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Inference for High-dimensional Data
Inference for High-dimensional Data
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Inference for High-dimensional Data
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EPISODES
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
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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 data
Using PCA output in further analysis
Statistical ModelsStatistical Models
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