Técnicas de análisis de microarrays

Técnicas
de
análisis
de
microarrays
Antonio
Gómez
Tato
Etapas
en
el
análisis
de
Microarrays
•
•
•
•
Segmentación
y
extracción
de
datos.
Filtrado
y
transformación
de
datos.
Normalización.
Análisis
estadísAco
en
función
del
objeAvo
– Análisis
cluster
y
clasificación
– Patrones
de
expresión
temporal
para
buscar
genes
corregulados.
– Búsqueda
de
genes
expresados
de
forma
diferencial
– .....
Segmentación
y
extracción
de
datos
3
Diferentes tipos de microarrays
4
5
6
Primera microarray de rodaballo
7
Imágenes de microarrays
8
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.
Etapas
del
análisis
de
las
imágenes
• Localizar
los
diferentes
spots
(segmentación).
• Decidir
qué
pixels
forman
parte
del
spot
y
cuáles
no.
• Calcular
un
valor
“representaAvo”
de
las
intensidades
de
cada
spot.
• Calcular
el
ruido
de
fondo
(background)
12
Primer paso de la segmentación
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.
13
Segundo
paso
de
la
segmentación
Localizar la región que sólo contenga al spot
que queremos analizar.
14
15
Tercer
paso:
Decidir
qué
es
spot
y
qué
background
16
Intensidades de un spot representadas
en tres dimensiones
20000
15000
Intensid
10000
ad
5000
15
0
10
5
gen10
1,3
can 15
al 2
5
20
17
Tercer
paso:
Decidir
qué
es
spot
y
qué
background
18
Programas
de
extracción
de
datos
de
microarrays
•
•
•
•
•
Spo\inder
(TIGR
suite)
Genepix
Spot
Feature
extracAon
(Agilent)
...
19
Ejemplo
segmentación
automáAca
Con Feature extraction de Agilent y una microarray de oligos.
Cada esquina tiene un patrón diferente
20
Ejemplo
segmentación
automáAca
Localizadas las esquinas y conociendo la
estructura de la microarray estima la
posición de los centros de los spots
21
Ejemplo
segmentación
automáAca
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
22
Resultado
del
proceso
de
segmentación
y
extracción
de
datos
Un archivo, para cada microarray, de (en nuestro
caso) 13544 filas por 67 columnas, que hay que
depurar y analizar.
23
Filtrado
y
transformación
de
datos
24
• Se
eliminan
los
spots
de
baja
calidad
atendiendo
a
diferentes
criterios.
– Forma
irregular
del
spot
– Spot
muy
diferente
de
las
otras
réplicas
del
mismo
gen
en
la
misma
microarray.
– Spot
con
intensidad
no
significaAvamente
diferente
del
background.
– Etc.
25
Ejemplo:
Genes
filtrado
en
una
microarray
de
rodaballo
Desviaciones típicas de las réplicas frente a logaritmo de sus medias
para los genes de la micro Cal_B12C.txt
1.0
●
●
●
Eliminados por filtrado
No eliminados por filtrados
0.6
0.8
●
sd
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0.0
0.2
0.4
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1
2
3
4
5
log(mean)
26
Intensidades de los dos canales
Intensidades de los dos canales
Enfrentar las intensidades
de un canal frente al otro
tiene dos aspectos negativos
6e+05
6e+05
5e+05
5e+05
●
●●
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Intensidades infectado
Intensidades infectado
4e+05
4e+05
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3e+05
3e+05
2e+05
2e+05
1e+05
1e+05
0e+00
0e+00
0e+00
0e+00 1e+05
1e+05 2e+05
2e+05 3e+05
3e+05
4e+05
4e+05
●
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●
1. Casi todos los valores se
encuentran concentrados en
la esquina inferior izquierda
2. La variabilidad aumenta
con la intensidad.
5e+05
5e+05
6e+05
6e+05
intensidades
control
intensidades
control
27
Histogramas de las intensidades antes y después de
aplicar log en base 2.
2000
4000
6000
Sin el logaritmo las
intensidades se acumulan
en los valores bajos
0
0
50000
100000
150000
200000
250000
300000
350000
Intensidades
Histograma de los logaritmos de las intensidades del canal rojo
Con el logaritmo las
intensidades aproximan a
una normal aunque aún
presenta diferencias
1500
Frequency
Frequency
8000
10000
Histograma de las intensidades del canal rojo
1000
500
0
6
8
10
12
14
16
18
Log de las intensidades
28
Después de aplicar logaritmos en ambos canales
20
log Intensidades de los dos canales
15
10
5
●
Los datos están
distribuidos a lo largo del
rango de intensidades y
la variabilidad es la
misma en la mayor parte
de dicho rango
0
log Intensidades infectado
Este gráfico es mejor.
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10
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15
A
30
Normalización
31
Necesidad
de
normalizar
cada
microarray
• En
cada
microarray
se
trata
de
comparar
la
expresión
de
los
genes
en
muestras
biológicas
someAdas
a
dos
condiciones
diferentes.
• La
hipótesis
(biológica)
inicial
es
que
la
mayoría
de
los
genes
no
se
van
a
expresar
de
manera
diferente
en
dichas
condiciones.
• La
distribución
de
los
logaritmos
de
los
raAos
debería
ser
simétrica
con
respecto
al
cero.
32
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.
33
MAPlot con la curva obtenida por regresión no lineal
antes y después de normalizar
Plot MA
Plot MA normalizado
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0
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●
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●
●
● ●
●●
●
●
●
●●●
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●
●
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●
●
●
M
●●
●
M
●
5
●
●
●
●
●●
●
●
●
●
●
●
●
●
−6
●
●
●
●
●
●
●
●
●
●
5
10
15
A
8
10
12
14
16
18
A
34
Necesidad
de
normalizar
las
disAntas
microarrays
entre
ellas
• Las
diferentes
etapas
del
proceso
introducen
errores
(“ruido”)
que
dan
lugar
a
diferencias
“globales”
entre
las
disAntas
microarrys.
• El
proceso
de
normalización
entre
las
microarrays
intenta
reducir
al
mínimo
esas
diferencias
“globales”.
• Los
mecanismos
fundamentales
en
la
normalización
entre
microarrays
son
el
escalado,
el
centrado.
35
Logaritmos de los ratios de las intensidades.
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0
5
●
●
Logaritmos de los ratios de las intensidades. Cada array normalizada
micro2
micro3
micro4
micro5
micro6
36
micro7
Logaritmos de los ratios de las intensidades.
Logaritmos de los ratios de las intensidades. Normalización entre micros
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micro4
micro5
micro6
micro7
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0
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−5
−10
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0
5
●
●
micro3
micro5
micro7
micro1
micro2
micro3
37
Análisis
de
los
datos
38
• Antes
de
empezar
hay
que
preguntarse
para
qué
se
quieren
uAlizar
los
microarrays.
• Como
es
obvio,
el
estudio
estadísAco
dependerá
de
dicho
objeAvo.
• Lo
que
no
es
tan
obvio
es
que
el
diseño
experimental
previo
también
depende
del
objeAvo
buscado.
• Veamos
dos
ejemplos
clásicos
con
disAntos
objeAvos.
39
Primeras
microarrays
• 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.
• Entre
los
primeros
trabajos
se
suele
citar
el
de
Schena
(Science
1995)
sobre
la
monotorización
de
expresión
génica.
• Sin embargo el más citado es el de DeRissi
y sus colaboradores (Science 1997)
40
Uso de análisis cluster
Diferentes patrones
de expresión de
genes de la
levadura en
respuesta a la
disminución de la
concentración de
glucosa.
41
Uso
de
métodos
de
clasificación
Ejemplo: Golub 1999
42
El
trabajo
que
impulsó
a
las
microarrays
• El
trabajo
de
Golub
y
sus
colaboradores
en
1999,
en
el
que
usan
microarrays
para
diagnosAcar
dos
Apos
de
cancer,
impulsa
definiAvamente
el
uso
de
las
microarrays
como
instrumento
de
diagnósAco
e
invesAgación
en
el
campo
de
los
estudios
sobre
el
cancer.
43
• 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.
44
•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.
45
46
•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
47
Descubrimiento
de
clases
• Aplicaron la técnica de “self-organizing
maps” (SOMs).
• La
técnica
predijo
dos
clases
A1
y
A2
con
24
(de
25)
ALL
en
A1
y
10
(de
13)
ALM
en
A2.
48
Búsqueda
de
genes
expresados
diferencialmente
• Si
estamos
en
una
situación
simple
donde
sólo
hay
un
factor
con
dos
niveles
(por
ejemplo:
infectado
o
no
infectado)
podemos
trabajar
con
los
tests
de
hipótesis
habituales.
• Sólo
hay
que
tener
en
cuenta
que
se
están
haciendo
miles
de
ellos
simultáneamente
y
hay
que
hacer
la
corrección
adecuada
en
los
p‐valores
49
Busqueda
de
genes
expresados
de
forma
diferencial
• En
casos
más
complejos
hay
que
recurrir
a
métodos
más
complejos
como
el
análisis
de
la
varianza
o
el
uso
de
modelos
como
en
el
paquete
“limma”
de
Bioconductor.
50
51
R1__610
100
●
R1__421
R1__397
R1__1072
●
R1__450
●
●
●
R1__295
R1__891
R1__1125R1__499
●
R1__2884
●
●
80
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
60
●●
●
●
●
●
●
●
●
●
40
●
●
●
●
●
20
●
●
●
●
●●
●
●
●
●●
●● ●
●●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
● ●
● ●● ●●
●
● ●
●
●●
● ●●
●
● ●● ●●
●
●
●
● ●
● ●●
●
● ●
●
●
●
●
●
●
●
●●
●
●
● ●●
●
●
●
●
●
●
●●
● ●
● ●●
●
●
● ●●
●●●
●
●●
●
●
● ●●
●
●
● ●
●
●
● ● ●●
● ●
●
● ●●●●
●
●
●
●
●
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●
●●
● ● ● ●●
●
● ● ●
●
●
●
● ●
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●
●
●
●
●●● ● ●●
●
●●
● ●● ● ● ● ●
●
●
● ●● ●
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●● ● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
● ● ● ●
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●
●
● ●
● ●
● ●
● ● ●
●
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●
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●
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52
−2
0
2
4
Los resultados del factor slide, normalizados Between
(Intercept)
1065
Slideslide2
42
354
330
442
57
54
Slideslide3
372
53
Apéndice
• Las
microarrays
son
un
instrumento
valioso
en
la
invesAgación
en
biológica
y
medicina.
• Las
técnicas
necesarias
para
su
estudio
son
conocidas
pero
no
triviales.
• Para
ello
se
pueden
usar
la
colección
de
paquetes
estadísAcos
(libres)
desarrollados
en
el
proyecto
Bioconductor
.
• http://www.bioconductor.org/
54