Técnicasdeanálisisdemicroarrays

AntonioGómezTato

EtapasenelanálisisdeMicroarrays• Segmentaciónyextraccióndedatos.

• Filtradoytransformacióndedatos.

• Normalización.

• AnálisisestadísAcoenfuncióndelobjeAvo– Análisisclusteryclasificación

– Patronesdeexpresióntemporalparabuscargenescorregulados.

– Búsquedadegenesexpresadosdeformadiferencial

– .....

Segmentaciónyextraccióndedatos

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4

Diferentes tipos de microarrays

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Primera microarray de rodaballo

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Imágenes de microarrays

La imagen de un microarray que obtiene un scanner no es más que una colección de números.

La imagen de un microarray que obtiene un scanner no es más que una colección de números.

La imagen de un microarray que obtiene un scanner no es más que una colección de números.

Etapasdelanálisisdelasimágenes

• Localizarlosdiferentesspots(segmentación).

• Decidirquépixelsformanpartedelspotycuálesno.

• Calcularunvalor“representaAvo”delasintensidadesdecadaspot.

• Calcularelruidodefondo(background)

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Se coloca una primera rejilla sobre la imagen de la microarray para dividirla en regiones, una por cada una de la agujas del robot de impresión.

Primer paso de la segmentación

Segundopasodelasegmentación

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Localizar la región que sólo contenga al spot que queremos analizar.

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Tercerpaso:Decidirquéesspotyqué

background

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Intensidades de un spot representadas en tres dimensiones

gen 1,3 canal 2

510

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20

5

10

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Intensidad

0

5000

10000

15000

20000

Tercerpaso:Decidirquéesspotyquébackground

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Programasdeextraccióndedatosdemicroarrays

• Spo\inder(TIGRsuite)

• Genepix

• Spot

• FeatureextracAon(Agilent)

• ...

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EjemplosegmentaciónautomáAca

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Con Feature extraction de Agilent y una microarray de oligos.

Cada esquina tiene un patrón diferente

EjemplosegmentaciónautomáAca

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Localizadas las esquinas y conociendo la estructura de la microarray estima la posición de los centros de los spots

EjemplosegmentaciónautomáAca

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En cada centro hipotético coloca tres “galletas” concéntricas radios prefijados.

Los pixels dentro de la primera galleta son los del spot.

Los pixels que sólo están en la galleta más grande son el background local

Resultadodelprocesodesegmentaciónyextraccióndedatos

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Un archivo, para cada microarray, de (en nuestro caso) 13544 filas por 67 columnas, que hay que depurar y analizar.

Filtradoytransformacióndedatos

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• Seeliminanlosspotsdebajacalidadatendiendoadiferentescriterios.– Formairregulardelspot

– Spotmuydiferentedelasotrasréplicasdelmismogenenlamismamicroarray.

– SpotconintensidadnosignificaAvamentediferentedelbackground.

– Etc.

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Ejemplo:Genesfiltradoenunamicroarrayderodaballo

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Eliminados por filtradoNo eliminados por filtrados

Desviaciones típicas de las réplicas frente a logaritmo de sus medias para los genes de la micro Cal_B12C.txt

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0e+00 1e+05 2e+05 3e+05 4e+05 5e+05 6e+05

0e+00

1e+05

2e+05

3e+05

4e+05

5e+05

6e+05

Intensidades de los dos canales

intensidades control

Inte

nsid

ades

infe

ctad

o

Enfrentar las intensidades de un canal frente al otro tiene dos aspectos negativos

1. Casi todos los valores se encuentran concentrados en la esquina inferior izquierda

2. La variabilidad aumenta con la intensidad. ●

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0e+00 1e+05 2e+05 3e+05 4e+05 5e+05 6e+05

0e+00

1e+05

2e+05

3e+05

4e+05

5e+05

6e+05

Intensidades de los dos canales

intensidades control

Inte

nsid

ades

infe

ctad

o

28

Histograma de las intensidades del canal rojo

Intensidades

Frequency

0 50000 100000 150000 200000 250000 300000 350000

02000

4000

6000

8000

10000

Histograma de los logaritmos de las intensidades del canal rojo

Log de las intensidades

Freq

uenc

y

6 8 10 12 14 16 18

0

500

1000

1500

Histogramas de las intensidades antes y después de aplicar log en base 2.

Sin el logaritmo las intensidades se acumulan en los valores bajos

Con el logaritmo las intensidades aproximan a una normal aunque aún presenta diferencias

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0 5 10 15 20

05

1015

20

log Intensidades de los dos canales

log intensidades control

log

Inte

nsid

ades

infe

ctad

o

Este gráfico es mejor.

Los datos están distribuidos a lo largo del rango de intensidades y la variabilidad es la misma en la mayor parte de dicho rango

Después de aplicar logaritmos en ambos canales

Podemosgirarelgráfico

30

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5 10 15

−5

0

5

Plot MA

A

M

Normalización

31

Necesidaddenormalizarcadamicroarray

• EncadamicroarraysetratadecompararlaexpresióndelosgenesenmuestrasbiológicassomeAdasadoscondicionesdiferentes.

• Lahipótesis(biológica)inicialesquelamayoríadelosgenesnosevanaexpresardemaneradiferenteendichascondiciones.

• LadistribucióndeloslogaritmosdelosraAosdeberíasersimétricaconrespectoalcero.

32

33

La regresión LOESS realiza una gran (o pequeña) cantidad de regresiones locales sobre ventanas que se superponen y se van desplazando recorriendo todo el rango de una de las variables.

La unión de todas estas pequeñas curvas locales da lugar a una curva de regresión no lineal.

Es una técnica relativamente sofisticada pero que está implementada en R y Matlab. El ancho de la ventana puede variar bastante la bondad del ajuste.

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5 10 15

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0

5

Plot MA

A

M

MAPlot con la curva obtenida por regresión no lineal antes y después de normalizar

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8 10 12 14 16 18

−6

−4

−2

0

2

4

Plot MA normalizado

A

M

NecesidaddenormalizarlasdisAntasmicroarraysentreellas

• Lasdiferentesetapasdelprocesointroducenerrores(“ruido”)quedanlugaradiferencias“globales”entrelasdisAntasmicroarrys.

• Elprocesodenormalizaciónentrelasmicroarraysintentareduciralmínimoesasdiferencias“globales”.

• Losmecanismosfundamentalesenlanormalizaciónentremicroarrayssonelescalado,elcentrado.

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micro1 micro3 micro5 micro7

−10

−50

5

Logaritmos de los ratios de las intensidades.

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micro1 micro2 micro3 micro4 micro5 micro6 micro7

−50

5

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micro1 micro2 micro3 micro4 micro5 micro6 micro7

−50

5

Logaritmos de los ratios de las intensidades. Normalización entre micros

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micro1 micro3 micro5 micro7

−10

−50

5

Logaritmos de los ratios de las intensidades.

Análisisdelosdatos

38

• AntesdeempezarhayquepreguntarseparaquésequierenuAlizarlosmicroarrays.

• Comoesobvio,elestudioestadísAcodependerádedichoobjeAvo.

• LoquenoestanobvioesqueeldiseñoexperimentalpreviotambiéndependedelobjeAvobuscado.

• VeamosdosejemplosclásicoscondisAntosobjeAvos.

39

Primerasmicroarrays

• Las primeras pruebas de la potencia de los microarrays surgieron en el estudio de la levadura, ya que fue el primer genoma secuenciado y ensamblado.

• EntrelosprimerostrabajossesuelecitareldeSchena(Science1995)sobrelamonotorizacióndeexpresióngénica.

• Sin embargo el más citado es el de DeRissi y sus colaboradores (Science 1997)

40

41

Diferentes patrones de expresión de genes de la levadura en respuesta a la disminución de la concentración de glucosa.

Uso de análisis cluster

Usodemétodosdeclasificación

42

Ejemplo: Golub 1999

Eltrabajoqueimpulsóalasmicroarrays

• EltrabajodeGolubysuscolaboradoresen1999,enelqueusanmicroarraysparadiagnosAcardosAposdecancer,impulsadefiniAvamenteelusodelasmicroarrayscomoinstrumentodediagnósAcoeinvesAgaciónenelcampodelosestudiossobreelcancer.

43

44

• Golub parte de dos tipos de cancer:ALL (acute lymphoblastic leukemia) y AML(acute myeloid leukemia).

• Los métodos de diagnóstico eran sólidos pero costosos. Requerían varias pruebas e involucraban a numerosas especialidades médicas.

45

•Obtuvieron muestras de 38 pacientes (27 ALL y 11 AML) (acute myeloid leukemia).

•El RNA obtenido se hibridó en microarrays de affimetrix que contenían “probes” para 6817 genes.

•Diseñaron un algoritmo de predicción que era capaz de diagnosticar correctamente con 50 genes.

46

47

•El test asignaba correctamente 36 de los 38 casos y los otros dos los dejaba como inciertos.

•Sobre una colección independiente de 34 muestras, predecía correctamente 29 casos

Descubrimientodeclases

• Aplicaron la técnica de “self-organizing maps” (SOMs).

• LatécnicapredijodosclasesA1yA2con24(de25)ALLenA1y10(de13)ALMenA2.

48

Búsquedadegenesexpresadosdiferencialmente

• Siestamosenunasituaciónsimpledondesólohayunfactorcondosniveles(porejemplo:infectadoonoinfectado)podemostrabajarconlostestsdehipótesishabituales.

• Sólohayquetenerencuentaqueseestánhaciendomilesdeellossimultáneamenteyhayquehacerlacorrecciónadecuadaenlosp‐valores

49

Busquedadegenesexpresadosdeformadiferencial

• Encasosmáscomplejoshayquerecurriramétodosmáscomplejoscomoelanálisisdelavarianzaoelusodemodeloscomoenelpaquete“limma”deBioconductor.

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−2 0 2 4

020

4060

8010

0

Los resultados de B1 en la primera slide,grupo1=B11,13,14,grupo2=B12A,B,C

Log Fold Change

Log

Odd

s

R1__610

R1__421 R1__450R1__397R1__1072R1__295R1__891R1__2884 R1__499R1__1125

53

Los resultados del factor slide, normalizados Between

(Intercept) Slideslide2

Slideslide3 372

54

42

57

1065

330

354

442

Apéndice

• LasmicroarrayssonuninstrumentovaliosoenlainvesAgaciónenbiológicaymedicina.

• Lastécnicasnecesariasparasuestudiosonconocidasperonotriviales.

• ParaellosepuedenusarlacoleccióndepaquetesestadísAcos(libres)desarrolladosenelproyectoBioconductor.

• http://www.bioconductor.org/54