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Gene Expression Analysis: Differential Expression & Statistical Inference, Study Guides, Projects, Research of Computer Science

This document from utah state university, dated summer 2006, discusses the challenges and solutions in identifying differentially expressed genes through gene expression analysis. Topics include recurring themes in differential expression tests, assumptions in linear models, and the use of hierarchical models to borrow information across genes. The document also provides a quick example of differential expression analysis using r.

Typology: Study Guides, Projects, Research

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Uploaded on 07/30/2009

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Project Introduction:
Gene Expression Analysis
Utah State University
Bioinformatics: Problems and Solutions
Summer 2006
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Project Introduction:Gene Expression Analysis

Utah State UniversityBioinformatics: Problems and SolutionsSummer 2006

Traditional questions & extensions „^

Which genes are really changing expression levelbetween conditions (disease, tissue, genotype,etc.)? „^

How much (and in which direction) are they reallychanging? „^

Where do these genes lie? (chromosomal location) „^

How are these genes different from the others?(gene ontology)

A generalized t-test in a linear model (limma) „^

For gene k under “treatment” j on array i:

„^

What if there are more covariates than justtreatment? –

use matrix notation for convenience:

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ijk

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expressionlevel(log scale)

treatmenteffect (DE)

treatment level (could bemore than just 2 levels)

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Y E

β

covariate effects design matrix (n x m)

log-scale expression vector

Assumptions in linear model (Smyth)

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k not necessary here

Differential expression: a quick example in R

# load datalibrary(ALL);

data(ALL)

# define comparison (based

on knowledge of samples)

eset <- ALL # these are normalized expression levelssampleNames(eset)trt <- c(rep(0,95),rep(1,33)) # 0=B, 1=T# test for differential expression (DE)library(limma)design <- cbind(Intercept=1,trt=trt)fit <- lmFit(eset@exprs,design)e.fit <- eBayes(fit)# Visualize resultstop.all <- topTable(e.fit,n=nrow(eset@exprs),

coef=2,adjust="BH")

hist(top.all$P.Value,main='raw P-value')hist(top.all$adj.P.Val,main='adj. P-value')sum(top.all$adj.P.Val<0.05)

“gene” location info.

“gene” function info.

“gene” DE results

Background „

Barley (Hordeum vulgare)^ ‰

grain used for animal feed (poultry, e.g.)and human use (bread, beer) ‰ Utah is among the top 12 U.S. producers „

Several cultivars (strains or genotypes):Kindred, Peruvian, Beka, … „

Susceptible to pathogens causing leafblotch:

Septoria passerinii

&

Septoria tritici

Questions & Problems „^

Questions: ‰^

Which genes are differentially expressed betweencultivars? ‰^

Where are these genes? ‰^

How are they different from other genes?

„^

Problems: ‰^

Chromosomal location not automatically included in anannotation package for barley – but other sources possible: „^

hvuhomology package for R

„^

barleybase.org
, plantgdb.org, gramene.org

„^

NetAffx from affymetrix.com

‰^

Gene ontology information not available for all probe sets

Project description „^

Analyze these barley data, focusing on: ‰^

graphical display of results ‰^

visualization of chromosomal locations ofsignificant genes ‰^

summarization of possible over-representation ofgene ontology terms among differentiallyexpressed genes

„^

All team members will need to contribute,culminating in poster presentation