BS831 1.20.0

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Markdowns
- Introduction to Genomics Analysis
- Efficient Computation in R
- The ExpressionSet Data Object
- Probability Distributions
- Mixture of Normals
- Preprocessing and Quality Control
- Data Acquisition and ExpressionSet Basics
- Quality Control
- Dimensionality Reduction
- Heatmaps
- Hierarchical Clustering
- Quantile Normalization
- Comparative Experiments I: Microarrays and Linear Models
- Sample Size and Statistical Significance
- Multiple-Hypothesis Correction
- Linear Regression
- Differential Analysis as Linear Regression
- Gene Expression Differential Analysis with Microarrays
- Gene Expression Differential Analysis based on Limma
- Comparative Experiments II: RNA-seq and Generalized Lineary Models
- RNA-Seq Data Scaling and Normalization
- RNA-seq Differential Expression Analysis with DEseq2, edgeR and limma
- Comparative Experiments III: Differential Enrichment Analysis
- HyperGeometric Enrichment
- KS-based Enrichment
- Mapping genesets for KS testing
- Gene Set Projection based on KS
- Geneset projection based on ASSIGN
- Classification
- Feature Redundancy: the Good and the Bad
- Classification Using Caret
- Clustering
- Clustering and Heatmap Visualization
Reference

AEDAT.collapsed.mad4k

Source: R/data.R

AEDAT.collapsed.mad4k.Rd

AEDAT.collapsed.mad4k

AEDAT.collapsed.mad4k

Format

An object of class ExpressionSet with 4000 rows and 47 columns.

Contents

Developed by Stefano Monti, Anthony Federico, Eric Reed, Amy Li.

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