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 ● ● ●● ● 0.0 0.2 0.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●●● ● ●● ● ● ● 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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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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 ● ● 4 ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ●● ● 2 0 −2 −4 ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ●● −5 ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ●●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●● ●● ● ● ●● ● ●● ● ● ●● ● ●● ●● ● ●●●● ● ● ●●●●●●● ● ● ● ●● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ●● ● ● ●●● ● ● ●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ●● ●● ●● ● ●●● ● ●● ● ● ● ●●●● ● ● ●● ● 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normalización entre microarrays son el escalado, el centrado. 35 Logaritmos de los ratios de las intensidades. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● micro3 micro5 micro7 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● micro1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● micro1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −5 −10 −5 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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● micro4 micro5 micro6 micro7 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● micro1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −5 −5 −10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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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Log Odds ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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