micro arrays ii - image analysis and data pre-processing(1)

8/2/2019 Micro Arrays II - Image Analysis and Data Pre-Processing(1)

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GENOMICA FUNCIONALDR. VCTOR [email protected]

A7-421

Microarrays Image Analysis


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Microarray - Pre-Processing Purpose


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Microarray Image AnalysisTECHNOLOGIES

DNA Probes Oligos~20

40nt

Target(cDNA, PCR products, etc.)

Copies per gene Usually 1Usually 3

OrganizationSectors (print-tip) n x m probsets

Probeset

mprobsets

(~100)

ysectors

(~=3)

x sectors (~=3) n probsets (~100)

Sectorsi x j spots (18x20)

Empty spots

landing lights

perfect match probes (pm)

mismatch probes (mm)

Controls


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Microarray - Image AnalysisTECHNOLOGIES

10,000 genes* 2 dyes

* 3 copies/gene* ~40 pixels/gene

= 2,400,00 values

only 10,000 values

10,000 genes* 20 oligos

* 2 (pm,mm)* ~ 36 pixels/gene

= 14,400,00 values

only 10,000 values

RAW DATA

Image AnalysisPre-processing


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Image Analysis

Addressing:Estimate location of spot centers.Segmentation:Classify pixels as foreground or background.Extraction:For each spot on the array and each dye

foreground intensities background intensities

quality measures.Addressing Done by GeneChipAffymetrix software


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Image Analysis



quality measures.

Addressing (by grid, GenePix)


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Image Analysis



quality measures. Segmentation

Circular featureIrregular feature shape

Finally compute Average


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Background Reduction

Extraction:

DeterminingBackground


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Image Analysis

Segmentation

(Spot detection)

Background

Estimation

Value

Value = Spot Intensity Spot Background

Gene 1

Gene 2Gene 3

.

.

Gene k..

Gene N

Sample 1

100

2097

.

.

9882..

2298

Sample 1

98

42092..

9711..

28


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Data Transformation two dyes

Gene 1Gene 2Gene 3

.

.

Gene k..

Gene N

Sample 1

100209

7..

9882..

2298

Sample 1

984209

2..

9711..

28 G=Sample 1

R=Sample1

Log2(G=Sample 1)

Log2(R=Sample1)

Log2

Microarray Bioinformatics - D. Stekel (Cambridge, 2003)


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Data Transformation two dyes

Gene 1Gene 2Gene 3

.

.

Gene k..

Gene N

Sample 1

100209

7..

9882..

2298

Sample 1

984209

2..

9711..

28

(log2 scale)

R

G1 value?

( )

2

2

2

GRLog

A

G

RLogM

=

=

A

M

MA-PlotG=Sample 1

R=Sample1

Desv

Intensity


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8 10 12 14 16

-4

-3

-2

-1

0

1

(log2(G)+log2(R)) / 2

log2(R)-log2(G)

A

M

"With-in"(2 color technologies)

Normalization 2 dyes

(assumption: Majority No change)


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(assumption: Majority No change)

Before

After

"With-in"(2 color technologies)


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"With-in" Spatial(2 color technologies)

Before Normalization

Aftter loessGlobal Normalization

Aftter loessby Sector (print-tip)

Normalization


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Data Transformation one dye

Gene 1Gene 2Gene 3

.

.

Gene k..

Gene N

Sample 1

100209

7..

9882..

2298

Log2


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7 8 9 10 11 12

0.0

0.5

1.0

1.5

N = 3840 Bandwidth = 0.1051

Density

9 10 11 12 13 14 15 16

0.0

0.2

0.4

0.6

0.8

1.0

log intensity

density

10 11 12 13 14 15

0.0

0.2

0.4

0.6

0.8

x

density

Before normalization After normalization

Between-slides

Normalization 1 or 2 dyes

quantileMAD (median absolute deviation)

scale

qspline

invariantset

loess


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Sumarization = "Average"(Intensities)

Summarization Affymetrix

Oligonucleotide dependent technologies

Usual Methods:tukey-biweightav-diffmedian-polish

PMMM

The "summarization" equivalent intwo-dyes technologies is the average

of gene replicates within the slide.


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Microarrays Filtering / TreatingUndefined Values

Some spots may be defective in the printing process Some spots could not be detected Some spots may be damaged during the assay Artefacts may be presents (bubbles, etc)

Use replicated spots as averages Remove unrecoverable genes Remove problematic spots in all arrays Infer values using computational methods (warning)


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Microarray Data Filtering

More than 10,000 genes Too many data increases Computation Time and analysis

complexity

Remove Genes that do not change significantly Undefined Genes Low expression

Keeping

Large signal to noise ratio Large statistical significance Large variability Large expression


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Data Processing

BackgroundDetection &Subtraction

a)

MicroarrayImage

Scanning SpotDetectionIntensityValue

Affymetrix

Twodyes

b)Image Analysis and Background Subtraction

c)Transformation

BetweenWithin

d)

A=log2(R*G)/2M=log2(R/G)

Normalization

Microarray Pre-Processing Summary


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Image Analysis Exercise

Data processing of Placental Microarrays Dr. Hugo A. Barrera Saldaa Paper in Mol. Med. 2007 : DNA Microarrays - A

Powerful Genomic Tool for Biomedical Research -Trevino - Barrera - Mol Med 2007 Search PubMed for Trevino V


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Experimental DesignGoal : Differential Expression

Placenta 1Placenta 2mRNA Extraction

Reference Pool

Labelling

MicroarrayHybridization(by duplicates)

Scanning &Data Processing

Detection ofDifferentially

Expressed Genes

Validation and

Analysis

Green GreenRed Red

ttestH0: = 0pvalues correction: False Discovery Rate

Comparison With Known Tissue Specific Genes

ImageAnalysis

WithinNormalization

(per array)

BetweenNormalization

(all arrays)

(controls)

(Dr. Hugo Barrera)


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SLIDES' SCANNINGSGROUP SLIDE CY3 (GREEN) CY5(RED) COMMENTS

1a 52 A V Sample Control

1b 52 B V Sample Control

2a 51 A V Sample Control RIGHT TOP GROUP

2b 51 B V Sample Control RIGHT BOTTOM GROUP

3a 56 A V Control Muestra

3b 56 B V Control Muestra

4a A 54 V Control Muestra

4b B 54 V Control Muestra

5a A 55 V Control Control LEFT TOP GROUP

5b B 55 V Control Control LEFT BOTTOM GROUP

6a A 53 V Control Control

6b B 53 V Control Control

Experimental Design - Slides

http://bioinformatica.mty.itesm.mx/?q=node/68

DownloadImages from


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Read ImagesRead BOTH Imagestogether using SpotFinder

Mark file 1 as "Cy3" = GreenMark file 2 as "Cy5" = Red

Adjust Image Brightness and Contrast


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Create Grid

Create GridMetarows = 12,Metacolumns = 4Rows = 24, Columns = 24Pixels = 450 (of the 24 x 24

spots)Spacing = 18 (betweenmetacolumns and metarows)


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Adjust Grid

Adjust each of the 12*4Grids to correctpositions

Right mouse button in agrid to move that gridArrow keys also work

Right mouse button in ablank section to move all

grids

Created Grids are not aligned to the image.

Use VisibleAll (right click in

a blank area)

Use Move AllTo adjust overall

position. Use

visible all to

restore grid.


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Save Grid

Save the grid frequently to avoid loosing your work


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Image Analysis

Use Gridding and Processing Adjust (save grid first, in mac adjust doesnt work well) Process

Copy images 1 From the grid adjust 1 From the RI plot

1 From the data (figure) 2 From the QC view (A and B) What does they represent?

Export to .mev file Open .mev file in excel Remove comment lines Compute signal:

Signal A = Cy3 Green = MNA - MedBkgA = Media del spot A - Mediana delfondo B

Signal B = Cy5 Red = MNB - MedBkgB = Media del spot B - mediana del fondoB

Plot Signal A vs Signal B Copy image in a word file

DO NOT SAVE THE modified .MEV FILE


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Execute Process

- Select Gridding Tab- Use Histogram Segmentation

- Spot Size = 10- Process All !


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Inspect DATA PROCESSED

Select Data Tab

Select a row / spot

See results and interpretoutput


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Inspect MA-PLOT

Select RI-PLOT Tab Observe the MA-PLOT

You can switch on/offspecific grids

A tendency can beobserved (which has

to be corrected to 0see MIDAS exercise)


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Quality Control View

Quality view tab

View 2 gives if each had M > 1(yellow, or 0.5 in this image)or M < -1

View 1 gives the count of all Mvalues per color (yellow, gray, blue,and green)


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Export DATA and VIEW in Excel

Save data to a .mev file

Open .mev file in excel

Remove comment lines(important !)

Compute signal: Signal A = Cy3 Green = MNA -

MedBkgA = Media del spot A -Mediana del fondo B

Signal B = Cy5 Red = MNB - MedBkgB= Media del spot B - mediana delfondo B

Plot Signal A vs Signal B Copy image in a word file

DO NOT SAVE THE modified .MEVFILE

The Plot in Excel shouldbe similar to the MAplot (RI-Plot)


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Resumen del Uso de SpotFinder

Lemos 2 imgenes, Verde=Cy3, Roja=Cy5 paragenerar un valor de intensidad con ruido defondo reducido para cada color:Generamos un grid con la cantidad de spots y diseo

espacial especificado para el microarreglo

Ajustamos las posiciones visualmente moviendo los gridsCalculamos el valor de la seal y el ruido de fondo

para cada colorObtuvimos un archivo con datos

Imagen

Datos

micro arrays ii - image analysis and data pre-processing(1)

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