Microarray(dataanalysis( - UiO

Microarray data analysis

MBV-‐INFX410 26th Nov, 2012

Ståle Nygård, BioinformaCcs core facility, OUS/UiO [email protected]

Gene expression Gene expression is the process by which informaCon from a gene is used in the synthesis of a funcConal gene product.

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)

4

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

5

Development of microarrays •  MulCple Northern blots 1977

6

•  MulCple Northern blots •  Macroarrays (spoèd cDNAs, nylon filter, ~ 1000

gener) 1987

Development of microarrays 1977

7

Development of microarrays •  MulCple Northern blots •  Macroarrays •  cDNA microarrays (cDNA probes >200 nt, PCR

produced)

1977

1987

1995

8

Development of microarrays •  MulCple Northern blots •  Macroarrays •  cDNA microarrays •  OligonucleoCde microarrays (oligos ~50-‐80 nt,

more than 10 000 genes)

1977

1987

1995

1996

9


density arrays (e.g Illumina BeadArrays: 50 nt probes, 1 000 000s of probes)

1977

1987

1995

1996

2003

10


density arrays •  Next generaCon sequencing

(RNA-‐Seq)

1977

1987

1995

1996

2003

2005

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

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)

13

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

14

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`BÈðBE BHE PB$E �BE B�E B@E(E BEBPE €B8E àB$E àB$E PB E#°BLE `BÈ àBPE °B E ÐBDE B8E B��B B��E B$E�ÀBLE BE �BÈ`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 BÈ��BHE �� B8EpB��E pB@E B

Pre-‐processing

Sample

15

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

16

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.

17

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

18

Experimental design: Batch effect (1)

Samples color coded according to biology

19

Exp. design: Batch effect (2)

Samples color coded according to labeling date

20

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

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).

22

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

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.

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

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

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 beèr fit. (Ballard et al, 2010)

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)

•  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

CorrecCon for mulCple tesCng

In ordinary microarray studies (looking at all genes), use false discovery rates instead of ordinary p-‐values

30

Hierarchical clustering •  Genes and samples can

be clustered at the same Cme

•  AgglomeraCve: start with one element as a cluster (boòm-‐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)

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

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.

PaCent focused analysis (predicCon/classificaCon)

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

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

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.

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Microarray(dataanalysis( - UiO