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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.
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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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(^0) ,
,^
k
ijk
ijk
jk k
k
ijk^
Var
T
Y
σ
ε
ε
β
β^
=
=
expressionlevel(log scale)
treatmenteffect (DE)
treatment level (could bemore than just 2 levels)
[^
]^
X
Y E
covariate effects design matrix (n x m)
log-scale expression vector
k
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k
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d
k k
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k k wk
k wk
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k k
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2 2
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,
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σ
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χ σ
σ σ
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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: ^
^
^
^
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