|April 27, 2019
PubMed
Summary

scClustViz is a new R Shiny tool that helps researchers explore single-cell RNA sequencing data. It assesses the biological relevance of cell clustering, aiding in the identification of distinct cell types and marker genes.

Keywords:
R Shinydata sharingdifferential expressionfunctional analysisinteractive visualizationsingle-cell RNAseq

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNAseq) enables transcriptome profiling of individual cells within complex mixtures.
  • Characterizing tissue heterogeneity relies on identifying and classifying diverse cell types.
  • Clustering algorithms for scRNAseq data have tunable parameters influencing cluster identification.

Purpose of the Study:

  • To introduce scClustViz, an R Shiny tool with an interactive graphical user interface for exploring scRNAseq data.
  • To provide a method for assessing the biological relevance of clustering results in scRNAseq experiments.
  • To facilitate the selection of optimal clustering parameters for downstream analysis.

Main Methods:

  • scClustViz utilizes differential gene expression between clusters as a metric for evaluating clustering fit.
  • The tool offers interactive visualization of technical factors (e.g., cell cycle stage) and metadata per cluster.
  • It provides cluster-wise gene expression statistics and distributions for cell type annotation and marker gene identification.

Main Results:

  • scClustViz enables interactive assessment and biological interpretation of cell-type classifications from scRNAseq data.
  • The tool visualizes gene expression patterns across all cells and cell types.
  • It aids in identifying cell type-specific marker genes and annotating cell types.

Conclusions:

  • scClustViz simplifies the exploration and interpretation of scRNAseq clustering results for biologists.
  • The tool can be integrated into existing bioinformatic analysis pipelines.
  • It empowers researchers to assess and validate cell-type classifications without extensive computational expertise.