Last updated: 2020-04-12

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Introduction

This is a summary of Irizarry et al 2009

Goal: Incorporating biological information into differential expression analysis.

The very common differential expression analysis is referred to as margianl approach: decide on a null hypothesis, test this hypothesis for each gene, produce a p-value, and attach a significance level that accounts for multiplicity.

In gesa, gene categories or gene sets are formed prior to the statistical analysis. It is more common to use category definitions from the Gene Ontology project.

Aggregate score approach: assign scores to each gene set based on all the gene-specific scores for that gene set.

For a gene set \(A_g\), each gene in it has a t-statistics \(t_i\), for \(i\in A_g\). We want to assign a gene set score \(E_g\) and a significant level that tells how \(t_i, i\in A_g\) differ from \(t_i,i\notin A_g\).

Some test proposed: K-S test, t-test, \(\chi^2\) test.

Can we do simultaneous RUV, and GESA? via regression and factor analysis.

Also think about regresing covariate on loadings.