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Lesson Description Beta This lesson is in the beta phase, which means that it is ready for teaching by instructors outside of the original author team.

    Lesson Description
    Inference for High-dimensional Data
    • Inference for High-dimensional Data
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    • Instructor Notes
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    Inference for High-dimensional Data
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    Summary and Schedule
    1. Example Gene Expression Datasets
    2. Basic inference for high-throughput data
    3. Procedures for Multiple Comparisons
    4. Error Rates
    5. The Bonferroni Correction
    6. False Discovery Rate
    7. Direct Approach to FDR and q-values
    8. Basic EDA for high-throughput data
    9. Principal Components Analysis
    10. Statistical Models

    • Key Points
    • Instructor Notes
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    Instructor Notes

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    Example Gene Expression DatasetsExplore a Gene Expression Dataset


    Basic inference for high-throughput data


    Procedures for Multiple Comparisons


    Error Rates


    The Bonferroni Correction


    False Discovery Rate


    Direct Approach to FDR and q-values


    Basic EDA for high-throughput data


    Principal Components AnalysisWhat is a principal component?How many principal components do we need?Using PCA to analyse gene expression dataUsing PCA output in further analysis


    Statistical ModelsStatistical Models



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