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Single-cell RNA-Seq is a cutting edge technology to study cell-specific gene expression patterns and investigate transcriptomic heterogeneity within populations of cells.

How Heterogeneous Are Your Cells?

Genestack’s single cell RNA-seq apps combine trusted open source tools with novel algorithms to produce reusable, interactive analyses and visualizations.

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  • Quality control plots allow you to quantify technical and biological noise easily, and check accuracy of the noise model used on your data
  • Clustered heatmap with dynamic zoom lets you see quickly the expression levels of highly variable genes across all your samples
  • Interactive PCA plot lets you visualize how your single cell samples cluster based on their expression profiles
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  • Use Genestack Expression Navigator to identify differentially expressed genes between clusters of cells
    • Assess interactively technical and biological noise of single cell RNA-Seq data
      • Layer 491

        • Quality control plots allow you to quantify technical and biological noise easily, and check accuracy of the noise model used on your data

        • Clustered heatmap with dynamic zoom lets you see quickly the expression levels of highly variable genes across all your samples

        • Interactive PCA plot lets you visualize how your single cell samples cluster based on their expression profiles

        tab4

        • Use Genestack Expression Navigator to identify differentially expressed genes between clusters of cells
    • Identify genes with highly variable levels of expression across a single-cell population
      • Layer 491

        • Quality control plots allow you to quantify technical and biological noise easily, and check accuracy of the noise model used on your data

        • Clustered heatmap with dynamic zoom lets you see quickly the expression levels of highly variable genes across all your samples

        • Interactive PCA plot lets you visualize how your single cell samples cluster based on their expression profiles

        tab4

        • Use Genestack Expression Navigator to identify differentially expressed genes between clusters of cells
    • Identify transcriptionally homogeneous subpopulations in single-cell RNA-seq datasets
      • Layer 491

        • Quality control plots allow you to quantify technical and biological noise easily, and check accuracy of the noise model used on your data

        • Clustered heatmap with dynamic zoom lets you see quickly the expression levels of highly variable genes across all your samples

        • Interactive PCA plot lets you visualize how your single cell samples cluster based on their expression profiles

        tab4

        • Use Genestack Expression Navigator to identify differentially expressed genes between clusters of cells
    • Identify differential expression markers defining single-cell subpopulations
      • Layer 491

        • Quality control plots allow you to quantify technical and biological noise easily, and check accuracy of the noise model used on your data

        • Clustered heatmap with dynamic zoom lets you see quickly the expression levels of highly variable genes across all your samples

        • Interactive PCA plot lets you visualize how your single cell samples cluster based on their expression profiles

        tab4

        • Use Genestack Expression Navigator to identify differentially expressed genes between clusters of cells

    Simplify your pipeline

    Our pipeline offers a novel method to do single cell RNA-Seq analysis without spike-ins

    This approach allows you to dispense with using spike-in RNAs for the estimation of technical noise in your experimental design. Comparisons with published analyses showed significant overlap with methods based on spike-ins.

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    Raw Reads
    with Single Cell Samples
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    Mapped Reads,
    Raw Gene Counts
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    Single Cell
    RNA-seq Analysis

    This alternative approach does not require the use of spike-in RNAs in your experimental design for the estimation of technical noise; comparisons with published analyses showed a large overlap with methods based on spike-ins.

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    Raw Reads
    with Single Cell Sample and Spike-in Control
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    Mapped Reads,
    Raw Gene Counts (Reference and Spike-ins)
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    Single Cell
    RNA-seq Analysis
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    Our Method Paper

    Genestack’s Single Cell RNA-Seq Analyser and Visualizer apps provide an intuitive user interface to efficiently process and interpret single cell data. Their integration within the Genestack platform enables them to benefit from its highly scalable data model, and they can be seamlessly integrated within fully reproducible data analysis pipelines.

     

    Two web-based applications to explore transcriptomic heterogeneity in single cell RNA-Seq data.

     

    Daniel Ohayon, William Bradshaw, Alessandro Riccombeni, Alex Gutteridge, Misha Kapushesky.

    Biorxiv Pre-publication, Dec 2014.

     

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