course vhir-ucts-ueb - session 3 - statistical analysis
DESCRIPTION
High throughput technologies in Genomics - Tecnologías de alto rendimiento en genómica. Session 3: Statistical Analysis Course held at Vall d'Hebron Research Institute (VHIR), in Barcelona, Catalonia, Spain, on October 5th, 2011.TRANSCRIPT
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Statistical analysis of gene expression data
Alex SánchezUnitat d'Estadística i Bioinformàtica (VHIR)
Statistics Department (UB)
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Who, where, what?
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Outline
• Basic principles of experimental design• Analysis of RT-qPCR data• The microarray data analysis process
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Basic principles of Experimental Design
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To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.
Father of modern Mathematical Statistics and Developer of Experimental Design and ANOVA
Sir Ronald A. Fisher
And Fisher said…
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The three basic principles of Experimental Design
• Apply the following principles to best attain the objectives of experimental design– Replication– Local control or Blocking– Randomization
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1. Replication
• Each treatment must be applied independently to several experimental units.
• Provides the means to estimate the EE variance in the absence of systematic differences among EUs treated alike which is important because treatment differences are judged against the EE variance.
• Provides the capacity to increase the precision for estimates of treatment means.
• By itself, does not guarantee valid estimates of EE or treatment differences.
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Biological vs Technical Replicates
@ Nature reviews & G. Churchill (2002)
2Bσ
2Aσ
2eσ
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Replication vs Pooling
• mRNA from different samples are often combined to form a ``pooled-sample’’ or pool. Why?– If each sample doesn’t yield enough mRNA– To compensate an excess of variability ?
• Statisticians tend not to like it but pooling may be OK if properly done– Combine several samples in each pool– Use several pools from different samples– Do not use pools when individual information is
important (e.g.paired designs)
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Examples of “pooling”
• Study with 12 patients 12 chips Expensive– Optiob 1:
• Group A: 6 individuals 1 pool of 6 1 chip• Group B: 6 individuals 1 pool of 6 1 chip
– Option 2: • Group A: 12 individuals 4 pools of 3 4 chip• Grupo B: 12 individuals 4 pools of 3 4 chip
– Option 2 may be cheaper and, at the samae time have similar precisioHowever, without having information about variability within pools and between individuals it cannot be assured
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Local Control
• Group EUs so that the variability of units within the groups is less than that among all units prior to grouping – Differences among treatments are not confused with
differences among experimental units. – EE is reduced by the variability associated with
environmental differences among groups of units.– Effects of nuisance factors which contribute
systematic variation to the differences among EUs can be eliminated.
– Analysis is more sensitive.
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Confounding block with treatment effects
Awful design Balanced designSample Treatment Sex Batch Sample Treatment Sex Batch
1 A Male 1 1 A Male 12 A Male 1 2 A Female 23 A Male 1 3 A Male 24 A Male 1 4 A Female 15 B Female 2 5 B Male 16 B Female 2 6 B Female 27 B Female 2 7 B Male 28 B Female 2 8 B Female 1
• Two alternative designs to investigate treatment effects– Left: Treatment effects confounded with Sex and Batch
effect– Right: Treatments are balanced between blocks
• Influence of blocks is automatically compensated• Statistical analysis may separate block from treatment efefect
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3. Randomisation
• Randomly assigning samples to groups to eliminate unspecific disturbances– Randomly assign individuals to treatments.– Randomise order in which experiments are
performed.
• Randomisation required to – ensure validity of statistical procedures.– Lead to unbiased estimates of variances and
unbiased estimates of treatment differences,– Simulates the effects of independence among
EUs that are otherwise controlled, selected, and monitored.
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Allocating samples to treatments
• A key point in any experiment is the way that experimental units are allocated to treatments– It must be chosen so that random variability
is as small as possible– It must be chosen so that the best local
control is achieved. – It implicitly defines the analysis model, so it
must be chosen so that the analysis can be performed and validity conditions hold.
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Scary stories: batch effects
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Efecto Batch en Microarrays
Diferencias/variaciones no biológicas observadas en experimentos de microarrays
Origen:
•Técnico que procesa las muestras
•Amplificación
•Lote del kit de tinción
•Reparto de muestras en las tandas de amplificación
•Kit de amplificación....
No suele invalidar el expeimento aunque si añade una cantidad de ruído no cuantificable
Solemos conocer la fuente pero no siempre se podrá cuantificar y/o eliminar!!!
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Técnico que procesa las muestras
Técnico 1: procesa muestras control
Técnico 2: procesa muestras problema
Técnico 1: procesa muestras control y problema
Técnico 2: procesa muestras problema y control
SOLUCION
Técnico 1 y 2 no comparten proyecto
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Reparto de muestras en las tandas de amplificación
12 muestras máximo por tanda de amplificación
Proyectos n>12 muestras se han de repartir en diferentes tandas de amplificación
Tanda 1: Controles
Tanda 2: muestras problema
Tanda 1: se procesan muestras control y problema
Tanda 2: se procesan muestras problema y control
SOLUCION
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Lote del Kit de tinción
Sondas se marcan con ficoeritrina
Va perdiendo intensidad con el tiempo
Hibridar cada tanda de 12 muestras
Esperar a tener todas las muestras preparadas e hibridarlas todas a la vez
SOLUCION
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Eliminación del efecto batch
• Con un diseño experimental apropiado el efecto batch se puede eliminar o atenuar
• de forma implícita balanceando las muestras entre distinos lotes
• de forma explícita estimando los efectos del batch y substrayéndolos de los valores originales.
• Si el diseño no es adecuado, (e.g. hay CONFUSIÓN entre lote y tratamientos) no se podrá hacer nada.
• Incluso con un buen diseño no se puede realizar la eliminación de muchos efectos batch de forma indefinida, porque cada vez se pierde más potencia estadística.
• Es fácil que al final tengamos que aceptar algún efecto batch.
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EJEMPLOS-1 Efecto del kit de marcaje
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EJEMPLOS-2
fileName Camada Grupo ShortName Colores
E39+_-.CEL 1 1 E39pm11 yellow
E39+_+.CEL 1 2 E39pp21 green
E40+_-.CEL 2 1 E40pm12 yellow
E40+_+.CEL 2 2 E40pp22 green
E41+_-.CEL 3 1 E41pm23 yellow
E41+_+.CEL 3 2 E41pp13 green
E42+_-.CEL 4 1 E42pm24 yellow
E42+_+.CEL 4 2 E42pp14 green
Efecto batch de nacimiento
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SIN CORREGIR
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CORREGIDO
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In summary
• Good experimental design is essential to perform good experiments.
• Experimental design means planning ahead– Should be done before the experiment starts– Should consider all the steps: from sampling
to data analysis.
• Not a question of "statistical snobism" but of saving time and money and of doing good science
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Basic aspects of qPCR data analysis
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Outline
• Common types of qPCR data analyses• Biostatistical aspects of relative
quantification• Confirmatory and exploratory statistical
analysis.
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Real time qPCR data
• RT-qPCR data are CT or threshold cycle values.
– CT= Cycle number at which detectable signal is achieved.
– The Lower/higher the CT Larger/Smaller amount of starting material
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Basic types of RT-qPCR analysis
• Two basic types of analysis– Absolute quantification– Relative quantification
• Choice based on– Experimental goals– Available resources
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Absolute quantification
• Use absolute quantification…– To understand properties that are intrinsic
to a given sample.– To answer the question "how many"?
• Examples of applications– Chromosome or gene copy number
determination– Viral load measurements
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Standard curve
• Absolute quantification is achieved by comparing CT values of each sample to a standard curve, which is obtained by– Using different known amounts of sample
– For which CT is calculated
– And plotted vs the (log) (known) quantity
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Standard Calibration Curve
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Example: determining absolute copy number from absolute quantification
• The standard curve is used only for interpolation but not for extrapolation (relation may not be linear outside the limits tested).
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Absolute vs Relative quantifications
• Absolute quantification answers the question "how many" but gives no information about change.
• Relative quantification can be used to– Compare levels or changes in gene
expression.– Answer the question – What is the fold
difference?
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Relative quantification methods
• For absolute quantification one requires a standard template with several known concentrations to build the curve.
• For relative quantification one needs to apply some form of normalization, that is one has to transform the data in order to– Remove possible experimental biases– Make data from different samples/groups
comparable so that the term "relative" keeps its meaning.
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Normalization against a unit mass
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Normalization against a reference gene
• Benefit: – Circumvents need for accurate
quantification of starting material
• Drawback: – Requires known reference genes with stable
expression levels
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Required CT values
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Most common approaches
• Livak or ∆∆CT method
• The ∆CT method against a reference gene
• The Pfaffl method
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Livak method (1)
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Livak method (2)
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Other methods
• Although Livak method is the most used
• The ∆CT method yields equivalent results but is simpler to calculate.
• The Pfaffl method is preferable when reaction efficiencies of the target and reference are not similar.
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Biostatistical aspects of relative quantification
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Biostatistical analysis
• Two main types of analyses– Comparative analyses,
• Relatively rigorous• Check a predefined hypotheses• Relies on statistical testing
– Expression profiling: Search for trends and patterns in the data• Exploratory, hypothesis generating approach• Less rigorous • Cluster analysis or PCA
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Relative quantification
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Expression profiling
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Three basic premises
• Statistical analyses of RT-qPCR data relies on three assumptions– One gene-at-a-time– We are sampling from two different
(unknown) independent populations– There exist unknown mechanisms that
contribute to variability.
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From assumptions to strategies (1)
• Use random sampling and randomization to obtain independent and representative samples.
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From assumptions to strategies (2)
• Apply experimental design principles to minimize confounding variability
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From assumptions to strategies (3)
• Perform statistical testing• DO NOT FORGET about multiple testing adjustments
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Statistical analysis
• Standard statistical approach: Confirmatory study-Reject or accept predefined hypothesis
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Comparing two groups…
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Comparing more than two groups
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Exploratory statistical analysis
• If instead of confirming hypothesis we want to generate them (finding patterns in data)
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Multivariate methods for exploratory data analysis
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Software for the analysis
• ABI– DataAssist
• Biogazelle– REST
• Bio-Rad– GENEX (Gene expression macro)
• Multid– GenEx
• Bioconductor– HTqPCR
• Integromics– StatMiner
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Introduction to microarray data analysis
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Esquema de la presentación
Introducción y objetivosAnálisis de datos de microarrays
Tipos de datos y Tipos de estudios. Herramientas. El proceso de análisis. Ejemplos
Críticas, consensos, consejos y “estado del arte” Críticas a los microarrays Consensos y consejos (“dos and don’ts”) MAQC-I, MAQC-II
De los microarrays al diagnóstico ¿Porque está siempre por llegar?
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Para aprender más …
http://www.ub.es/stat/docencia/bioinformatica/microarrays/ADM/
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Tipos de estudios
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(1): Class comparison
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(2): Class discovery
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(3): Class prediction
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Y muchos más …
Time Course Perfiles de expresión a lo largo del tiempo
Pathway Analysis-(Systems Biology) Reconstrucción de redes metabólicas a partir
de datos de expressión
Whole Genome, CGH, Alternative Splicing
Estudios con datos de distintos tipos Fusión o Integración de datos
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Herramientas para el análisis
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Programas de análisis de datos
Multitud de herramientas Gratuítas / Comerciales [R, BRB, MeV, dChip…] / [Partek, GeneSpring, Ingenuity] Descargables / En-linea [R, BRB, MeV…] / [Gepas,…] Aísladas / Parte de “suites” o de sitios [BRB, dChip] / [MeV (TM4), OntoTools]
A survey of free microarray data analysis tools: http://chagall.med.cornell.edu/I2MT/MA-tools.pdf
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Programas de análisis libres
Programa
R/Bioconductor Potente, flexible, actualizado,
Unix/Windows/Mac
Consola, difícil de dominar
BRB tools Basado en Excel,
User-friendly
Si falla, falla.
Difícil de extender
dChip Expresión & SNP’s
User-frinedly
Solo Windows
Pocas opciones
Babelomics Web-based,
Multiples opciones,
Buen material
Web-based
Manejo algo rígido
…
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Babelomics: Viaje al conocimiento
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Programas de análisis comercialesPrograma
geneSpring Muy extendido
Gráficos potentes
Extensible (R)
ANOVA limitados
CARO
Partek ANOVA muy potente
Mult. tipos de datos
Visualización 3D
Sólo estadística “clásica”
No extensible. Caro
Ingenuity BD de anotacionesAnálisis de redes y de significación biológica
Centrada mayormente en datos de cáncer.
Caro.
…
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El proceso de análisis
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Análisis de un experimento con microarrays
(1) Imágenes(Datos crudos)
(2) C. de calidad(bajo nivel)
(3) Preprocesado
(4) Exploración de la Matriz de Expresión
(5) Análisis
(6) Significación Biológica
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(0) Diseño experimental
• Variabilidad– Sistemática
• Calibrar/Normalizar
– Aleatoria• Diseño Experimental• Inferencia
• Decidir acerca de– Réplicas, – Lotes (“Batch effect”)– Pools …
Awful design :-( Balanced design :-)Sample Treatment Sex Batch Sample Treatment Sex
1 A Male 1 1 A Male2 A Male 1 2 A Female3 A Male 1 3 A Male4 A Male 1 4 A Female5 B Female 2 5 B Male6 B Female 2 6 B Female7 B Female 2 7 B Male8 B Female 2 8 B Female
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(1) Obtención de la imagen
•Entra: Microarrays•Salen:
– Imágenes (1/chip) – Ficheros de imagen
• Información para cada sonda individual
•Datos para el análisis de bajo nivel– Control de calidad– Preprocesado– Sumarización
…
…
1.cel, 1.chp 2.cel, 2.chp
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(2) Control de calidad de bajo nivel
• Entra: – Imágenes (.CEL, ...)
• Proceso– Diagnósticos y
Control de calidad– Análisis basado en
modelos (PLM)
• Salen:– Gráficos– Estadísticos de
control de calidad
…
1.cel, 1.chp 2.cel, 2.chp
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(3) Preprocesado
• Entra:– Fichero de Imágenes
(datos del escaner)
• Proceso– Eliminación de ruido
– Normalización
– Sumarización
– Filtrado
• Sale:– Matriz de expresión
…
1.cel, 1.chp 2.cel, 2.chp
C01-001.CEL C02-001.CEL C03-001.CEL1415670_at 8.954387 9.088924 8.8338631415671_at 10.700876 10.639307 10.6109531415672_at 10.377266 10.510106 10.4617011415673_at 7.320335 7.252635 7.1123131415674_a_at 8.381129 8.332256 8.3937181415675_at 8.120937 8.082713 8.0515141415676_a_at 10.322229 10.287371 10.2828121415677_at 9.038344 8.979641 8.905711
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(4) Exploración
• Entra– Matriz de expresión
• Proceso– PCA, Cluster, MDS– Representaciones en
2D/3D– Agrupaciones
• Sale– Detectado efectos
batch– Verificación calidad
C01-001.CEL C02-001.CEL C03-001.CEL1415670_at 8.954387 9.088924 8.8338631415671_at 10.700876 10.639307 10.6109531415672_at 10.377266 10.510106 10.4617011415673_at 7.320335 7.252635 7.1123131415674_a_at 8.381129 8.332256 8.3937181415675_at 8.120937 8.082713 8.0515141415676_a_at 10.322229 10.287371 10.2828121415677_at 9.038344 8.979641 8.905711
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(5) Análisis estadístico (i):Selección de genes diferencialmente expresados
•Entra:– Matriz expresión– Modelo de análisis
•Proceso– t-tests, ANOVA
• Ajustes de p-valores
• Sale– Listas de genes
• Fold change, p.values
– Gráficos– Perfiles de expresión
C01-001.CEL C02-001.CEL C03-001.CEL1415670_at 8.954387 9.088924 8.8338631415671_at 10.700876 10.639307 10.6109531415672_at 10.377266 10.510106 10.4617011415673_at 7.320335 7.252635 7.1123131415674_a_at 8.381129 8.332256 8.3937181415675_at 8.120937 8.082713 8.0515141415676_a_at 10.322229 10.287371 10.2828121415677_at 9.038344 8.979641 8.905711
ProbeSet gene ID logFC t P.Value adj.P.Val B1450826_a_at Saa3 1450826_a_at 4,911 63,544 6,21E-14 2,80E-10 22,2441457644_s_at Cxcl1 1457644_s_at 4,286 53,015 3,52E-13 7,69E-10 20,7911415904_at Lpl 1415904_at -4,132 -50,455 5,66E-13 7,69E-10 20,3731449450_at Ptges 1449450_at 5,164 49,483 6,82E-13 7,69E-10 20,2071419209_at Cxcl1 1419209_at 5,037 47,175 1,08E-12 9,71E-10 19,7941416576_at Socs3 1416576_at 3,372 42,107 3,19E-12 2,08E-09 18,7841450330_at Il10 1450330_at 4,519 42,056 3,23E-12 2,08E-09 18,7731455899_x_at Socs3 1455899_x_at 3,648 40,821 4,29E-12 2,12E-09 18,5021419681_a_at Prok2 1419681_a_at 3,709 40,645 4,48E-12 2,12E-09 18,4631436555_at Slc7a2 1436555_at 3,724 40,081 5,12E-12 2,12E-09 18,335
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(5) Análisis estadístico (ii):Construcción & validación de un predictor
• Entra:– Matriz expresión
• Proceso– Selección variables– Ajuste modelo– Validación
• Sale– Modelos predictivos– Medidas de fiabilidad
/reproducibilidad
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(6) Significación biologica
• Entra– Listas de genes
• Proceso– GEA, GSEA, …
• Sale:– Clases GO /
Grupos de GenesPathwaysespecialmente representados
ProbeSet gene ID logFC1450826_a_at Saa3 1450826_a_at 4,9111457644_s_at Cxcl1 1457644_s_at 4,2861415904_at Lpl 1415904_at -4,1321449450_at Ptges 1449450_at 5,1641419209_at Cxcl1 1419209_at 5,0371416576_at Socs3 1416576_at 3,3721450330_at Il10 1450330_at 4,5191455899_x_at Socs3 1455899_x_at 3,6481419681_a_at Prok2 1419681_a_at 3,7091436555_at Slc7a2 1436555_at 3,724
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Ejemplo de análisis de datos
Comparación de perfiles de expresión entre tumores BRCA1/BRCA2 y
Construcción de un predictor que permita distinguir entre ambos.
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Fuente del ejemplo
Gene Expression Profiles in Hereditary Breast Cancer
•Hedenfalk, I, et. al., NEJM, Vol. 344, No. 8, pp 539-548.
Objetivo: Encontrar un predictor basado en perfiles de expresión para diferenciar tumores asociados a BRCA1 y BRCA2
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Esquema del análisis
• Diseño experimental y datos para el análisis
• Preprocesado• Exploración • Selección de genes• Construcción de varios predictores y
selección del más apropiado
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Diseño experimental
• RNA extraido de– 7 pacientess. BRCA1– 8 pacients BRCA2– 7 con cancer “esporádico”
• 6512 sondas– 5361 genes
• 3226 retenidos para el análisis
• Diseño de referencia– Cada muestra comparada
contra linea celular no tumorgénica (MCF-104)
Patient
ArrayPID
BRCA1 v BRCA2 v Sporadic
s1321 20 Sporadic
s1996 1 BRCA1
s1822 5 BRCA1
s1714 3 BRCA1
s1224 7 BRCA1
s1252 2 BRCA1
s1510 4 BRCA1
s1900 10 BRCA2
s1787 9 BRCA2
s1721 8 BRCA2
s1486 22 BRCA2
s1572 16 Sporadic
s1324 17 Sporadic
s1649 15 Sporadic
s1320 18 Sporadic
s1542 19 Sporadic
s1281 21 Sporadic
s1905 6 BRCA1
s1816 13 BRCA2
s1616 14 BRCA2
s1063 11 BRCA2
s1936 12 BRCA2
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Datos: log ratios
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Preprocesado: Filtrado y Normalización
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Exploración (1)
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Exploración (2)
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Análisis (1). Selección de genes (class comparison)
• BRCA1 vs noBRCA1• Usamos un t-test y
un cutoff de 0.0001 – es decir declaramos
diferencialmenete expresados los genes cuyo p-valor sea inferior a 0.0001
• No hacemos ajustes– Mínimo FC– Multiple testing
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Resultados (1): Lista de genes
Order FDR Fold-change Unique id Description Clone1 1.66e-05 0.0198 2.24 HV34H7 ESTs 2478182 2.17e-05 0.0198 2.03 UG5G3 minichromosome maintenance deficient (S. cerevisiae) 7 460193 2.3e-05 0.0198 0.31 HV17G6 keratin 8 8977814 3.37e-05 0.0198 1.89 HV18E8 SELENOPHOSPHATE SYNTHETASE ; Human selenium donor protein 8407025 3.63e-05 0.0198 2.21 HV32C7 ESTs 3078436 4.32e-05 0.0198 1.57 UG1F1 very low density lipoprotein receptor 260827 4.5e-05 0.0198 1.67 HV24F5 chromobox homolog 3 (Drosophila HP1 gamma) 5668878 4.92e-05 0.0198 2.02 LO3F1 butyrate response factor 1 (EGF-response factor 1) 3666479 9.43e-05 0.0338 1.85 HV9E3 "tumor protein p53-binding protein, 2" 212198
Parametric p-value
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Análisis (2):Construcción de un predictor
• Construímos predictores por 6 métodos distintos.
• Genes candidatos por class-comparison.
• Elegimos el que presente menor tasa de error de predicción (estimada por leave one out)
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Resumiendo…
El análisis de microarrays puede visualizarse como un proceso.
Es importante conocer Los métodos apropiados para cada problrma, los parámetros, el significado, las limitaciones de
cada paso.
Una aplicación adecuada del proceso proporciona información relevante como... una lista de genes diferencialmente expresados
(biomarcadores). un modelo con capacidad de predecir (firma)
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Limitaciones del método
Críticas, consejos, consensos y “estado del arte”
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Limitaciones de los microarrays
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An array of problems?
• Poca reproducibilidad entre estudios– Poca coincidencia entre las listas de genes– No reproducción de las predicciones en
nuevos conjuntos de test
• Falta de estándares• Falta de consenso en los métodos• El paso a la clínica siempre por llegar
• Mediados de la década: ¿Promesa o realidad?
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Que no estamos tan mal...
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Algunos consensos (Allison 2006)
• Diseño– Biological replication is essential – There is strength in numbers: power & sample size – Pooling biological samples can be useful
• Seleccion de genes diferencialmente expresados– Using FC alone as a differential expression test is not valid – 'Shrinkage' is a good thing – FDR is a good alternative to conventional multiple-testing approaches
• Clasificación y Predicción– Unsupervised classification is overused – Unsupervised classification should be validated using resampling-
– Supervised-classification requires independent cross-validation
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No todos los estudios se hacen bien...
• Dupuy & Simon estudian 90 publicaciones. – Análisis detallado de los métodos usados en 42.
• Ecuentran algunos errores comunes– Objetivos pobremente definidos.– No hay control de la multiplicidad
104 genes 104 tests P(Falso+) muy alta– Ni se informa bien de la fiabilidad de un predictor.– No se utiliza un conjunto de test independiente.– Se abusa por doquier del análisis de clusters.
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Aunque es posible hacerlo bien si...
• Se procura... (do’s)– Definir bien objetivos.– Combinar el p-valor y
el FC al seleccionar genes.
– Usar la FDR para el control de multiplicidad.
– Validar un predictor con un conjunto de prueba independiente.
– Contar con un estadístico
• Se evita... (don’t)– Basar la selección tan
sólo en “Fold Change”– Usar p-valores de 0.05– Usar métodos de cluster
si lo que se deseara es clasificar muestras.
– Violar el principio básico de la validación (no debe usarse el cjto de prueba antes de la validación).
... Hasta 40 “do’s” y “don’ts” en la tabla 3 de Dupuy y Simon (JNCI 99 (2): 147-157).
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Resumiendo
• Los microarrays tienen algunas limitaciones –razonables e intrínsecas-
• Un adecuado uso de los métodos de análisis puede generar información útil, fiable y reproducible.
• Aún así el paso de la clínica al diagnóstico es más lento de lo que se esperaba.
¿Por qué?
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De la investigación básica a los diagnóstico basados en microarrays
¿Para cuando?
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La idea está clara...
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Pero hay muy pocos kits de diagnóstico...
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Algunas de las dificultades
• Se precisan estudios muy grandes para establecer la potencia de un (kit) diagnóstico y validarlo en una cohorte independiente y suficientemente amplio.
• Hacen falta estandarizaciones y sistemas de control de calidad validados según criterios de laboratorios clínicos.
• Los tests de perfiles de expresión han de cumplir las normas de la Agencia Médica Europea y/o la FDA.
• Para justificar su desarrollo hay que hacer estudios de coste efectividad que sugieran una clara mejora en el tratamiento al paciente y retorno de inversión y beneficios en el medio/largo plazo.
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Estado de los diagnósticos basados en microarrays
Lleno: , Vacío:
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Resumiendo
• Se espera que la creciente calidad y tamaño de los estudios genere nuevos perfiles de expresión transportables al diagnóstico.
• Aspectos como estandarización y automatización (robótica) para minimizar la intervención humana están cada vez mejor.
• Otros como la regulación por parte de las agencias y las políticas de reembolso a los inversores y los laboratorios deben de irse resolviendo.
• No es improbable un futuro en el que el “lab-on-a-chip” forme parte de las herramientas de los clínicos.