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Microarray data analysis
MBV-‐INFX410 26th Nov, 2012
Ståle Nygård, BioinformaCcs core facility, OUS/UiO [email protected]
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Gene expression Gene expression is the process by which informaCon from a gene is used in the synthesis of a funcConal gene product.
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Microarrays
• Measure the expression of several thousand genes simultaneously
• Are oQen used to find differenCally expressed genes – Between groups of individuals (with different phenotypes, e.g. disease/healthy, long/short survival etc)
– Over Cme (e.g as disease develop, as Cssue develop)
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Development of microarrays • MulCple Northern blots • Macroarrays • cDNA microarrays • OligonucleoCde microarrays • Todays technology: High
density arrays • High througput sequencing
(”Next generaCon sequencing”)
• High througput sequencing
1977
1987
1995
1996
2003
2005
Future
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Development of microarrays • MulCple Northern blots 1977
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• MulCple Northern blots • Macroarrays (spo`ed cDNAs, nylon filter, ~ 1000
gener) 1987
Development of microarrays 1977
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Development of microarrays • MulCple Northern blots • Macroarrays • cDNA microarrays (cDNA probes >200 nt, PCR
produced)
1977
1987
1995
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Development of microarrays • MulCple Northern blots • Macroarrays • cDNA microarrays • OligonucleoCde microarrays (oligos ~50-‐80 nt,
more than 10 000 genes)
1977
1987
1995
1996
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9
Development of microarrays • MulCple Northern blots • Macroarrays • cDNA microarrays • OligonucleoCde microarrays • Todays technology: High
density arrays (e.g Illumina BeadArrays: 50 nt probes, 1 000 000s of probes)
1977
1987
1995
1996
2003
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10
Development of microarrays • MulCple Northern blots • Macroarrays • cDNA microarrays • OligonucleoCde microarrays • Todays technology: High
density arrays • Next generaCon sequencing
(RNA-‐Seq)
1977
1987
1995
1996
2003
2005
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11
Development of microarrays • MulCple Northern blots • Macroarrays • cDNA microarrays • OligonucleoCde microarrays • Todays technology: High density
arrays • Next generaCon sequencing (RNA-‐
seq) • Next-‐next generaCon sequencing:
True single molecule sequencing. E.g NanoPore technology (h`p://www.nanoporetech.com)
1977
1987
1995
1996
2003
2005
Future
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Microarray technology vs RNAseq • Main caveats microarrays:
– Problem with alternaCve splicing; Probes on the microarray might not represent all the (alternaCvely spliced) RNAs
– Problem with degradaCon (less of a problem for RNAseq)
• Main caveats RNA-‐Seq: – Highly expressed genes can take up very much of the space on the slide, giving low accuracy to lowly expressed genes
– RNAseq technology is sCll more expensive than microarrays (but the prices are dropping)
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The experiment pipeline
Biological question
Experimental design
QC of samples
Microarray experiment Preprocessing
of data
Statistical analysis
Biological verification & interpretation
1
2
3
4
QC of data
56
7
8
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Microarray pipline (simplified)
AmplificaCon and
Labelling
RNA/DNA Nucleic acid
purificaCon
Labeled RNA/DNA
HybridisaCon, washing
Bioinformaticanalysis
Scan, QuanCtate
Raw data
E B E`B E pBEBLE ÐB@E @B@E àB@E BhEpBHE °BPE pB‚E`B`EðBE BHE PB$E �BE B�E B@E(E BEBPE €B8E àB$E àB$E PB E#°BLE `B`E àBPE °B E ÐBDE B8E B���B B���E B$E�ÀBLE BE �B`E`B@E"�BTE °B E �B€E @B,E ���ÀB8E%BªE ÀB\E °BHE �B8E @B\E �BLE €B4E àB$E `B E ÀB8E @B4E ðB@E B E àB$E �BDE B<E ÐBTE ���°B,E B$E PB E B@E ðB,E B<E 0BHE €B4E B E @BE B(E €B,E BXE!@BXE `BDE àBdEpBHE B(E#ÀB4E `B4E €B4E °B4E)`B E @B4E 0BDE pBdE`BHE PB E @B E @B�E ÀBE!PB0E pB E"°B E pB,EàBPE B`E��BHE ��� B8EpB���E pB@E B
Pre-‐processing
Sample
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The experiment pipeline
Biological question
Experimental design
QC of samples
Microarray experiment Preprocessing
of data
Statistical analysis
Biological verification & interpretation
1
2
3
4
QC of data
56
7
8
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Experimental design: general strategy
• Ensure that you will not have any systemaCcs biases: – Distribute the biological groups in a balanced way. – Divide into batches of the same sizes, limited b the capacity on each step.
– Tip: In Excel (or similar program) color code sample name according to biological group, and in next column color code by batch.
• Randomize and balance according to the biology your are interested in.
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Experimental plan: an example Biology
A1
A2
A3
A4
A5
A6
B1
B2
B3
B4
B5
B6
C1
C2
C3
C4
C5
C6
Biology Sample prepara3on
order
A1 1 B4 2 C2 3 A3 4 B6 5 C4 6 A5 7 B2 8 C6 9 A2 10 B3 11 C1 12 A4 13 B5 14 C3 15 A6 16 B1 17 C5 18
Biology Sample
prepara3on order
Extrac3on
order
A2 10 1 B6 5 2 C1 12 3 A5 7 4 B5 14 5 C6 9 6 A6 16 7 B4 2 8 C5 18 9 A3 4 10 C3 15 11 B2 8 12 A4 13 13 C4 6 14 B1 17 15 A1 1 16 B3 11 17 C2 3 18
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Experimental design: Batch effect (1)
Samples color coded according to biology
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Exp. design: Batch effect (2)
Samples color coded according to labeling date
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Image analysis of microarray data • Main steps
– Address spots – Separate foreground from background – Quality check: Localize and remove bad quality spots
– Quality check of the microarray as a whole
• AutomaCzaCon ü Commercial plamorms
• Today image analysis is basically an automated procedure performed by the soQware
• Manual quality check is relevant for protein arrays and tailor-‐made microarrays
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NormalizaCon
• Goal: remove technical arCfacts, which can be due to – Different amounts of input material – Different degrees of degradaCon – Dust, scratches etc on the arrays – ++
• Most normalizaCon methods assume that the overall intensity is the same for different samples (e.g quanCle normlizaCon).
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QuanCle normalizaCon • Enforce equal distribuCon between the
microarrays. Procedure – Sort the expression values for each
microarray from highest to lowest – Calculate the mean value for each rank – For every array
• let the highest ranked gene have the mean value of the highest ranked genes (of all arrays)
• Let the second highest ranked gene have the mean value of the second highest ranked genes (of all arrays)
• and so on for all ranks
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NormalizaCon using TMM (Trimmed Mean of M-‐values)
Highly expressed genes having big influence on library size
.
(a)
log2(Kidney1 NK1) − log2(Kidney2 NK2)
Den
sity
-6 -4 -2 0 2 4 6
0.0
0.4
0.8
log2(Liver NL) - log2(Kidney NK)
Den
sity
-6 -4 -2 0 2 4 6
0.0
0.2
0.4(b)
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-20 -15 -10
-50
5
A = log2( Liver NL Kidney NK)
M=
log 2
(Liv
erN
L)-
log 2
(Kid
ney
NK)
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Housekeeping genesUnique to a sample
(c)
In TMM the genes with the smallest and largest raCos (i. e 40% of the genes) are not used in the normalizaCon.
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24
DistribuCon of microarray data • Ordinary scale: noise proporConal to signal, data not normally distributed
• Log2 scale: noise less proporConal to signal, distribuCon closer to normal (a prerequisit for many tests)
Normal scale
Log2 scale
![Page 25: Microarray(dataanalysis( - UiO](https://reader031.vdocuments.mx/reader031/viewer/2022020623/61efb09549c3b7746e1159d4/html5/thumbnails/25.jpg)
TesCng for differenCal expression – microarray data
Ordinary t-‐test: Variance esCmates can be improved by ”borrowing strength” across genes in a technique called variance shrinkage Many methods use this technique, e.g SAM and limma. NB! This technique is relevant only for small sample sizes.
ti =xi − yiσ i
t 'i =xi − yi
B*σ i + (1−B)*σ all
![Page 26: Microarray(dataanalysis( - UiO](https://reader031.vdocuments.mx/reader031/viewer/2022020623/61efb09549c3b7746e1159d4/html5/thumbnails/26.jpg)
DistribuCon of RNAseq data
• What is the distribuCon of counts for a parCcular RNA – Counts from technical replicates are approximately Poisson distributed.
– Biological replicates exhibit more variance, for which the negaCve binomial distribuCon gives a be`er fit. (Ballard et al, 2010)
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Poisson vs negaCve binomial distribuCon
0 5 10 15 20
0.00
0.10
Mean=5
Count
Prob
abilit
y
PoissonNeg.binom (phi=0.01)Neg.binom (phi=0.1)
0 50 100 150 200
0.00
0.02
0.04
Mean=100
Count
Prob
abilit
y
PoissonNeg.binom (phi=0.01)Neg.binom (phi=0.1)
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• Counts are normalized using TMM (Trimmed mean of M-‐values)
• A negaCve binomial distribuCon is assumed and the extra dispersion parameter is esCmated. The parameter can be common to all genes, gene-‐specific, or a combinaCon
The edgeR procedure
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CorrecCon for mulCple tesCng
In ordinary microarray studies (looking at all genes), use false discovery rates instead of ordinary p-‐values
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30
Hierarchical clustering • Genes and samples can
be clustered at the same Cme
• AgglomeraCve: start with one element as a cluster (bo`om-‐up). Most common
• Divisive: start with all elements in one large cluster (top-‐down)
• Dendrogram: a cluster tree
• Why cluster genes? ü Reduce complexity ü Generate hypothesis, e.g.
hypothesize that a group of genes with similar expression profiles interact or are involved in the same process
• Why cluster samples? ü IdenCfy known sub-‐
groups ü Find new or more
detailed subgroups ü Quality check (detect
outliers)
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31
Distance measures • In clustering algorithms two similar elements should be placed in the same cluster
What profiles are most similar?
-‐ Dependent on the distance measure used
X If
Eucledian distance measure is
used
If correlaCon is used as distance measure
X
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Network construcCon based on microarray data
• Network construcCon from genomic data is difficult. Many possible combinaCons of interacCons. • Network construcCon could be guided by including external informaCon about interacCons. • Seeded Bayesian Networks (Djebbari and Quackenbush, 2008) guide the network construcCon by including interacCons reported in literature and protein-‐protein interacCon databases. • The R package Bionet connects regulated genes using a protein-‐protein interacCon database.
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PaCent focused analysis (predicCon/classificaCon)
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ClassificaCon/predicCon approach • Instead of looking at each gene’s correlation to the phenotype
one by one (gene focused analysis), the optimal classification/prediction rule looks at the effect of all genes simultaneously. We then answer the question: what is the effect of gene i when we account for the effect of all other genes.
• Best prediction rule picks out genes with orthogonal (independent) information about the phenotype.
• Methodological problem: How to fit a model with a much larger number (p) of explanatory variables (the genes) than the number of individuals (n). This is called the p > n (p larger than n) problem.
• The solution is to reduce the number of dimensions
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Variance bias trade-‐off • Such methods are in fact biased, i.e underesCmaCng the effect of each gene.
• But they have reduced variance, leading to smaller predicCon error.
• PredicCon error=bias^2 +variance
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Dealing with survival data
• Survival or time to event data have the problem of censoring. Event (e.g. death) does not always occur before end of study.
• The Cox model is the most common model dealing with censoring. In the Cox model the hazard rate , i.e. the instantaneous risk of failure at Cme t, is modeled by
where t is Cme and x is the gene expressions of a specific gene and β is the effect of the gene on survival.